Quality Prediction in Jet Printing Using Neural Networks

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DEGREE PROJECT IN MECHANICAL ENGINEERING, SECOND CYCLE, 30 CREDITS

STOCKHOLM, SWEDEN 2020

Quality Prediction in Jet Printing Using Neural Networks

Daniel Brun

Colin Lawless

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Authors

Daniel Brun Colin Lawless

KTH Royal Institute of Technology

Place for Project

Täby, Sweden Mycronic AB

Examiner

Hans Johansson Stockholm, Sweden

Supervisor at KTH

Carl During

Stockholm, Sweden

Supervisor at Mycronic

Gustaf Mårtensson Täby, Sweden Mycronic AB

Master’s Thesis Coordinator

Fredrik Asplund Stockholm, Sweden

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Master of Science Thesis TRITA-ITM-EX 2020:229

Quality Prediction in Jet Printing Using Neural Networks

Approved

2020-06-01

Examiner

Hans Johansson

Supervisor

Carl During

Commissioner

Mycronic

Contact person

Gustaf Mårtensson

Abstract

Surface mount technology is widely used in the manufacturing of commercial electronics, and the demands on the machines increase as the complexity of the electronics increases and the size of the components decreases. Mycronic is a company that focuses on addressing those demands with their high-technology jet printing and pick-and-place machines. This master's thesis has been performed at Mycronic and has focused on the MY700 jet printer. Due to unknown factors, the quality of the ejected solder paste droplets from the machine can vary over time. It was therefore of interest to monitor variables of the MY700 in order to gain more knowledge about the cause of the varying quality, and also to be able to detect substantial changes in deposit quality.

In this project, the temperature has been measured at three key locations on the ejector as well as the current going through the piezoelectric actuator. This data was fed to a neural network in order to make quality predictions with respect to the diameter of the solder paste deposits. Different combinations of sensor data were used to evaluate how the different sensors affected the performance of the neural network.

Thereby, a better understanding of how big an impact the different variables had on the quality of the deposits could be achieved.

The results indicate that the current was more significant than the temperature for making quality predictions. Using only the temperature data, the neural network was not able to accurately predict quality deviations, whereas with the piezo current data or both of them combined, better predictions could be made. The current data also significantly improved the performance of the neural network when printing jobs with varying diameters were used. The conclusion is that none of the three temperature sensors significantly improved the performance, and there were no considerable differences between them, while the current did improve it.

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Examensarbete TRITA-ITM-EX 2020:229

Kvalitetsestimering av jetdispenserad lodpasta med ett neuralt nätverk

Godkänt

2020-06-01

Examinator

Hans Johansson

Handledare

Carl During

Uppdragsgivare

Mycronic

Kontaktperson

Gustaf Mårtensson

Sammanfattning

Ytmonteringsteknologi är en väletablerad metod som används inom tillverkningen av kommersiell elektronik, och kravet på dessa maskiner ökar i takt med att elektronikens komplexitet ökar och storleken på komponenterna minskar. Mycronic är ett företag vars fokus ligger i att möta dessa krav med deras högteknologiska jet printing- och pick-and-place-maskiner. Detta examensarbete har utförts på Mycronic och har fokuserat på jet printing-maskinen MY700. På grund av okända faktorer kan kvaliteten på den deponerade lodpastan från maskinen variera över tid. Det var därför intressant att övervaka variabler hos maskinen för att få mer kunskap om orsaken till den varierande kvaliteten och också för att kunna upptäcka förändringar i kvaliteten.

I det här projektet har temperaturen mätts på tre kritiska positioner på ejektorn samt även strömmen som går genom det piezoelektriska ställdonet. Dessa data gavs till ett neuralt nätverk för att göra kvalitetsprognoser med avseende på diametern på deponeringarna av lodpasta. Olika kombinationer av sensordata användes för att utvärdera hur de olika sensorerna påverkade det neurala nätverkets prestanda.

Därigenom kunde en bättre förståelse av hur stor påverkan de olika variablerna hade på kvaliteten på deponeringarna uppnås.

Resultaten indikerar att strömmen var mer betydelsefull än temperaturen för att göra kvalitetsprognoser. Om bara temperaturdata användes lyckades inte det neurala nätverket göra exakta förutsägelser för kvalitetsavvikelser, medan med bara strömdata eller båda kombinerade kunde bättre förutsägelser göras. Strömdatan förbättrade också prestandan hos det neurala nätverket när jobb med olika diametrar användes.

Slutsatsen är att ingen av de tre temperatursensorerna förbättrade prestandan signifikant, och det fanns inga betydande skillnader mellan dem, medan strömmen förbättrade prestandan.

