Integrating Design Optimization in the Development Process using Simulation Driven Design

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Linköping University | Department of Management and Engineering Master’s Thesis 30 hp | Master of Science – Mechanical Engineering & Machine Design Spring 2019 | LIU-IEI-TEK-A--19/03414—SE

Integrating Design

Optimization in the

Development Process using

Simulation Driven Design

Authors: Daniel Haraldsson Marcus Svensson

Supervisors: Johan Salomon, Scania CV AB Johan Persson, Linköping University Examiner: Mehdi Tarkian, Linköping University

Linköping University SE-581 83 Linköping, Sweden 013-28 10 00, www.liu.se

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Abstract

This master thesis has been executed at Scania CV AB in S¨odert¨alje, Sweden. Scania is a manufacturer of heavy transport solutions, an industry which is chang-ing rapidly in order to meet stricter regulations, ensurchang-ing a sustainable future. Continuous product improvements and new technologies are required to increase performance and to meet markets requirements. By implementing design optimiza-tion in the design process it enables the potential of supporting design exploraoptimiza-tion, which is beneficial when products with high performance are developed.

The purpose was to show the potential of design optimization supported by simulation driven design as a tool in the development process. To examine an alter-native way of working for design engineers, elaborating more competitive products in terms of economical and performance aspects. Furthermore, to minimize time and iterations between divisions by developing better initial concept proposals. The alternative working method was developed iteratively in parallel with a case study. The case study was a suction strainer and were used for method improve-ments and validation, as well as decision basis for the included sub-steps.

The working method for implementing design optimization and simulation driven design ended up with a procedure consisted of three main phases, con-cept generation, detail design and verification. In the concon-cept generation phase topology optimization was used, which turned out to be a beneficial method to find optimized solutions with few inputs. The detail design phase consisted of a parameterized CAD model of the concept which then was shape optimized. The shape optimization enabled design exploration of the concept which generated valuable findings to the product development. Lastly the optimized design was verified with more thorough methods, in this case verification with FE-experts.

The working method was tested and verified on the case study component, this resulted in valuable knowledge for future designs for similar components. The optimized component resulted in a performance increase where the weight was decrease by 54% compared with a reference product.

Keywords: Design optimization, Topology optimization, Simulation driven

de-sign, Parametric CAD models, Frequency response analysis.

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Acknowledgments

This thesis is the final assignment for master’s studies at Link¨oping University. The work has been conducted at Scania CV AB during the spring 2019, covering 20 weeks of work equal to 30 credits. The thesis was challenging but has given us valuable knowledge both in an academic and an industry perspective.

We would like to thank Scania and everyone involved in our thesis for making this possible. Additional thanks to our supervisor, Johan Salomon, for the help-fulness and engagement to make our time inspiring and pleasant. We are thankful to perform our work at the NMBO department, the positive attitude and interest-ing discussions has been worthwhile. Another thanks to Mikael Tellner and Tina Louka for valuable insights within the subject of optimization and simulation.

We are also thankful for the feedback and support from our supervisor at Link¨oping University, Johan Persson. And finally, we would like to show our gratitude to our opponent, Mattias Andersson, for providing valuable feedback and dedication of time and effort throughout the thesis.

Link¨oping, June, 2019

Daniel Haraldsson Marcus Svensson

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Nomenclature

Abbreviation Meaning

CAD Computer-Aided Design

CATIA V5 Computer-Aided Three-Dimensional Interactive Application V5 DOE Design of Experiments

FE Finite Element

FEM Finite Element Method

GAS Generative Assembly Structure Analysis

HEEDS Hierarchical Evolutionary Engineering Design System KBE Knowledge Based Engineering

MDO Multidisciplinary Design Optimization NMBO Base engine lubrication system PD Product Development

SDD Simulation-Driven Design TO Topology Optimization

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Introduction

This master thesis is performed in collaboration with the engine department NMBO at Scania CV AB. Scania is a manufacturer of heavy transport solutions including trucks and buses. NMBO are responsible for designing and testing vari-ous components of the base engine, components with the main purpose to lubricate the engine.

In order to meet a sustainable future within an industry which is changing rapidly and becoming more regulated, continuous development of each component is required to minimize emissions. By reducing the total weight of the trucks and buses the fuel consumption is reduced and the load carrying capacity is increased [1]. Optimizing components to increase their performance and minimize weight is therefore desirable, reducing the environmental impact and providing more com-petitive products.

1.1 Background

The most critical factor in profit-driven enterprises is the ability to develop suc-cessful products with an economic success, where the development process is one of the fundamental parts [2]. Since the product development process requires man-hours, the total development cost of products can be reduced by changing companies working methods. Scania’s development process starts with an initial CAD model from a design engineer followed by analysis from FE-experts. Based on the simulation results the design engineer makes changes and send the CAD model to the simulation engineers iteratively until the product meets the require-ments. Then the component needs to be physically tested and approved from an assembly and purchasing point of view until the final design gets approved [3]. This development process requires many changes to the CAD model and therefore time due to demands from the different disciplines. Since the design process is time consuming there is short amount of time for increasing the performance of the components even further.

The complexity of products are increasing successively and by implementing design optimization at an early stage in the design process, the support for the

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decision making increases. The design engineer can deliver a better initial pro-posal for the specific product, this induces fever iterations between the different disciplines. The procedure of design optimization can to a high extent explore re-lationships between the various properties of a product. By using an optimization algorithm together with simulation driven design, the exploration of the design space increases and simultaneously a product with higher performance can be found and created [4].

1.2 Purpose and Goals

The purpose of this master thesis is to encourage the usage of design optimization as a tool in the development process. To examine an effective alternative way of working to minimize iterations between design engineers and FE-experts. Another purpose is to implement optimization and simulation driven design in Scania’s product development process and show the potential of how to elaborate more competitive products, in terms of economical and performance aspects.

The goal is to develop a working process which can be implemented at Scania in order to work more efficiently and optimize various similar products. This working process will be validated and applied on a case study of an already existing product which is a suction strainer.

1.3 Research questions

The research questions to answer in this thesis work are:

• RQ1: How can design optimization involving CAD and FEM aid the design process?

