Abstract
Industry 4.0 is on the rise and shows promise in manufacturing. Currently a lot of technologies are required to progress in its development to make Industry 4.0 a reality.
A physical production line operation at Volvo Cars was analysed by creating a virtual copy in a CAD-software, and then running simulations.
The aim of this thesis was re-creating a lathe operation virtually to verify that no collisions occur during operation 30. Research was made to reduce the non-machining time by repositioning the tools in a turret which reduced the number of rotations needed by the turret to fully machine the crankshaft. Data from the research suggest four different tool configurations that save time, and had to be simulated before implementation. By simulating and analysing the virtual simulation it’s possible to verify that no collisions occur during operations, no matter the tool locations, meaning that the suggested turret configuration can be used safely.
Certification
This thesis has been submitted by Emil Hafsteinsson and Richard Hjertqvist to the University of Skövde as a requirement for the degree of Bachelor of Science in Mechanical Engineering. The undersigned certifies that all the material in this thesis that is not our own has been properly acknowledged using accepted referencing practices and, further, that the thesis includes no material for which we have previously received academic credit.
Emil Hafsteinsson Skövde 2021-01-25
School of Engineering Science
Richard Hjertqvist Skövde 2021-01-25
School of Engineering Science
Acknowledgements
We would like to acknowledge the people who have been involved with this thesis project.
First of all, we would like to thank our supervisor from the University of Skövde, Kent Salomonsson for aiding with the project, he was instrumental in the development of the project from the beginning. We would also like to thank Kaveh Amouzgar for helping us throughout the project with vital information. A special thanks to Niklas Helsing and Goran Ljustina from Volvo Cars who invited us to Volvo to experience OP30 in person and who also supported us with the necessary CAD files.
Table of contents
1. Introduction 6
1.1 Background 6
1.2 Problem Statement 8
1.3 Delimitations 8
1.4 Ethics, social- and environmental aspects 8
2. Method and concept 9
2.1 Software selection 9
2.2 Literature survey 9
2.3 Computer-Aided Design (CAD) 10
2.4 Modeling 10
2.5 Virtual movement 11
3. Implementation 11
4. Results 14
5. Discussion 16
5.1 Future Work 17
6. Conclusions 18
References 19
1. Introduction
The first industrial revolution brought with it the mechanization of the factories replacing agriculture as the backbone of the societal economy. The second industrial revolution, considered by many as the most important one, was caused by the advancements in new energy sources: electricity, gas and oil. The third industrial revolution brought about the rise of electronics, computers and telecommunication. The fourth and now current industrial revolution, also known as Industry 4.0, came at the dawn of the third millennium with the creation of the internet (NE.se, 2020). The internet made the world even more connected which has made it a lot easier for companies to contact new buyers and form new partnerships. Industry 4.0 focuses on innovation, and the creation of new exciting ideas and utilizes technologies such as: cloud computing, Internet of Things (IoT) and big data analytics. The focus of this thesis is to simulate biaxial computer numerical control (CNC) machines to verify that no collisions occur during operation such as those found in Figure 1.
Amouzgar et al., (2020a) optimisation research shows a significant reduction in operation time by re-ordering the indexed tools on the turret (see Figure 3) by decreasing the number of rotations needed to select the correct tool for the process step.
Figure 1. Collision of CNC lathe between chuck and tool (Department of Mechanical &
Aerospace Engineering) 1.1 Background
Simulating machining is vital for optimisation as it does not require calibrating and testing in a live environment, therefore, manufacturing can still operate whilst optimisation is being done virtually. “This allows operators to test and optimise the machine settings for the next product in line in the virtual world before the physical changeover, thereby driving down machine setup times and increasing quality” (Rüßmann et al., 2015). With Industry 4.0 it will be possible to connect an entire factory to gather and analyse data. This data will be used to predict failure, configure automatically and adapt to these changes. By being able to use this data it will make the production faster, more flexible and more efficient (Rüßmann et al., 2015). Utilizing this research into simulations and optimisations will reduce the cost and increase safety during manufacturing. Creating this simulation is a stepping stone to create a virtual factory with all aspects of a real physical factory.