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Acknowledgements

Firstly, we want to thank Gustaf Mårtensson, Daniel Grafström and Juan Albahaca for realizing this project and giving us the opportunity to complete it. You have also given us your support and encouragement throughout the project, for which we are grateful.

We also want to express our deepest appreciation to all our coworkers for providing expert knowledge and for supporting us when needed.

A special thanks to our supervisor, Carl During, for his support and feedback throughout the project.

Tack!

Thank you!

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1 Introduction

1

1.1 Background . . . 1

1.2 Problem Formulation. . . 3

1.3 Research Question . . . 5

1.4 Requirements . . . 5

1.5 Delimitations . . . 6

1.6 Methodology . . . 6

1.7 Thesis Outline . . . 7

2 Frame-of-Reference

9 2.1 Jetting Technology . . . 9

2.1.1 Piezoelectric Actuator . . . 10

2.1.2 Jet Printing Quality . . . 11

2.2 Non-Newtonian Fluids . . . 12

2.2.1 Solder Paste . . . 13

2.3 Sensors . . . 14

2.3.1 Temperature . . . 14

2.3.2 Current . . . 15

2.4 Data Acquisition . . . 15

2.5 Neural Network Architecture . . . 17

2.5.1 Recurrent Neural Network . . . 20

3 Methodology

25 3.1 Research Strategy . . . 25

3.2 Internal and External Validity . . . 26

3.3 Procedure. . . 27

4 Implementation

29 4.1 Hardware Configuration . . . 29

4.1.1 Sensors . . . 31

4.1.2 Red Pitaya . . . 32

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CONTENTS

4.2 Software Configuration . . . 33

4.3 Experimental Procedure . . . 35

4.4 Verification and Validation . . . 37

4.4.1 Thermocouples . . . 37

4.4.2 Shunt resistor . . . 39

4.4.3 Prediction Model . . . 41

5 Results

43 5.1 BGA Results . . . 43

5.2 RT1 Results . . . 47

5.3 Fulfillment of Requirements . . . 48

6 Discussion

51 6.1 Test Cases . . . 51

6.1.1 BGA . . . 51

6.1.2 RT1 . . . 53

6.1.3 Neural Network Performance . . . 53

6.2 Sensors . . . 54

6.3 Requirements . . . 55

6.4 Research Method . . . 55

7 Conclusions

57

8 Future Work

59

References

61

Appendices

66

A PCB Schematic Overview

66

B Training and Validation Loss

67

C Performance Without Current

68

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List of Figures

1.1.1 Ejector of a jet printing machine. . . 2

1.1.2 An assembly line solution at Mycronic. . . 2

1.2.1 Temperature sensor placement. . . 4

1.2.2 Current and voltage waveform. . . 5

2.1.1 Simplified illustration of the ejector in a jet printing machine. . . 9

2.1.2 Three-phase voltage waveform of the piezo actuator for a single ejected solder paste droplet. . . 11

2.1.3 Two different qualities of a BGA jet printing job. . . 12

2.1.4 Quality measurement of a single droplet on a substrate. . . 12

2.2.1 Rheological properties of different non-Newtonian fluids. . . 13

2.2.2 Shear viscosity as a function of shear rate for two solder paste samples. 14 2.3.1 Simple thermocouple circuit. . . 15

2.4.1 Red Pitaya overview. . . 16

2.5.1 Typical architecture of a fully connected neural network with one hidden layer. . . 17

2.5.2 Structure of an artificial neuron in a neural network. . . 18

2.5.3 Circuit diagram of a cell in an RNN. . . 20

2.5.4 Illustration of the data flow through an LSTM cell. . . 21

2.5.5 LSTM models. . . 23

4.1.1 Overview of hardware setup. . . 30

4.1.2 Flow chart illustrating the data transfer. . . 31

4.1.3 The PCB used for data gathering. . . 33

4.4.1 Measured temperature before and after calibration. . . 37

4.4.2 Thermocouples response to temperature changes. . . 38

4.4.3 Measured temperature before and after filtering. . . 38

4.4.4 Positioning of the shunt resistor. . . 39

4.4.5 Measured current of a single solder paste shot in a BGA job. . . 39

4.4.6 Measured noise from the current sensing. . . 40

4.4.7 Calibrated measurement of current. . . 40

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LIST OF FIGURES

4.4.8 Verification and validation of the LSTM model. . . 41 5.1.1 Training and validation loss when training the LSTM model using a

BGA job. . . 44 5.1.2 Predicted and true diameter using a BGA job with current data as input. 45 5.1.3 Predicted and true diameter using a BGA job with three different

variable configurations as input. . . 45 5.1.4 Predicted and true diameter using a BGA job with all input variables

given to the model. . . 46 5.1.5 Predicted and true distribution using a BGA job. . . 46 5.2.1 Training and validation loss when training the LSTM model using an