• RQ2: How can topology optimization support concept generation early in the design process?

• RQ3: What differences and similarities does the developed working process have compared to a traditional development process at a truck manufacturer?

1.4 Deliverables

During the thesis the following deliverables will be performed gradually: • Investigate the requirements on the case study.

• Develop a working method for how design engineers can work with design optimization.

• Apply the method and develop an optimized suction strainer. • Evaluate the developed design suggestion of the suction strainer.

• Evaluate the working method where design optimization has been imple-mented.

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1.5 Delimitations 3

1.5 Delimitations

The thesis work is performed from a design engineers’ point of view. Since the main focus is on how design optimization can be used to provide a more competitive final product. The provided working method will focus on how the design engineering should work to minimize the required iterations between different disciplines.

The developed working method will be applied on only one case study, but the method should be applicable on similar products. The development of the case study component will stop at FE-verification, which means no prototypes or physical testing will be performed.

For this thesis Altair Inspire will be used for topology optimization, CATIA V5 will be used for CAD-modelling, CATIA Frequency Analysis will be used for calculations and Heeds will be used as the optimization software. These software’s will be investigated mainly because Scania is using them today which makes it easier to implement the findings in their development process.

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Chapter 2

Theoretical framework

This chapter describes the theory and literature studies relevant for the thesis and the studied field. The chapter covers areas such as product development process, CAD-modelling, optimization, simulation driven design and relevant manufactur-ing methods.

2.1 Product development process

In order to develop competitive and profitable products on the market it is of great interest to work efficiently. This chapter describes the fundamental steps of product development and the design paradox.

2.1.1 Design Process

According to Ulrich and Eppinger the product development process is a sequence of activities consisting of the workflow in figure 2.1.

Figure 2.1: Product development process [5]. 5

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The product development process starts with the planning phase which defines the opportunities and evaluates the market objectives and potential technologies. When ending the planning phase, the mission statement of the product should be defined, containing the business goals, target market and limitations [5].

After the planning phase ends the concept development phase starts. The crucial task in this phase is to identify the needs and generate several concepts with desired performance. Followed by selecting one or a few concepts for further development and testing.

When one concept has been chosen the process continuous into the system-level design where the architecture of the product is determined. The product concept is decomposed into several subsystems and parts, were the crucial components are preliminary designed. The assembly scheme for production should also be defined in this phase.

The following phase is detail design, where each part is fully specified in terms of material, geometries and tolerances. The manufacturing preparation of the components should also be completed in this phase and production cost should be definite.

The next step is testing and refinement phase were the product performance is evaluated by simulations or physical testing making sure it will fulfil the require-ments before entering the final phase which is production ramp up.

In the production and ramp up phase the assembly staff are educated and if there are any production related issues left they should be solved before the full scale production [5].

2.1.2 Design Paradox

The design paradox describes the evolvement of the product development process. When a project starts the engineers have brief knowledge about the design prob-lems to solve. During the early phase of the product development process there are large possibilities to make changes since few decisions have been made. As the project progresses the knowledge of the subject increases. However, the possibility of design changes are very limited and costly due to already established decisions [6][7]. Generally, wrong decisions in the conceptual product development can in-crease the manufacturing cost by more than 60%. It is therefore important to map and solve potential problems as early as possible [8]. This dilemma is called the design paradox and is illustrated in figure 2.2.

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2.2 CAD-modelling 7

Figure 2.2: The design process paradox [7] .

2.2 CAD-modelling

In order to work more efficiently with product development there are several tools which can be used. This chapter describes how to work smarter with CAD-modelling, integrating information by knowledge based engineering (KBE) and how to make models more usable and flexible using parametrization.

2.2.1 Parametrization

One way of making the product development process more time efficient is to develop reusable CAD-models. This can be done by controlling the CAD-model with parameters which are non-geometric features. The basic idea is to be able to reuse the CAD model by modifying its geometry with minimal effort [9]. Making it possible to generate several versions in for example a product family [10].

There are various levels of parametrization which are divided into morpho-logical and topomorpho-logical transformations. Morphomorpho-logical transformations represent changes in shape while topological transformations involve positioning of objects and features within a CAD-model [11].

As figure 2.3 shows, the morphological transformation contains four levels. Fixed object is the lowest level which corresponds to a model without the ability to change shape. The parameterized object level represents models with possibility to change shape with the help of parameters, lacking relations between the various parameters. The equation-based relation level involves dependencies between pa-rameters. The top level of morphological transformation is script-based relations describing relations with programming [11].

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Figure 2.3: Morphological transformation levels [11].

Figure 2.4 illustrates the topological transformation levels, which concerns adding, removing and changing instances in a CAD-model. The levels in topo-logical transformation increases the complexity in how instances are handled from manually instantiated to fully automated instantiation [11].

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2.2 CAD-modelling 9

2.2.2 Knowledge based engineering

Engineering knowledge is a fundamental part when developing products were the traditional way of storing the knowledge have been various books, technical docu-ments and manufacturing drawings. Knowledge based engineering (KBE) has the purpose of embedding the knowledge into suitable software’s making the knowl-edge reusable and the development process faster. By integrating knowlknowl-edge in technology platforms the possibility for effective collaboration between different disciplines increase because of easier access [12] [13]. Within the area of CAD modelling example of knowledge of interest to integrate can be various rules and guidelines about the manufacturing method, geometry and other crucial informa-tion for the specific components [14]. KBE also act as a support for design op-timization since it connects design parameters and functional requirements with optimization formalizations [15].

Implementing knowledge based engineering in the design process can reduce the development time needed since the needed time for routine work decreases, see figure 2.5. Decreasing the amount of time needed for routine tasks enables more time for innovative work in the design process [14].

CAD software’s often include KBE modules since it is a crucial part in the design process where repetitive work can be minimized. One example of a CAD software offering modules for enabling the potential of KBE is CATIA [14].

Figure 2.5: Benefits with Implementing KBE in the design process [14].