Some technologies and systems are vital to be able to execute Industry 4.0, these aspects have to be developed to integrate and work with each other to make industry 4.0 possible.
These technologies and systems are IoT, cloud computing, big data, simulation, augmented reality, additive manufacturing, horizontal and vertical systems integration, autonomous
robots as well as cybersecurity (Alcácer & Cruz-Machado, 2019)(Figure 2.). IoT allowes physical objects to exchange data over the internet such as sensors, software and other technologies. Big data is the accumulation of data collected from IoT which is then processed by cloud computing to extrapolate relevant data and make decisions based on collected data.
Figure 2. The nine technologies required for Industry 4.0 (Rüßmann et al., 2015) Further research is needed for Industry 4.0, although it is still in its developing phase.
Currently there are no complete solutions using the software that can fulfill the needs of these tasks. Virtual factories are a part of Industry 4.0 and labeled as the fourth industrial revolution by the German government’s High-Tech Strategy (2014). “Industry 4.0 aim is to work with a higher level of automatisation achieving a higher level of operational productivity and efficiency, connecting the physical to the virtual world” (Jain et al., 2001).
If Industry 4.0 was active the research done by Amouzgar et al. (2020a) and this thesis would be done automatically by the machine to reduce the required rotations and verify no collision occurs during operations.
1.1.1 About the University of Skövde
The department of engineering at the University of Skövde is currently working on several aspects of Industry 4.0. This thesis is a part of a project that is working towards smart manufacturing and knowledge-driven optimisation. This research aim is to help strengthen the competitiveness of Swedish industry (University of Skövde, 2020).
1.2 Problem Statement
The problem is threefold, firstly, generate a virtual simulation of the tool path and tool rotations during the machining process of a crankshaft, verifying visually that the tool path is working as intended and not colliding with the workpiece during machining or tool-changes.
Secondly, Create a digital twin of Operation 30 (OP30) and generate a functioning tool path that can easily be changed and optimised. Due to the nature of optimisation and high speeds of the lathe, several variables, such as vibrations, tool life and changes in rotational speed need to be considered to be able to accurately recreate the fabrication process virtually.
Lastly, Create a proof of concept that may be further researched with additional variables from other departments.
1.2.1 Operation 30
OP30 is a fabrication process to machine a crankshaft. The crankshaft is machined in a bi-axial lathe at high speed and with two independent tools simultaneously. This stage in the fabrication is high tolerance due to the high speeds and precision required from a high performing crankshaft.
Operation 30 is Computerised Numerical Control (CNC) lathe machine operation to manufacture crankshafts for 4-cylinder engines. The CNC lathe is biaxial and consists of two turret magazines 45 index locations (Figure 3).
Figure 3.: (a) Turret magazine with 45 index positions used in OP30; (b) Schematic of OP30 turret (Amouzgar et al,. 2020b)
1.3 Delimitations
This thesis is used to verify the data generated by Amouzgar et al. (2020a) by proving that the tool location optimisation made is working as intended, and thus verifying that no collisions occur during operations. Amouzhar et al. (2020a) focused on the right side tool turret for optimisation, for this reason no work in regards to collision on the left side will be done.
1.4 Ethics, social- and environmental aspects
Industry 4.0 faces a lot of challenges before it can be established. Using the sustainable development goals (SDG) from the United Nations, it is possible to improve in a lot of areas regarding manufacturing. Optimisation towards manufacturing can: lower the energy requirements to produce a product, decrease the amount of material needed as well as improve handling of waste material. “91 percent of the global economy has been linearly oriented (production – use – disposal), while only 9 percent of the
resources used are kept in the cycle.” Federal Ministry of Education and Research (2019).
Dantas et al., (2020) researched links between Industry 4.0 and circular economy in regards to SDG with a systematic literature review. The data shows that utilizing Industry 4.0 and circular economy it is possible to improve the current situation towards progressing the SDGs in the following areas:
● SDG 7 - Ensure access to affordable, reliable, sustainable and modern energy for all.
● SDG 8 - Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all.
● SDG 9 - Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation.
● SDG 11 - Make cities and human settlements inclusive, safe, resilient and sustainable.
● SDG 12 - Ensure sustainable consumption and production patterns.