RT1 job. . . 47 5.2.2 Predicted and true diameter using an RT1 job with all input variables

given to the model. . . 48 5.2.3 Predicted and true distribution using an RT1 job with all input

parameters to the LSTM model. . . 48 A.0.1 PCB Schematic Overview. . . 66 B.0.1 Training and validation loss for different input parameter

configurations using a BGA job. . . 67 C.0.1 Results from the predictions by the LSTM model when given only the

temperature as input when using a BGA job. . . 69 C.0.2 Results from the predictions by the LSTM model when given only the

temperature as input when using an RT1 job. . . 69

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List of Tables

4.2.1 Architecture of prediction model. . . 34

4.3.1 Variable configuration for the BGA and RT1 job . . . 35

4.3.2 Evaluation cases for the LSTM model, where a BGA job with different input configuration is fed to the model. . . 36

5.1.1 BGA test results for different sensor combinations. . . 44

5.2.1 RT1 test results for selected sensor combination. . . 47

C.0.1 Test results when only using temperature data. . . 68

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List of Abbreviations

A/D Analog-to-Digital

ASIC Application Specific Integrated Circuit BGA Ball Grid Array

BNC Bayonet Neill–Concelman D/A Digital-to-Analog

EMI Electromagnetic Interference FPGA Field-Programmable Gate Array GPIO General-Purpose Input/Output GPU Graphics Processing Unit I²C Inter-Integrated Circuit IC Integrated Circuit

IDC Insulation-Displacement Contact LSTM Long Short-Term Memory

MAE Mean Absolute Error MRE Mean Relative Error PCB Printed Circuit Board PnP Pick-and-Place

PRT Platinum Resistance Thermometer RNN Recurrent Neural Network

RT1 Robustness Test 1

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LIST OF TABLES

SGD Stochastic Gradient Descent SMD Surface Mount Device SMT Surface Mount Technology SPC Statistical Process Control SPI Serial Peripheral Interface

UART Universal Asynchronous Receiver/Transmitter

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Chapter 1 Introduction

This chapter introduces the project, its purpose and its framework. Moreover, it introduces the company where the master’s thesis project has been conducted, namely Mycronic, as well as provides a brief overview of their technology.

1.1 Background

Surface mount technology (SMT) is a technology used for producing printed circuit boards (PCBs), which started to become widely used in the 1980s. Instead of having components with leads that go through the PCB, so called through-hole components, surface mount devices (SMDs) are used. SMT is used in virtually all commercial production of circuit boards due to advantages in regards to size, cost, reliability and automatability.

There are different ways of applying solder paste for SMD components, one of which is jet printing. This method utilizes a piezoelectric actuator to operate a piston which ejects solder paste out of a nozzle. The solder paste dots are ejected at high frequencies and typically have a volume of 5-20 nl. An example of what a printing head, also called ejector, can look like can be seen in Figure 1.1.1.

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CHAPTER 1. INTRODUCTION

Figure 1.1.1: Ejector of a jet printing machine [1].

One company which uses this type of technology is the Swedish manufacturer Mycronic. Mycronic is a high-tech company that has been producing world-leading production equipment for display and electronic manufacturing since the early 80s [2].

In addition to Mycronic’s mask writer, they also offer complete assembly line solutions, which include the jet printing machine and the Pick-and-Place (PnP) machines [3].

This project was focused on their technology within SMT and, more specifically, the jet printing machine which is the second unit in the assembly line as seen in Figure 1.1.2.

Figure 1.1.2: An assembly line solution at Mycronic [4].

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1.2 Problem Formulation

A jet printing machine deposits solder paste or other assembly fluids with high accuracy and good repeatability, but due to unknown factors the quality of the ejected solder paste droplets can still vary between shots. Thus, it is of interest to monitor variables of the jet printing machine using a neural network in order to gain more knowledge about the cause of the varying quality.

The manufacturers of circuit boards have an increased demand on increasing the density and the complexity in today’s technology. The traditional technique of detecting defects in PCB production is through statistical process control (SPC), but it is used off-line which means that defects are detected after the completed process [5]. In [5], [6] and [7] it is mentioned that real-time detection of process drifts is preferred in order to make production more efficient and robust. In [5], it is stated that a neural network has advantages in both accuracy and robustness in the field of modelling semiconductor processes. It is further explained in [5] that a neural network can learn to map complex sequences and handle corrupted data.