2.2.3 CAD model robustness and flexibility

Design optimization and knowledge based engineering requires flexible and robust CAD-models. Flexible in the sense it has the possibility to adapt and change

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shapes and configurations, making it possible to have a product family covered by a CAD-model. The CAD-models’ flexibility therefore increases by the amount of product variations they can represent. Robustness can be explained as the ability for a CAD-model to be flexible without encountering instability errors. The higher the robustness, the fewer error occurs [11].

Developing advanced CAD-models which are flexible and robust for design optimization is an experimental process, since there is no specific recommendation on how to successfully develop CAD-models with these qualities. However, there are ways to measure the robustness, so improvements can be made until the model meets the required level for successfully carrying out optimization.

Robustness can be calculated using equation 2.1 where RSc represents the

robustness and the index Sc indicates robustness for the sub space. NFailures

corresponds to the number of trials resulting with errors and NUpdates are the

number of trials. Note, the number of trials needs to be sufficiently large in order to get a robustness with statistical relevance [11].

RSc= 1 −

NF ailures

NU pdates (2.1)

2.3 Optimization

Optimization can be explained as finding the best solution among several feasible ones. The feasible solutions are all solutions not violating any constraints. Ob-jective function is the function describing the desired property which can either be minimized or maximized. Where the objective function can represent vari-ous properties such as weight, efficiency and manufacturing cost. The constraints are properties expressed with functions which have limits not to be exceeded, for example maximum allowed stress or minimum flow performance [16].

One way of describing the mathematical formulation of an optimization prob-lem can be expressed as below. Where f(x) is one or several objective functions, g(x) and h(x) are constraint functions and the vector x represent the design vari-ables [16]. Objective function(s): f(x)k k=1,2,3,..., K Subjected to: g(x)i ≤0 i=1,2,3,..., m <n h(x)j=0 j=1,2,3,..., r <n where x=      x1 x2 ... xn     

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2.3 Optimization 11

Within optimization experiments are changes of inputs by a given rule and iden-tifying the correlating output. Design of experience (DOE) is the umbrella term for techniques to efficiently guide selection of experiments [17]. Latin Hypercube sampling is one DOE technique which is inspired by a mathematical combination namely the Latin Square, where an N x N matrix is filled with N objects so they cover each row and column in the matrix, see figure 2.6. This technique can be adapted to cover and explore the design space within optimization [18].

Figure 2.6: LHS example in 2D with N=4 [18].

2.3.1 Multidisciplinary design optimization

When developing complex engineering systems with several interest from two or more disciplines, multidisciplinary design optimization (MDO) is a useful tool to use early in the development process when there is still a high level of design free-dom. The MDO can map the relations and dependencies between each subsystem, enabling a greater understanding of the system characteristics [19]. The gained knowledge serves as a well-founded base to make decisions from in the design pro-cess. MDO facilitates more design exploration which increases the possibility to find optimal solutions [20].

Using concurrent design where interest from all disciplines are combined, com-promised global optimums for the system can be reached. Instead of finding opti-mums for each subsystem which most likely will not collaborate due to conflicting relations [20]. Since MDO handles conflicting objectives from various disciplines, no single optimum exists but instead several Pareto optimal solutions. The Pareto front illustrates the trade-off between the objectives where each design solution on the Pareto front is a non-dominated design and is therefore an optimal solution [21]. Figure 2.7 illustrates an example of a Pareto front where the non-dominated solutions are Pareto optimal and the other solutions are feasible dominated solu-tions [16].

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Figure 2.7: Illustration of a Pareto front [16].

2.3.2 Topology optimization

Topology optimization (TO) is a general form in structural optimization, the method places materials anywhere inside a boundary to achieve optimal perfor-mance solutions with a given set of conditions. In a three-dimensional case the basic idea is to let the finite elements in the design boundary take values 0 or 1, when the value is set to 0 no material is placed and vice versa. The number of elements are minimized to achieve the optimal performance with its given design constraints [22]. An example where TO has been implemented can be seen in figure 2.8.

There are areas where topology optimization is beneficial, for instance when a new product is developed it is often designed from different concepts with a potential of satisfying the requirements. These concepts are often developed based from existing components or from experience. When a product in a new field is developed, TO can be implemented early in the design process to evaluate different possible solutions. The benefit with the method is the usage of few number of input variables needed for the algorithm to find the first solution. The input data can for instance be the boundary conditions and the given sets of loads [23].

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2.4 Simulation driven design 13

Figure 2.8: Illustration of a topology optimized beam [23].

2.4 Simulation driven design

The usage of simulation driven design (SDD) can broaden and explore scenarios to evaluate different design opportunities. The design performance can increase as well as enable verification of physical properties earlier in the design process [24]. Simulation-driven design is defined by Sellgren [25] as, ”a design process

where decisions related to the behaviour and the performance of the design in all major phases of the process are significantly supported by computer based product modeling and simulation”.

The use of simulation can be implemented in several disciplines, such as fluid-and solid mechanics. The main advantage is enabling developers to test the func-tionalities and receive feedback iteratively before significant development commit-ments are made [26]. Investigated systems can often be very complex because of interaction between several physical domains, the numerical models used in simu-lations can be used to decrease the complexity and give a solution to the system. However, physical testing of the system is often beneficial to test the hypothesis and verify the results [25].

2.4.1 Finite element method

The fundamental reason to use the finite element method (FEM) is to find solutions to a complex problem, the solution approximates the exact solution but is often adequate to most existing problems. Because the existing mathematical tools are not sufficient to find exact solutions. By using more computational efforts into the problem, the approximated solution can be improved and refined in a cost- and time-effective way [27].

The finite element method is a key feature in most development processes, and is often implemented in stress analysis, thermal analysis, fluid flow analysis, etc. The analyst determines for instance displacement in stress analysis or the heat flux in a thermal analysis.

The general working principle for the method is to divide the problem domain into small interconnected subregions called finite elements, see figure 2.9 [28]. This makes the theory applicable to a wide range of different boundary value problems, a boundary value is described as the existing solution in the domain of a body

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to-gether with a set of constraints called boundary conditions. There exist three main categories of boundary value problems, equilibrium and steady-state, eigenvalue problems and transient problems. An equilibrium problem is often solid mechanic problems where the displacement or stress distribution is defined. In an eigenvalue problem the natural frequencies are calculated. The type of transient problems are time-dependent and are used for instance when there is interest of finding the response of time-varying forces [27].