● SDG 13 - Take urgent action to combat climate change and its impacts.
According to an analysis that used Germany as an example; an expected increase in employment with 6% over the next ten years and as much as 10% for mechanical engineers.
As more companies embrace Industry 4.0 an increase in productivity of 90 billion to 150 billion euros is estimated. Depending on the industry the productivity varies, for example the industrial-component manufacturers are looking at a 20-30% increase in productivity and automotive companies can expect an increase of 10-20% (Rüßmann et al., 2015).
2. Method and concept
2.1 Software selection
Software for Industry 4.0 is currently being worked on both for research and the private sector, as of now there are no complete software solutions to utilize Industry 4.0 with one software. This is due to the demand for progress in several aspects that are needed within Industry 4.0 (Alcácer & Cruz-Machado, 2019). Since this simulation is planned to be studied with different aspects in mind, it is important to pick the most versatile software for this task.
Choosing simulation software for future research requires an in-depth analysis of several aspects to avoid problems with compatibility and computational restrictions. To decrease compatibility it is important that the selected software is able to be used in other systems without losing vital data.
2.2 Literature survey
The literature research involved identifying, finding and analysing conceptual literature such as research articles, journals, books, theses and other relevant literature. It is important to evaluate and critically review the literature to establish its credibility.
2.3 Computer-Aided Design (CAD)
Computer-aided design (CAD) can be defined as the use of a computer system to assist in the creation, modification, analysis or optimisation of a design (Narayan et al., 2013).
2.4 Modeling
The tool turret as well as the crankshaft were both imported and modified in PTC Creo 7.0 CAD software during the project. The early versions of the whole assembly can be seen in Figure 4 below. The first iteration, labeled as A, was a quick rendition of what OP30 was believed to look like and modeled simply for reference during future discussions. This first model was used to see if movement was possible in PTC Creo 7.0 or if another program had to be used.
The second version (B) was a bit more refined, it included a free version of a Ford Figo crankshaft that was found online at GrabCAD. The model also featured other geometries that were necessary for the turret to move as it did in OP30.
Models (C) and (D) both featured a jaw chuck that was also found on GrabCAD. These versions were mostly an attempt to change the turret to look like what was described during an early stage and were only briefly used in a presentation during the first meeting at Volvo.
The final version was mostly imported from files received from that first meeting. The only objects still in the model were the additional geometries added to attach the different motors in PTC Creo 7.0. What is notable in this figure is the size difference between the turret and the workpiece which was a substantial difference from previous versions (Figure 5).
Figure 4.: The progress of the simulation of the bi-axial lathe during the project (A-D) created in PTC Creo 7.01.0
Figure 5.: Final version of the simulation of the bi-axial lathe used for collision detection.
2.5 Virtual movement
The lathing data was retrieved from internal documents and a printout of the code from OP30 lathe itself. This data was then used as velocity for the motors, i.e. driving mechanisms, in the model to replicate the tool path and tool change locations. Due to G-codes used in the program, some unknown internal radius compensations from a subroutine in OP30 made minor manual compensations required to fully replicate the tool-path.
Due to how the motors work in PTC Creo 7.0, accurate velocity is required as well as a starting point for the tool turret. Since everything was based on velocity, if an error were to occur early, it would affect all future locations of the tool path. Accuracy was required from the start to get a proper tool path.
3. Implementation
Indexed tool locations based on (Amouzgar et al,. 2020a) will be verified before implementation at the manufacturer. Focus on collision detection to ensure there are no failures during operation.
3.1 Current situation
The research performed by Amouzgar et al. (2020a) uses available data such as: tool life, number of rotations to each tool and total rotations until all tools need to be changed due to wear. An advanced algorithm was used to achieve this. The focus of their report was
specifically the right hand turret tool wheel in OP30. This data needs to be verified as such that no collisions occur during operations. A total of 1304 crankshafts can be created until all 45-indexed tools need replacing. Four specific data points that need to be verified in regards to collision shown in figure 7.