In order to implement a neural network for predicting the quality of solder paste deposits, it first has to be decided what variables should be monitored. The choice of variable is dependent on the availability of the signal and its probability to predict a certain behaviour. One possibility is temperature. When the temperature of solder paste varies, so do its rheological properties [8], [9]. Different components and mechanisms in the ejector are affected depending on how much the temperature changes during a printing job. A previous study mentions that an increase in temperature will decrease the viscosity, which will affect the quality of the printing negatively [9]. Therefore, a possible hypothesis is that data gathered from temperature sensors in the ejector can be used as an input to a neural network that could be used to predict changes in jetting quality. The place where the temperature is measured may have an influence on the performance of the neural network, and therefore three sensor positions will be compared in this project. These three temperature sensor positions are based on sensor positions used in a previous master’s thesis at Mycronic [4].

A stepper motor controls the Archimedes screw which feeds the ejector with solder paste, as seen in Figure 1.1.1. The efficiency of the stepper motor is highly dependent on the solder paste properties, such as the viscosity of the fluid. Before each shot, the screw is turned a certain number of degrees and if the temperature changes, the volume being fed will also change to some extent. The temperature here is affected by heat generated in the ejector, friction in the paste screw, as well as heat generated by the stepper motor. Therefore, one temperature sensor was placed after the paste screw, which is shown as Sensor 1 in Figure 1.2.1. The solder paste being fed to the screw comes from a tube which has been stored in a refrigerator, and once mounted in

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the machine is slowly heated up to room temperature before jetting starts. Measuring the temperature of the paste being fed was therefore also of interest, since this could also affect the pump. This position is named Sensor 2 in Figure 1.2.1.

As for the piezo-controlled piston, the temperature can have multiple effects. The rheological properties are a function of temperature. Changes of those properties will affect how easy or hard it is for the piston to push the paste through the ejector nozzle, but also the number of undesirable air pockets that are created in the chamber.

These air pockets change how much solder paste is ejected and at what speed, thereby decreasing the quality. A temperature sensor was therefore placed in the chamber next to the piston, presented as Sensor 3 in Figure 1.2.1. As changes in temperature have an impact on the jetted quality due to changes in viscosity [9], having a sensor in this location was also of interest. The properties of the paste here affect how the paste exits the nozzle, that is, if there is a tendency for satellites, if the positioning is good, etc. Satellites are small undesirable solder paste droplets that break off from the main deposits. This is illustrated in more detail in Section 2.1.2.

Figure 1.2.1: Temperature sensor placement [4].

The displacement of the piezoelectric actuator used in the ejector is controlled by a voltage reference which follows a predetermined curve. Collected data from the measured voltage curve, such as rise time and amplitude, could be used to train the neural network to predict the quality of the jetted solder paste. However, Mycronic considers the supply voltage to the actuator to be too noisy for qualitative measurements [4]. An alternative way of measuring the displacement of the piezoelectric actuator is to measure the current required to follow the voltage as seen in Figure 1.2.2, which was performed in [4]. It is Mycronic’s hypothesis that the measured current variations expresses individual droplet characteristics [4]. The data gathering from the ejector can also be performed in a non-invasive way.

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Figure 1.2.2: Theoretical graph of the current and voltage waveform sequence for an individual jetted solder paste droplet [4].

1.3 Research Question

The research question for this master’s thesis is as follows:

In a piezo-based material depositing device, what are the implications of the predetermined temperature sensor positions, when providing supporting data from a current sensor, in regard to increasing the accuracy of predicting jetted solder paste quality by training a neural network?

1.4 Requirements

The requirements for this project were decided together with the stakeholders and are listed below.

• A neural network shall be trained to predict changes in the quality of jetting deposits which later can be used for real-time prediction.

• Temperature and current shall be measured and the data shall be used as input to the neural network.

• Three different locations on the ejector shall be used for temperature measurements.

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• The quality of the solder paste shall be based on the diameter of the shots and these shall be measured using a MY700 jet printer.

• Acceptable results from the neural network require the predicted diameter to vary less than 8% from the actual diameter for individual predictions.

1.5 Delimitations

The delimitations of the project are listed below.

• The MY700 shall be used to gather data for the neural network.

• The focus of the project shall be to only evaluate correlations between two predetermined input parameters and one output parameter.

• The two inputs shall be temperature and current, while the output shall be the quality of jetting deposits with respect to diameter.

• The project shall only include two different types of jetting jobs on the MY700, which are a Ball Grid Array (BGA) job and a Robustness Test 1 (RT1) job.

• The study shall be performed on one machine and one ejector only.

• The neural network shall be trained off-line so that, at a later stage, it can be used for real-time prediction.

1.6 Methodology

The methodology used in this project was a case study. Two different jet printing jobs were performed with different conditions for the neural network to be trained on. The argument for using a case study in this project, and a more detailed explanation of the jet jobs can be found in Chapter 3.

The workload between us has been equally shared in this project and both of us have been working on the software and the hardware. The main purposes of doing equally much in all areas were that the both of us should gain knowledge in all fields and that we could easily share thoughts and ideas during the development of the project. We have followed the Scrum team management framework in order to assign weekly tasks and easily perform a follow-up.