Figure 2.9: Meshed triangular elements in a rectangular domain [28]. According to Liu and Quek [28], the procedure of using the finite element method consist primarily of four steps:

1. Modelling of the geometry 2. Meshing of the geometry 3. Define material property

4. Specification of boundary and loading conditions.

In engineering design the geometry is created in a CAD-software. However, the ”real” CAD-model created by the designer is often very complex and need to be simplified in order to perform a good analysis. The model can then be meshed were the geometry is divided into small pieces. It is important to have the right coarse of the mesh, providing an accurate result while decreasing the simulation time. The type of material and which load conditions the geometry is subjected to must be defined before the analysis is performed [28].

2.4.2 Frequency response

Structures and components can under certain conditions start to vibrate either in a constant or exaggerated motion. Resonant vibration is characterized by modes and the phenomena is caused due to combination of the materials elastic and inertial properties. Problems regarding the topic is common in machinery environment during operation [29].

Vibrations are caused by a combination of resonant vibration and a forced vibration. The forced vibration can be caused by unbalances, external loads,

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2.5 Sand casting 15

ambient excitation or internally generated forces. A structures level of deflection, strain and stress, caused by static loading is typically amplified significantly by vibration response when subjected to resonant vibration.

Modes are implicit properties of a structure and they are determined by the structural stiffness, the mass and damping properties. Each individual mode is defined by mode shape, mode damping and the natural frequency. Structures modes will therefore change if the boundary conditions or the material properties are changed.

Frequency response function measure dynamic properties in mechanical com-ponents. The measurement can be defined as resulting velocity, acceleration or displacement response per given excitation force as an input. The response curve of a structure is represented by a summation of the response curves for each mode. A mechanical structure is sensitive at certain natural frequencies (modal fre-quencies) since the modes will act as amplifiers, meaning a small input force causes a very large response. The critical regions on a frequency response curve are the section where high amplitude is combined with high frequency [29].

2.5 Sand casting

One of the most popular manufacturing method is sand casting. This is due to the diversity of different materials, for its cost effectiveness and because of its great geometric freedom capability [30]. The casted parts can vary in size and weight from a dozen of grams to several tons. Sand casting is characterized as the use of sand as mold material with a suitable bonding agent, which the desired shape of the component can be created in. Common materials which are widely used in sand casting are cast iron, magnesium and aluminium. The method is often used when prototypes are made because of its inexpensive molds compared with other sufficient casting processes. However, the method is only favourable for low volume production [31].

The manufacturing method can receive sufficient tolerances and surface finish for a large field of applications. For an aluminium component with a size around 1500 mm an expected tolerance will be around ±0.75 mm with a surface finish of 50-150 µm [32].

When designing sand casted components there are several factors to consider. There is no manual to follow strictly because of different geometries creates dif-ferent behaviours, therefore guidelines or rules has been developed to assist in the development process [33].

Some design considerations to consider when developing sand casted compo-nents are [30][33]:

• Use simple flowing lines with minimum projection in opposite directions. • Build in strength of design instead of adding material, such as ribs to stiffen

and strengthen castings.

• Minimize the need of usage of cores, number of cores increase complexity and expense.

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• Avoid sudden changes in section thickness, this occur unintended in junctions and will create hot spots, see figure 2.10.

• Avoid sharp corners and use fillets in a high extent.

• Extensive horizontal flat surfaces should be avoided, because of warpage. • Long transport sections for the melt must have a suitable wall thickness. • Seek after a straight parting line for the component.

• Design it to be easy to pour and have into account where the ricer will be located.

Figure 2.10: Representation of the effect of junction and creation of hot spot [31]. However, there exist dimensioning rules to follow when designing components for sand casting. For all vertical surfaces a positive draft angle should be used, otherwise the detachment of the molded component can be difficult. The recom-mended draft angle should be somewhere between 0.5°to 2°. To stiffen or strength-ening castings ribs should be used, the size of rib is dimensioned depending on the wall thickness. The rib thickness should be somewhere between 1 to 1.5 times the wall thickness. The wall thickness cannot be smaller than 3 mm for sand casted components. In the design sharp edges shall be avoided, when an L-junction is created the preferable inner radius can be set to the wall thickness and the outer to two times the wall thickness [30]. A summarize of the dimensioning rules can be seen in figure 2.11.

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2.5 Sand casting 17

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Chapter 3

Thesis methodology

This chapter describes the methodology and working procedure adapted in this thesis. The work was divided into two main parts, first a pre-study was performed and based on those findings the main work and result generation were conducted. The general workflow outline for the thesis is presented in figure 3.1.

The first phase, pre-study, included a literature study in the investigated field, as well as a current situation analysis of Scanias working method to get knowledge and find improvement potentials. In the thesis a developed method was tested and improved with a case study component. The requirements on the case study was in this stage defined and documented.

The second part of the work was the primary result generation, this process was accomplished in an iterative manner, where the working method was developed in parallel with the case study validation. Finally, the thesis result was presented with a final developed working method as well as a finalized case study.

Figure 3.1: Thesis methodology flow.

3.1 Pre-study

To provide a knowledge base for this thesis, studies of relevant literature in the field and former thesis work was realized. A study of the current working method

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and case study requirements was also conducted.

3.1.1 Literature study

To provide a knowledge foundation about the areas covered in the thesis a litera-ture study was performed. The gained knowledge served as a support for making adequate decisions throughout the thesis, as well as answering the research ques-tions. Previous work and research within the relevant areas were studied. The information was gathered mainly from technical reports, books and scientific arti-cles. However, some information about how Scanias current working process came from their internal documents such as standards and guidelines.

Previous thesis work within similar field were studied with the main purposes of narrowing down the scope of the thesis, in addition to absorbing their conclusions and findings. The study of previous thesis work was not used as a base for the theoretical chapter of the thesis. It was instead used as an inspiration how to plan the project and giving ideas of suitable research questions for the field etc.