Currently, OP30 takes approximately 45 seconds, nine of those are non-machining time where the turret changes tools. Table 1 shows the number of changes (NoC) and figure 7 shows the number of rotations based on NoC. By reducing the number of rotations the non machining time can be reduced by a significant amount thus reducing the total machine time per crankshaft. (Amouzgar et al,. 2020a) works show the possibility to reduce the non machining time from 9 seconds down to 2.8 seconds if 36 tools were to change location. If option B was to be implemented the total non machining time would be reduced by 70%.
As mentioned by (Amouzgar et al,. 2020a) as the current tool locations (option A) would be changed to option B would require a total of 36 NoC, this might not be a simple task thus researching options A,B,C and D is required to make an educated choice in regards to tool locations. This also requires in depth collision detection before implementation.
Figure 6.: Number of unit rotations required to execute OP30 throughout the lifespan of 45-index turret magazine under two extreme solutions (marked by a and b in Figure
3). (Amouzgar et al,. 2020b)
Since (Amouzgar et al,. 2020a) also considered tool wear as a variable in the algorithms, that means as more crankshafts are manufactured, tools will differ, thus needing another
collision verification. Figure 7 shows current tool locations (option a) compared to option b in regards to total rotations for 1304 crankshafts to be manufactured (Figure 7). All possible tool locations suggested by Amouzgar et al. (2020a) need to be verified for collision. To prove that no collisions occur at the suggested tool locations, a simulation verifying this has to be done.
Figure 7.: Number of changes through the different iterations presented by (Amouzgar et al,. 2020a)
Table1.: The specific data featured in Figure 7 (Amouzgar et al,. 2020a)
Solution NoC Total no. of
rotations
Rotations per part
Non-machining time per part (s)
a 0 58398 ≈ 45 ≈ 9
b 36 18421 ≈ 14 ≈ 2.8
c 6 38085 ≈ 29 ≈ 5.8
d 12 27909 ≈ 21 ≈ 4.2
3.2 Literature survey
Before the process of researching the paper was done, a specific research question and topic would need to be set for what was considered most important about the research subject. It was decided that a qualitative research method was the preferred method since it would develop the idea of a simulated crankshaft into a more specific research project to set the focus early. To implement this, (Holme et al., 1997) describes the qualitative research process where the researcher would first start with a research theory where information is gathered about the topic to formulate questions in order to enhance the understanding of the research subject.
Such questions were imagined about the simulation that was kept in mind during the visit at Volvo Cars where the OP30 was observed. (Nilsson et al., 2015) Also supports this statement saying that observations are a good way of identifying shortcomings and to discover notable areas to look out for that can become problematic during the project. During the meeting on site at Volvo, and because of these questions and observations, a greater understanding of the project and its potential obstacles was gained.
3.3 Computer-Aided Design (CAD)
The entire operation 30 was designed in a CAD program (Creo Parametric 7.0) and the entire cutting process was simulated in the same program. By utilizing the built in feature Collision Detection in PTC Creo 7.0 it is possible to verify no collisions occur during the entire OP30 process.
4. Results
The CAD model made to replicate the entire OP30 process was successful in collision detection. To verify that all tool locations available in options A-D did not collide during operation, a modified tool wheel was created with the maximum dimensions of all tools (Figure 8 ,9 & 10). Using this modified tool during the collision simulation of the operation OP30, it is possible to verify that no collisions occur no matter the placements of the inserts on the tool wheel. The simulations made, prove that the work performed by Amouzgar et al,.
(2020a) through tool wheel optimisation can be used without worrying about collision because the locations of the tool change does not interfere with the workpiece.
Figure 8.: Frontal picture of all tools (yellow) with the modified tool wheel in gray
Figure 9 & 10.: Modified tool wheel showing all existing tools fit within.
5. Discussion
The information gathered from the CAD model in this project clearly demonstrates that the machine time can be reduced from 45 seconds down to 38.8 seconds. Yet, there is still research to be made, especially on the left tool wheel since there will be considerable consequences when one side is six seconds faster than the other.
Data to create the tool path during the operation was created in 2016 and documents from 2020 show differences. This might cause the tool changing locations to differ from the current tool path that is in use, which in turn could make this research obsolete and would have to be repeated with the updated information. The data used was in CNC programming language and used variables for tool radius compensation left and right, this variable is unknown which caused minor changes in the tool path. Compensations had to be made manually based on calculations and documentation since these compensations were made elsewhere and were not included in the data provided.