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1.7 Thesis Outline

Chapter 1 provides an introduction to the project and its purpose, as well as an overview of the company where the master’s thesis has been conducted. Furthermore, it describes the importance of this project in order to create a framework to further understand the changes in the quality of jetting deposits. Chapter 2 summarizes the literature study about the ejector technology, the properties of solder paste, the sensors used and different types of architectures of neural networks. Chapter 3 explains the chosen methodology for this project, as well as alternatives. Furthermore, it discusses the internal and external validity and the procedure of this project. Chapter 4 explains the procedure of implementing the sensors and extracting the data to the neural network, as well as the design of it. This chapter also explains the verification and validation process of each component of the project. The results of how well the neural network could predict the quality of the ejected droplets are presented in Chapter 5. Chapter 6 and 7 discuss the findings and link back to the research question and requirements. Improvements and future work are presented in Chapter 8.

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Chapter 2 Frame-of-Reference

This chapter summarizes the literature study about the ejector technology, the properties of solder paste, the sensors used, the data acquisition and different types of architectures of neural networks.

2.1 Jetting Technology

A simplified illustration of an ejector, in a jet printing machine, is shown in Figure 2.1.1, where some key components are highlighted. However, a missing key component in Figure 2.1.1, but which is shown in Figure 1.1.1, is the Archimedes screw which feeds the solder paste into the chamber from the container.

Figure 2.1.1: Simplified illustration of the ejector in a jet printing machine [4].

In the ejector, which is used in the Mycronic MY700, an Archimedes screw feeds the chamber with solder paste in a controlled way from the container with solder

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CHAPTER 2. FRAME-OF-REFERENCE

paste. The piezo expands as voltage is applied to it, causing the piston to move. The momentum from the piston is transferred into the solder paste in the chamber and the material is ejected out from the nozzle. The volume of the solder paste in the chamber is accurately controlled by the Archimedes screw. As the voltage level drops, the piezo volume is reduced and the spring moves the piston to its initial position [4]. As the piston moves back up, the process is repeated. The jet printers can eject solder paste droplets at a frequency of up to 300 Hz, and the volume of the droplets is measured in nanoliters [10], [11].

2.1.1 Piezoelectric Actuator

In a piezoelectric actuator there are piezoelectric crystals, forming a ceramic, which expand as voltage is applied and vice versa. Thus, electrical energy is converted to mechanical displacement. If an alternating voltage is applied to the material, it changes its dimensions cyclically at the frequency of the applied voltage. The frequency at which the piezo most efficiently converts the electrical energy to mechanical displacement is at its resonant frequency, which is where the impedance is the lowest [12]. The resonant frequency is determined by the composition of the piezoelectric crystals, as well as the shape and volume. The main advantages of a piezoelectric actuator is that it has high precision [12], [13], high force, fast response time [14] and fast acceleration [15]. A drawback is that they can be affected by hysteresis, that is, that the history of the electric field, stress and displacement can cause nonlinearity [16].

There are two different types of piezoelectric actuators: stack and stripe [12]. The stack piezo uses multiple stacked layers of piezo elements and each of these give a combined effect on the displacement generated from the elements [15], which is shown in Equation 2.1. Furthermore, the displacement of the stacked piezo is about 0.1–

0.15% of its total length. However, if the path of displacement is blocked, a force is applied to the blocking object. The movement of a stacked piezo actuator is defined by

∆L = n· d33· V, (2.1)

where n is the number of stacked piezo elements, d₃₃ is the piezoelectric coefficient and V is the voltage applied. The stacked piezo actuator can be divided into two different categories, which are either high or low voltage. The low voltage is rated for an operating voltage up to 200 V and the high voltage is rated for an operational voltage up to 1000 V. A stacked piezo actuator is categorized as either high or low voltage depending on the thickness of the piezo element. The thicker the piezo element is, the higher voltage it can operate at [15]. The stacked type of piezo actuator is used in the ejectors in Mycronic’s jet printing machines.

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A striped piezo actuator is configured with two stripes of piezo elements in an orientation such that when voltage is applied, one of them contracts and the other one expands [17]. This causes the striped piezo actuator to flex. However, this type is not used in the ejectors in Mycronic’s jet printing machines.

In Mycronic’s jet printing machines, the stacked piezo actuator is controlled by a multi-phase-waveform voltage level. An example of a simplified three-phase voltage- time waveform is shown in Figure 2.1.2.

Figure 2.1.2: Three-phase voltage waveform of the piezo actuator for a single ejected solder paste droplet.