3.1.2 Study of current working method and case study

Scanias product development process was investigated by reviewing their com-pany standards within the subject and by confirming the gained information with employed design engineers. The investigation of how design engineers work at Scania with product development served the purpose to be used as comparison with the developed working method. The information about how Scania works with product development can be seen in detail in chapter 4.1.

To provide knowledge about the suction strainer a thorough investigation about the case study component was done. The investigation covered the basic function, sub features and constraints due to the operating environment of the component. The investigation also covered the interfaces not allowed to change, manufactur-ing method and associated geometry limitations etc. The information about the suction strainer can be seen in chapter 4.2.

3.2 Development of working procedure

The main purpose with this thesis was to provide an alternative way of working with product development as a design engineer using design optimization. Hence a working procedure was developed and presented for Scania. The thesis method-ology used for developing the working procedure is illustrated in figure 3.1. The working procedure was carried out in an iterative approach, where an alternative method was realized and tested on a case study to gather valuable information. The method was then improved until it delivered as anticipated. The final method is presented and described in chapter 5.1.

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3.2 Development of working procedure 21

3.2.1 Method development

A working method was provided to aid a more efficient way of working with prod-uct development from the perspective of a design engineer working with design optimization. The method described the fundamental parts to think about in or-der to successfully carry out optimization and simulation driven design. With a purpose to find better solutions earlier in the development process, with less re-sources required. The method serves as a material which a design engineer should be able to use for future work. Note, the method is not a thorough manual to be used as a step by step process.

3.2.2 Case study validation of working process

In parallel with the development of the working method, the suggested procedure was validated with a case study. This gave a standing point for reflecting over decisions made when applying the developed method on the case study. Hopefully, providing design engineers deeper understanding when using the method from this thesis in future development projects.

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Chapter 4

Current situation analysis

This chapter describes the current product development method at Scania and a pre-study of the investigated case study component. The information presented in this chapter has been collected from internal sources at Scania and published reports in the field.

4.1 Product Development method at Scania

This section describes the product development at Scania and how different disci-plines work in a cross-functional organization to deliver desired products.

4.1.1 General description of product development process

The product development process (PD) at Scania has been developed to be able to handle a global perspective and in high extent promote parallel work as much as possible. This is a cross-functional organization system, which enables high interactions between different departments and specializations to achieve common goals. The PD process can be seen in figure 4.1, and describes the different stages. The main activities are Concept development, Product development and Product follow-up, where the project progresses from a concept or idea to a fully devel-oped product implemented in production. Advanced engineering and research is often used when new areas of investigation is necessary. A team of researchers, often external sources, and experienced engineers develop a solution to the specific problem [3].

In the first stage, concept development, a group with high degree of cross-functionality investigates business possibilities and the different technical solutions. An iterative approach is used between disciplines to find the best working concept and to utilize as much knowledge as possible. Here a lot of preliminary CAD models and simulations results are compiled to find a suitable solution. Finally, the product requirements are set, and a detailed design needs to be developed.

Next in the PD process, product development, a finalized product needs to be prepared for the production. The previous work is considered and further

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Figure 4.1: Product development process at Scania.

development is performed. An iterative approach between CAD, simulation and physical testing is used to reach desired goals and to minimize uncertainties. The product specifications can be updated along the way, which increase number of iterations. When the developed product has passed the requirements and all tests, it is ready to be produced.

The product is then followed up to maintain and update if necessary. There are different assignment tasks in this phase, it can be a quality check, product change request, design adjustments or cost reduction. Documentation about important learning from the development is compiled, it is called lesson learning and is used to minimize appearance of similar problems and to transfer knowledge between employees. This phase improves Scania’s opportunities to deliver more competitive products to their customers [3].

4.1.2 Design engineer’s role in the process

The design engineer at Scania has a wide range of activities, depending on the stage of the project and type of product. They often work as coordinators between the different disciplines during the development process for the specific product, see figure 4.2. The design engineer is involved during the whole process from concept to final product, and has the main activities to specify requirements, creating CAD-models and update existing products. To be the coordinator require knowledge from the different disciplines more specific FEM and CFD departments. The work between the disciplines are an iterative process, which means when a CAD-model is created and analysed with CFD- and FEM-simulations. The results are gathered and necessary improvement is made to the product iteratively [3][34].

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4.2 The case study component- Suction strainer 25

Figure 4.2: Design engineers role the in product development process [34].

4.2 The case study component- Suction strainer

In order to have a well-functioning engine the lubrication is a key factor. This is mainly executed by the oil. The primary purpose of oil is to minimize friction and wear between components and to dissipate heat from fundamental parts. The motor oil cleans the engine and collects harmful particles, and it is therefore im-portant to have a high exchange of oil cleaned successively by the oil filter [35]. When the oil is not used, it is stored in the oil pan. When the engine is operating, it is picked up with a suction strainer and transferred into the engine. The suc-tion strainer is therefore a fundamental part to ensure good and consistent flow of oil. The performance of the component depends on several factors and need to be fulfilled to reach desirable results.

4.2.1 Description of the component

The main function of the suction strainer is to ensure a good oil pick up from the sump into the engine internals. The oil-pump creates a suction force and transport oil through the inlet and outlet, before feeding it into the engine, see figure 4.3. At the end of the inlet a filter is attached to prevent harmful pieces making the way into the engine. The suction strainer is mounted below the engine block with three bolts, the placement of these mounting points are far away from the oil inlet pipe. The inlet pipe placement needs to be in this position in order to minimize risk of oil starvation, when the vehicle is in critical places such as inclines. The connection between the mounting points and the suction pipe are designed to have a rigid structure withstanding the external conditions and to ensure a physical fitment inside the oil pan. The component also has run-off angles and holes at suitable locations to ensure no creation of oil pockets. Another function

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is measurement of the oil-level which is accomplished by inserting an oil-stick in the oil-stick container, see figure 4.3.

Figure 4.3: The reference suction strainer developed at Scania.