One way of improving this would be if the machine provided absolute coordinates which would make the process of converting the data into the simulation a lot simpler.
Another potential problem that the simulation does not account for is forging mistakes and irregularities as well as machining errors or tolerances. This could be a potential problem due to different dimensions. However the quality control at the manufacturing facility should detect such large differences before machining starts.
Creating the virtual simulation in the CAD software, Creo Parametric 7.0 gave a lot of possibilities for future work in several areas such as vibration analysis, finite element analysis and more (Inman & Singh., 2014). Creating the tool path for the simulation was made with spreadsheets and easily implemented into the simulation, this allowed for quick changes to be made.
The calculation time for the tool path simulation is brief and made virtually no downtime between simulations. However when utilizing the collision detection built into Creo Parametric 7.0 the computational time increased exponentially based on the number of objects included in the collision analysis.
Since Creo Parametric 7.0 is a modeling program it would be beneficial to add more stages of the manufacturing process such as the attaching of the workpiece with chuck, as well as implementing the compressed air stage and the rest of the bi-axial machine. This would make it easier to do further work such as finite element- and vibration analyses. By adding more of the real world into the simulation it would become more accurate. If the entire machine was to be added into the simulation it would be possible to analyse collision between machine and tool or workpiece and tool, i.e. collision between all parts that can collide.
Vibrations occur during operations due to the tools interacting with the crankshaft and high rotational speed of the lathe (Inman & Singh., 2014). These vibrations are not implemented in this simulation due to time constraints. These vibrations affect all aspects of the operation and if not considered can lower the quality of the crankshaft.
According to the research done by Amouzgar et al,. (2020a), the tool wheel currently rotates 58398 times during its lifetime (Table 1) before the inserts need to be changed, during this time, the wheel rotations for each part differs 22 times (Figure 6), which means there are 22
different rotational patterns for the current settings of the wheel, these then decrease with the number of changes, but still not lower than 9 (with the maximum number of changes to the turret). What this means is that for an absolutely accurate collision detection simulation, the simulation needs to perform all these passes. It was decided that there was not enough time to process all that data, partly because the time the computer would require to do them would be substantial. Instead, the much simpler variant of engulfing the turret in a larger geometry to see if it would collide during transport or operations was used. If for some reason this larger geometry collided with the workpiece during any part of the path, then it would not be possible to say that the tool changes would be possible without further simulations.
PTC Creo Parametric 7.0 was perhaps not a great choice of software since the program struggled with using the coordinates to transport the tool wheel, where it would “teleport”
between the locations set by the x-z data. Because of this limitation, the data had to be converted into velocities which were individually calculated and depended on the previous position, which would lead to errors in the pathing that would need to be manually corrected before running the simulation. However, it was later ascertained that PTC offers an add-on to Creo that would be able to read and create the G-codes.
5.1 Future Work
Industry 4.0 is currently being researched in a lot of institutions and companies worldwide, a lot of research has been made but there is more research and development that is needed for a complete solution for Industry 4.0. This thesis is only the first step in a long process to simulate in real time as is needed for Industry 4.0 to work as intended.
The created simulation of the crankshaft is also going to be used to study any real-world aspects in manufacturing that may occur during the fabrication process and study them and find a solution virtually before implementing it in the physical factory.
Further work based on this thesis is expected such as lathe tool path optimisation and vibration analyses.
6. Conclusions
Implementing the new tool locations into production would be beneficial for manufacturing since this would reduce the non-machining time. However, more work needs to be done in order for this to happen since only the right side has been optimised.
Further work using this simulation can assist in future optimisations and collision detection.
This could be improved further if more aspects of the real world made it into the simulation.
In a real-world scenario, vibrations are a big part of the machining process, and this is reduced by using stabilizers during the operation. In the simulation, these can be implemented as well to get a more accurate representation of reality. It may be a good idea to implement this before any accurate calculations are made.
PTC Creo has additional addons that allow reading of G-codes, utilizing this will make the process of recreating a simulation quicker. Alternatively, different software that has this function may be a better choice.
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