2.1.2 Jet Printing Quality

At Mycronic, there are different ways of analyzing the jet printing jobs of the MY700 machine. One option of test jetting that is frequently used is to perform a BGA test, which entails producing deposits for generic BGA components. This test deposits a pattern of squares where the dots can have different sizes and distances between them [4], [18]. Figure 2.1.3a illustrates an approved jet printing job, while Figure 2.1.3b shows a faulty job. A faulty job can be confirmed if the deposits of the job have an erratic positioning or size, contain bridges of solder paste between the deposits or contain satellites. Another type of test is called RT1. This test shoots 12-dot strips with varying diameters and frequencies. The finished test boards are analyzed by the machine from which quality measurements such as diameter, satellites, area, positioning and shape can be extracted. Another solder paste inspection machine can extract additional quality measurements for each deposit, such as volume. As seen in Figure 2.1.3a, an approved job has few satellites, consistent pattern and accurate shape. In Figure 2.1.4, an image of an individual ejected droplet is shown along with the presence of a satellite.

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(a) (b)

Figure 2.1.3: Two different qualities of a BGA jet printing job [4]. (a) Accepted job. (b) Faulty job.

Figure 2.1.4: Quality measurement of a single droplet on a substrate. Positioning error, area, shape and satellites are illustrated [4].

2.2 Non-Newtonian Fluids

Fluids can be divided into two categories: Newtonian and non-Newtonian. A Newtonian fluid follows Newton’s law of viscosity, that is, that the viscosity of the fluid is independent of the shear rate [19]. Generally, the viscosity of a Newtonian fluid is constant at a given temperature and pressure, and examples of such are air and water. Not all fluids follow Newton’s law of viscosity, and these fluids are referred to as non-Newtonian. These fluids display a more complex behaviour as they do not have a constant viscosity at a given temperature and pressure. Instead, the viscosity is dependent on the flow conditions, such as shear rate, flow geometry and even kinematic history in certain cases [20].

The study of deformation and flow of material is called rheology. There are different types of non-Newtonian fluids which have different rheological properties, and they can be divided into four categories: pseudoplastic, dilatant, thixotropic and rheopectic fluids [21]. The behaviour of these can been seen in Figure 2.2.1. In a Newtonian fluid the viscosity, defined as shear stress divided by shear rate, is constant and is therefore represented by a linear relationship in the graph.

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Figure 2.2.1: Rheological properties of different non-Newtonian fluids [20].

Pseudoplastic fluids are shear thinning, meaning that as the stress increases, the viscosity decreases. An example of a shear thinning fluid is ketchup. A similar variant is yield-pseudoplastics which behave like pseudoplastics, but only after a certain yield stress. Dilatant fluids are shear thickening and behave the opposite way compared with pseudoplastics, meaning that the viscosity increases as the stress increases. Cornstarch mixed with water, also known as oobleck, is an example of this. Both thixotropic and rheopectic fluids are time-dependent. The viscosity of thixotropic fluids decreases with stress over time and with rheopectic fluids it increases [21]. Examples of a thixotropic and rheopectic fluids are solder paste and printer ink, respectively.

2.2.1 Solder Paste

Solder paste is a fluid which is composed of a mixture of metal solder powder, a binder, flux and other rheological components. The solder particles typically have a diameter between 10 and 30 µm in jet printing applications and are produced to be as spherical as possible [22]. Different alloy types can be used for the solder powder depending on the application. The binder is used to keep the paste from separating and the flux removes the oxide layer between the metal and solder as well as accelerates the wetting of the metal [23]. The composition of solder pastes affects their rheological properties, and the exact composition is generally not disclosed by the companies that produce them.

In order to provide a solder paste to Mycronic’s customers that fit their application, they cooperate with other companies that produce solder pastes [4].

The composition of solder paste gives it a non-Newtonian behaviour. When it is exposed to a shear stress, it exhibits a thixotropic behaviour, or in other words, the viscosity decreases over time. Using shear sweeps, Mycronic has tested two solder

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paste samples for shear viscosity as a function of shear rate which can be seen in Figure 2.2.2. The viscosity decreases as the shear rate increases and this confirms the thixotropic behaviour. One reason for this behaviour is that when no shear stress is applied, attractive forces between the metal particles create flocs of particles which increases the viscosity. As shear is applied these flocs break apart which decreases the viscosity of the paste. Once the shear is removed flocs begin forming again and the viscosity increases. However, the structure of the flocs might change which would mean that viscosity does not fully return to the same state as before [24].

Figure 2.2.2: Shear viscosity as a function of shear rate for two solder paste samples [4].

2.3 Sensors

This section describes the sensors that were considered for data gathering in the project.

2.3.1 Temperature

A common choice for temperature measurements is a thermocouple. Thermocouples work by having a closed circuit of two dissimilar metals, as can be seen in Figure 2.3.1.