The suction strainer is low volume production item and is therefore manufac-tured using sand casting. The material used is aluminium and has the properties shown in table 4.1.

Table 4.1: Material properties for the suction strainer.

Properties Value

Material EN AC-43100 SF Density 2770 kg/m3

Young’s modulus 71 GPa Poisson ratio 0.33 Yield strength 150 MPa Fatigue limit 45 MPa

4.2.2 Requirements on the suction strainer

There are several restricting factors to consider when developing the suction strainer. The manufacturing method, sand casting, has geometric freedom capability but there are still rules to follow, see section 2.5. The geometric boundary for the suction strainer is limited by several surrounding components in the engine, such

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4.2 The case study component- Suction strainer 27

as oil pan, balancing shafts, engine block etc. The design was also limited by the fixed position of the mounting points, oil pick-up location and oil-stick container. Since the component is placed inside an engine the loading conditions it must withstand are vibrations during runtime. The vibrations induce fatigue on the component and frequency calculations had to be performed to find stress concen-trations and fatigue limits. The engine has a specific frequency spectrum which defines the acceleration excitation which is acting on the component. These values have been measured by physical testing for Scanias various engines. Generally, the engines does not exceed a frequency range of 300 Hz, see figure 4.4. An investi-gation of the eigenfrequency of the suction strainer must be performed. If the natural frequency is below 300 Hz the component can start to oscillate and get damaged. By using this limit the probability of oscillations for the component de-creases significantly. The component must also have a adequate stiffness to ensure a low deflection.

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Chapter 5

Results

This chapter presents the gathered results from the method development and the results from the newly developed case study component. Followed by performance comparison between the developed and the original component.

5.1 Developed method results

The developed working method for implementing design optimization and simu-lation driven design in the development process is illustrated in figure 5.1. The method is divided into four phases, each phase includes relevant tasks. The tasks could be conducted simultaneously or sequentially, before a new phase in the prod-uct development process starts.

Figure 5.1: Developed method with the different phases. 29

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5.1.1 Phase 1: Start-up

The initial phase when developing a component is to identify what type of project to be executed, for example a concept development or detail development project. Different types of projects give diverse prerequisites in terms of design opportu-nities. From a design optimization perspective, a detail development project will most likely encounter a more restricted design space compared to a concept de-velopment project. Since decisions about neighbouring components for a detail development project affects the design freedom greatly. While the design freedom in the start of a concept development project is in general greater when a project starts from scratch.

The start-up phase contains identifying all the disciplines of interest for the project. The goal of the phase is to define the problem, identify the goals, the interests from each discipline and potential conflicting interest. When the phase is finished the departments should have a common idea of the expected outcomes and what each department is expected to deliver. The project plan should also be created during phase one, which should make all stakeholders familiar with the time scope and the amount of resources for the project.

5.1.2 Phase 2: Design requirements and concept generation

In this phase all the requirements on the product should be defined. This could for instance be type of manufacturing method and its limitations. Other examples of requirements can be constraints in terms of minimum allowed eigenfrequency, flow performance or maximum allowed stress for the specific product. Setting up all the design requirements and load cases in the correct way are crucial. If the set-up differs from the real environment the risk of component failure or inadequate performance increase.

Next step in the phase is to define and create a model of the design space and the fixed geometries as well as product functions. Which serves the purpose for topology optimization, giving the design engineer an idea of where the material is needed and how the initial concept could look like. The fixed geometries are the functionalities not allowed to change when performing the topology optimization. By setting up load cases and supports the component can be optimized with the objectives of maximizing the stiffness or minimizing the mass. The result of the topology optimization serves as a conceptual proposal of the structure, which fulfills the desired requirements. This concept serves as a decision basis for further development.

5.1.3 Phase 3: Detail design and design exploration

From the previous phase the design engineer should obtain a concept from the topology optimization, regarding how the product could look like along with all the requirements. The geometry should then be modelled in a CAD software in a flexible and robust way. The CAD model should be parameterized in order to be enable shape optimization, meaning various dimensions of the geometry are able to vary using parameters. The manufacturability of the concept should be verified

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5.2 Case study results 31

in some manner, making sure the simulations are not performed in vain with a concept not manufacturable.

The CAD model should then be analysed with relevant simulation models, in either the used CAD software or in another software for simulations. By setting up the simulation framework with relevant load cases and supports the optimization software can then explore the design space by varying the parameters in the sim-ulation model. Resulting in various design suggestions to be compared in terms of the desired performance. One of the design suggestions will be chosen and analysed further for verification.

5.1.4 Phase 4: Design verification and final decision

In the final phase the concept from the shape optimization needs to be verified with calculation experts. The experts should perform more computational expen-sive analysis, making sure the selected concept fulfils requirements of for example eigenfrequency, fatigue and flow etc. The main goal with the developed method is to provide a better initial proposal to verify with the calculation experts, not to eliminate the need of verification from these disciplines.

When the concept is confirmed by the calculation experts, the next step is to make physical testing and to verify the results with the responsible discipline. The physical testing will vary greatly depending on the type of product and some products might not need this step. When the concept has passed the verification with the disciplines mentioned above, the stakeholders should make a final decision regarding the products future.

5.2 Case study results

The developed method, described in chapter 5.1, was implemented on a case study component. The original component and its requirements has been described in detail in chapter 4.2. The case study results are described in the same order as the proposed method.

5.2.1 Case study-Phase 1

The goal with this case study was to develop a suction strainer design with lower weight compared to an already existing component, the newly developed com-ponent should withstand the given requirements. The question to be answered was:

• Can an alternatively suction strainer be developed with lower weight and still meet the requirements?

The disciplines involved for this project was primarily design engineers and culation engineers with competence in the field of vibrations- and frequency cal-culations. The reason why those disciplines were involved was because of the components operating environment. If this project was to develop a totally new

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concept the discipline of CFD should be involved as well, but because of perfor-mance satisfaction for the already existing fluid pipes in the design those was not involved.

In this phase it was decided that the design engineer should do most of the development work and the calculation engineer should in the end verify the pro-posed concept. The calculations performed had a level of difficulty which in some extent was executable for a design engineer to give a good result estimation.

5.2.2 Case study-Phase 2

In this phase of the project the goal was to compile a preliminary concept showing the functions and the possibilities. The outcome from this phase was used as the decision basis for detail development.

Define design boundary

The process started by investigating the already existing suction strainer and defin-ing where the biggest potential of improvement existed. Since the surrounddefin-ing components was not changeable, the placement for mounting holes, oil stick con-tainer and suction pipe was fixed. This gave the biggest potential of improvement to be the structure in between those parts.

The existing suction strainer was developed to not interfere with surrounding components, the acceptable design boundary for the structure was defined by drawing a geometrical boundary in CATIA where material can be added without interfering, see figure 5.2. The interfering components for this case was balancing shaft from the top and swash plate from the bottom, other small components such as screws had to be considered. The defined boundary as well as the fixed geometries, mounting points, oil-stick container, suction pipe, was then used as the input to the topology optimization.

Topology optimization in Altair Inspire

The topology optimization was performed in Altair Inspire, the software can with few inputs give a solution to the problem based on the geometry created in CATIA. When the CATIA model was created and saved it was loaded into Inspire and the optimization problem could be defined.

First, the created design boundary was defined as the acceptable design space to place material in as well as creating and specifying the type of material for the component. The material for this component was aluminium, EN AC-43100 SF, the specific material properties can be seen in table 4.1 in chapter 4.2.1. The load cases were then added to the model, mounting holes were set to fixed displacement, and loads was set in X, Y and Z-direction at the suction pipe. This placement of forces gave a good distribution of material and was a approximation to give the component high stiffness.

Inspire has a built-in function to take manufacturing methods into account, the component was going to be sand-casted which set requirements on the possibility for easy removal from the mold. By placing a shape control in the software at a

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Figure 5.2: Design boundary used as input to Inspire.

suitable partition plane for the component, the optimization algorithm took the mould removal into account.

The optimization problem was then defined with the input to maximize the stiffness as the objective. The problem was constrained by a minimum allowed eigenfrequency of 300 Hz, as well as a minimum thickness of 5 mm, see chapter 4.2.2.

The result from the optimization run can be seen in figure 5.3. The material placement, in orange, had a structure with several branches from the mounting points to the suction pipe. The oil-stick cup was connected to one of the mounting holes. The optimized solution was used as a baseline for the parametric CAD-modelling in CATIA, the model was inserted into CATIA for simplifying the re-creation of the optimized solution.

5.2.3 Case study-Phase 3

The suction strainer concept was developed and had to be prepared for shape optimization. The basic idea was to mimic the Inspire optimized shape and create a parametric CAD model.

Parametric CAD model

When the parametric CAD model was developed, the parameters were chosen so the model had a certain geometric freedom. The geometric freedom was required for enabling exploration of designs during shape optimization later in Heeds. The

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Figure 5.3: The optimized solution from Inspire.

parameters were enabled to vary the elliptic cross sections of the braces at various positions. The angles of where the braces were connected to the fixed geome-tries and the ratio of where brace 2 connected to brace 1 was also varied with a parameter. The result of the concept is illustrated in figure 5.4.

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Figure 5.4: The first CATIA developed concept of the suction strainer.

The optimization problem and architecture

Before the optimization algorithm could find different solutions to the parametric CAD-model, the optimization problem had to be defined and concretized. The ob-jective for the optimization was set to minimize the weight of the suction strainer, and had to be constrained by the eigenfrequency, fatigue stress and the geometric freedom.

The eigenfrequency had to be at least 300 Hz for the first eigenmode, the reason why this constraint excised was to avoid natural oscillations of the suction strainer since frequencies in the engine during operating typically does not exceed this value, see chapter 4.2.2. To fulfil the fatigue constraint the induced stress in the component was not allowed to exceed 6 MPa, which was based on the fatigue limit for the material and safety factor. The optimization problem included a geometry interference check, ensuring the suggested design did not interfere with surrounding components. A summarization of the optimization problem is illustrated below.

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The general optimization problem: Minimize:

Mass [kg] Subjected to:

Eigenfrequency > 300 Hz Fatigue limit, σf <6 MPa

Geometry interference check = OK

To solve the optimization problem the practical approach was described in an architecture of the information flow, see figure 5.5. The central part is the optimization algorithm and was handled by Heeds. The software was assigned to manage the design parameters range of acceptable minimum and maximum values. As well as the responses from the different conducted analysis from CATIA.

Figure 5.5: The optimization architecture used in the case study.

The first CATIA analysis was a geometry check and consisted of a Clash Anal-ysis, secondly a Frequency Case and three Harmonic Dynamic Response Cases was calculated. The Clash Analysis was performed to make sure the suggested designs from Heeds did not result in interference with the surrounding components in the engine. If the Clash analysis found an interference between the suction strainer and the nearby components, the design was considered to be failed and the Frequency Analysis and the Harmonic Dynamic Response Cases were not computed.

The Frequency Case calculated the eigenmodes for each design. In the Fre-quency Case the mounting holes were restrained with fixed displacement and rotation. The Frequency Case was then used as the reference for the restraint

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excitation in the Harmonic Dynamic Response cases. Three different Harmonic Dynamic Response Cases was used for excitation of the suction strainer in X-, Y-and Z-direction. For each direction a representation was inserted which included the accelerations in the respective directions at certain frequencies occurring in the central of gravity for the suction strainer. The excitation representations are visualized in figure 5.6. The resulted fatigue stress caused by excitation of the component was captured and analysed, the maximum stress had to be less than the fatigue constraint of 6 MPa in order to be considered feasible.

Figure 5.6: The frequency and excitation amplitude in X,Y and Z-direction.

Optimization results

The parametric CAD model was then optimized using Heeds as the solver and CA-TIA analysis for evaluating designs, one example of a feasible design from Heeds can be seen in figure 5.4. The concept did fulfil the constraints regarding mini-mum allowed eigenfrequency for the three modes, it also fulfilled the constraints regarding fatigue limit when exciting the component according to the excitation spectra in X-, Y- and Z-direction.