If there is a difference in temperature between the two junctions of the thermocouple, a voltage will be produced between the two metals due to the thermoelectric effect, which can be measured at one of the junctions [25]. This voltage can then be used to determine the temperature at the opposite junction. The combination of metals used in the sensor affects the voltage produced, and this can vary between sensors. The

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main advantages of thermocouples are that they are robust, relatively inexpensive, can measure a wide range of temperatures and are self-energized. The disadvantages with the sensors are that the signal is weak which makes them sensitive to electrical noise and also that the output is non-linear and requires amplification. Two other types of temperature sensors are platinum resistance thermometers (PRTs) and thermistors.

The basic principle for both of these sensors is that their resistance is dependent on temperature. However, PRTs are more expensive than thermocouples and thermistors cannot measure as wide of a temperature range [26].

Figure 2.3.1: Simple thermocouple circuit [25].

2.3.2 Current

There are several principles for measuring current, but the most common method is using a shunt resistor [27]. A shunt resistor is a low resistance resistor used for determining the current through the resistor by measuring the voltage drop over it.

Ohm’s Law states that

V = I· R, (2.2)

where V is the voltage drop over the resistor, I is the current through it and R is the resistance. This means that the voltage changes proportionally with the current.

The advantages of shunt resistors are that they are inexpensive, robust and have high accuracy. Some things to be aware of when using them are that there is a power loss which is proportional to the square of the current, which means that they are generally not suitable for measuring high currents. Furthermore, the resistance could vary due to factors such as aging or changes in temperature, which affects the precision of the measurement [28]. Other methods of measuring current include using Hall effect sensors to measure changes in the magnetic field created by the current, as well as using sensors based on Faraday’s Law where transformers are utilized.

2.4 Data Acquisition

A powerful measurement tool that is able to make multiple measurements simultaneously and features similar standard as laboratory equipment is the Red

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Pitaya. The Red Pitaya is a single board computer which is intended to be an alternative to the more expensive laboratory equipment. It is an open-source instrumentation platform that can measure or test a variation of tasks [29]. The Red Pitaya has a built in signal generator and pre-developed apps can be downloaded from the web page or one can develop one’s own apps [30]. Depending on the version, there are two 14-bit or 10-bit analog-to-digital (A/D) and digital-to-analog (D/A) converters on the board that can measure tasks at a sampling rate of 125 MHz [29]. These fast input channels have a bandwidth of 50 MHz. The Red Pitaya also has two extension connectors, which have access to four slow analog inputs, four slow analog outputs, 16 General- Purpose Input/Output (GPIO), Inter-Integrated Circuit (I²C), Universal Asynchronous Receiver/Transmitter (UART) and Serial Peripheral Interface (SPI) [29], [31]. These slow input channels have a bandwidth of 50 kHz. So, the Red Pitaya is a useful measurement tool if there is a demand of high performance signal processing with high frequency signals of up to 50 MHz [29]. Figure 2.4.1 shows the hardware overview of the Red Pitaya where some components are highlighted.

Figure 2.4.1: Hardware overview of the Red Pitaya [31].

The Red Pitaya features the Xilinx Zynq 7010, which is pointed out in Figure 2.4.1.

This system combines a Field-Programmable Gate Array (FPGA) and a multi-core processor. The advantage of FPGAs is that they can be reprogrammed for a desired task after it has been manufactured [32]. In other words, an FPGA that is working as a microprocessor can, for example, be reprogrammed to work as a graphics card. The more common technology is the Application Specific Integrated Circuit (ASIC) where a component is designed for only one purpose throughout its lifetime [32]. One example of that technology is the graphics processing unit (GPU) inside a modern phone, where the logic cannot be reprogrammed to work as another component.

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2.5 Neural Network Architecture

The main objective of a neural network is to recognize patterns. This is made possible by first having the neural network learn from a series of defining sets of input and output correspondences. The neural network can then apply what it has learnt to new, and unseen, input data to predict a relevant output [33]. A typical neural network is seen in Figure 2.5.1. The structure of neural networks consists of an input layer, one or more hidden layers, an output layer and interconnections between nodes of different layers.

Figure 2.5.1: Typical architecture of a fully connected neural network with one hidden layer.

The training process of a neural network can be divided into two categories:

forward-propagation and back-propagation. During forward-propagation the information is sent through the neural network and a prediction is made. The process from the input layer to the output layer is such that the input layer first receives information from an external source. That information is passed, via the connections, to nodes of the hidden layer, which processes all the information. Lastly, the output layer receives the processed data which is given to the user. The path of the information from the input layer, through the hidden layer, to the output layer is determined by the strength of the interconnections between nodes. Each node has a set of weights which determines the importance of its inputs, as well as a bias which adjusts the output.