However, this concept did not fulfil the requirements regarding manufactura-bility for sand casting since the cross sections varied too much which generates hot spots. Nor was the concept cost efficient since it would have required several partition planes for the braces which results in a more expensive manufacturing process of the mould. From the Heeds optimization the conclusion was drawn that the cross sections of the braces were large in order to reach the minimum allowed eigenfrequency and not for the fatigue limit since no design violated the constraint of 6 MPa. Which implies the concept could be improved to a structure using less material while still fulfilling the eigenfrequency constraints. The deci-sion was made to redo phase 3 from the beginning, to find a concept fulfilling the requirements for the manufacturing method.

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Revision of the Parametric model

Based on the findings from evaluation of the first concept, a second concept was developed to improve the faults. The concept can be seen in figure 5.7 and the concept was developed by combining the topology optimization in Inspire with the guidelines for sand casting described in chapter 2.5. The new concept had even cross sections for enabling consistent flow and to avoid hot spots. It also used a less complex geometry simplifying the creation of the mould, since the partition plane can follow one surface. Rather than the previous concept which used braces at different levels in the space, which makes a more complex mould for ensuring draft angles at all braces in the partition plane. The new concept did also use the guideline of strengthening the component by using ribs in the design rather than adding material. The design avoided sharp corners and sudden changes in material thickness.

Figure 5.7: The second CATIA developed concept of the suction strainer The parameters used in the second concept can be seen in figure 5.8. For example the material thicknesses were controlled individually for the geometries to the left and right. The rib thicknesses placed underneath the left and right geometry was also changed individually. The parameter values for the design is summarized in table 5.1.

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Figure 5.8: Description of the parameters used in the concept

To make sure the CAD-model had parameters enabling a wide range of con-figurations with few errors, a robustness and flexibility analysis was conducted. The robustness for the model was calculated using equation 2.1. The analysis was performed on 100 different design configurations in the design space using the Latin Hypercube design of experiments technique. Of these 100 designs five failed which implies a robustness of 95%.

Optimization results for the second concept

The results for the selected design from the shape optimization in Heeds are sum-marized in table 5.1. The mass of the concept was 0.69 kg and the maximum stress was 5.6 MPa. The maximum stress occurred when excitating the component in Y-direction, the sensitive areas are visualized in figure 5.9. The most sensitive spots were the rib placed above the surface and the rib connecting the suction pipe and the closest mounting point.

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Table 5.1: The optimization results for the selected design. Response Value Mass 0.69 Kg Frequency Mode 1 316 Hz Frequency Mode 2 400 Hz Frequency Mode 3 527 Hz Von Mises Stress X-direction 4.6 MPa Von Mises Stress Y-direction 5.6 MPa Von Mises Stress Z-direction 2.3 MPa

Rib Height 15.36 mm

Material Thickness R 3.28 mm Material Thickness L 3 mm Rib Thickness Right 5.04 mm Rib Thickness MP12 3 mm Rib Thickness Above 3 mm Rib Thickness Left 4 mm

Hole Size L 100 mm

Hole Size R L 50 mm

Hole Size R U 50 mm

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A Pearson correlation matrix of the two most critical responses was created, which which was the first eigenmode and the mass. The figure 5.10 illustrates the parameters which the responses mentioned were most sensitive to. One important note from the correlation matrix was the first eigenmodes large sensitive to the mass of the suction strainer. Which is a reason why the shape optimization in Heeds reached a lower limit of acceptable mass for not violating the eigenfrequency constraint. The frequency constraint was most sensitive to be the rib height, since it affects the moment of inertia the most. The mass was most sensitive to the material thickness of the left geometry and its corresponding hole size.

Figure 5.10: Correlation matrix design parameters.

5.2.4 Case study-Phase 4

The last step in the development process was to verify the optimized concept. The calculations used in the optimization framework were simplified which required a complementing calculation method with greater precision. This method was more computational expensive and advanced, the calculation was therefore performed in Abaqus. Instead of using a frequency response as in the optimization framework a random response analysis was performed to calculate the stresses. The stresses were compared using root-mean-square von Mises stress (RMISES), the calculated RMISES should be below 12 MPa, which was based on the fatigue limit of the material. The results from the analysis can be seen in table 5.2, the results showed that the developed component fulfilled the requirements.

From the verification analysis improvements on the developed method was found. The improvements to be made on the component was to increase radius sizes in critical places subjected to stress concentrations and fatigue. This kind of improvements does not need to be verified again and were therefore implemented on the case study component.

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Table 5.2: Results from the verification in Abaqus.

Response Value

Frequency Mode 1 [Hz] 314 Frequency Mode 2 [Hz] 416 Frequency Mode 3 [Hz] 508 RMS Mises Stress X-Direction [MPa] 5.5 RMS Mises Stress Y-Direction [MPa] 9.7 RMS Mises Stress Z-Direction [MPa] 6.6

5.2.5 Case study improvements

The developed suction strainer was compared with the original component, this was conducted to verify the potential of implementing optimization in the develop-ment process. The comparison between the two components can be seen in figure 5.11, the developed concept showed that less material in the middle could be used and still fulfil the requirements.

Figure 5.11: Comparison between the developed and original suction strainer. When performing frequency- and harmonic dynamic response analysis with the same set up in CATIA, several conclusions were realized. The results for the comparison are summarized in table 5.3. Firstly, the weight reduction from the original suction strainer to the optimized concept was 53,4 %. The weight reduction gave a negative impact on the three eigenmodes as the table shows. The reason why this correlation exists can be explained by the theory in chapter 2.4.2, which describes that modes are implicit properties determined by the mass, the structural stiffness and the damping properties.

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Table 5.3: Comparison of the original suction strainer and the developed concept.

Response Developed Original

Mass [Kg] 0.69 1.48

Frequency Mode 1 [Hz] 316 347

Von Mises Stress X-direction [MPa] 4.6 4.01 Von Mises Stress Y-direction [MPa] 5.6 1.22 Von Mises Stress Z-direction [MPa] 2.3 2.61

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Integrating Design Optimization in the Development Process using Simulation Driven Design