When a node in the input layer receives information, it is activated. That triggers a signal by the activation function to be emitted to its neighbouring nodes. This signal is either excited or inhibited depending on the strength of the interconnection, that is,

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the magnitude of the weights and biases. This process continues on through the neural network, which creates a pattern of activation that manifests itself in the output layer [33]. The forward-propagation in a neural network is defined, mathematically, by

a^(l) = g(a^(l⁻¹⁾; Θ), (2.3)

where g is the activation function, a is the preactivation, l denotes the layer and Θ represents the parameters, or in other words, the weights and biases. The preactivation is a weighted sum of the inputs to the layer. Figure 2.5.2 shows the principle of actions in an artificial neuron. First, the weighted sum of the input parameters, Θ_n, is calculated and then passed through an activation function, g.

Figure 2.5.2: Structure of an artificial neuron in a neural network.

Examples of activation functions are sigmoid, tanh and linear, which are defined by Equation 2.4, Equation 2.5 and Equation 2.6 respectively.

g(z) = 1

1 + e^−z (2.4)

g(z) = tanh(z) (2.5)

g(z) = z (2.6)

The first two activation functions are non-linear functions, whose purpose are to introduce non-linearity into the neural network. Equation 2.6, on the other hand, is a linear activation function. If only linear activation functions are used in the hidden layers of a neural network, the output will just be a linear transformation of the input.

In other words, a composition of successive linear transformations is equivalent to one linear transformation, which means that complex non-linear problems cannot be accurately mapped between input and output. Moreover, most real world problems are highly complex and non-linear, which is why non-linear activation functions are required in, at least, the hidden layers of a neural network. However, a linear activation

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function can be used in the output layer if a continuous value shall be predicted.

When this process is finished and the error of the prediction, that is the loss, has been calculated, the model has done its forward-propagation. However, in order to learn, that is, update its weights, back-propagation is needed. The purpose of back- propagation is to minimize the error that is propagated from each node to the total error [33]. This is made possible by a technique named gradient descent, which tunes the weights in order to minimize the loss function which evaluates how the model is performing. Minimizing the loss function is, thus, an optimization problem in terms of tuning the weights of the neural network. Since the loss function is a summation of the prediction errors by the neural network, the lower the loss, the better the performance of the neural network. Examples of methods to calculate the loss are mean absolute error (MAE) and mean relative error (MRE). These two methods are defined as

M AE = 1 n

∑n i=1

|yi− ˆyi|, (2.7)

M RE = 1 n

∑n i=1

|yi− ˆyi|

y_i , (2.8)

respectively, where n is the total number of data points, y is the true value and ˆyis the predicted value.

At each update, how much to modify the model with respect to the estimated error is determined by the learning rate. An excessive learning rate can cause an unstable training process, whereas a rate that is too low will require a longer training process.

Thus, the main idea behind training neural networks is to minimize the loss function by modifying the parameter of the model and in turn maximizing the accuracy [34]. As one iteration of forward- and backward-propagation is completed, the neural network has completed one epoch of training.

While training neural networks, the model is likely to overfit if there is no regularization. When overfitting, the model performs well on the training data but poorly on the new, unseen, data. This can be seen as a decreasing training loss, but constant or increasing validation loss while training. This means that the neural network has only memorized the training data rather than generalized on new data.

To minimize the risk of overfitting, different regularization techniques could be used, such as dropout or L2 regularization. Implementing dropout randomly removes connections in the neural network during training. Thus, the neural network cannot rely on the connections between nodes, which prevents it from overfitting. The L2 regularization method dynamically penalizes the weights, such that large weights are penalized more and vice versa. As with dropout, the L2 regularization also decorrelates the neural network.

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2.5.1 Recurrent Neural Network

Recurrent neural networks (RNNs) are neural networks that are specialized in processing sequences of data which can have variable lengths [35]. The main difference between the structure of an RNN and other neural networks is that the nodes of an RNN have a recurrent connection, which stores previous calculations and, thus, functions as a memory. This results in the RNN having two inputs, the present and the recent past. The additional input about the past holds valuable information about the future [36]. Figure 2.5.3 shows a cell in an RNN, which has the recurrent connection that is different from other neural networks, as seen in Figure 2.5.1.

Figure 2.5.3: Circuit diagram of a cell in an RNN. Here, xtis the input at time t, htis the state of the hidden layer at time t and o_tis the output at time t. Parameters for the input, hidden layer state and output are Θ_i, Θ_hand Θ_o, respectively [37].

The graphical model of an RNN cell in Figure 2.5.3 can be explained with the following equations:

o_t = f (h_t; Θ), (2.9)

h_t= g(h_t₋₁, x_t; Θ). (2.10)

In Equation 2.9 and Equation 2.10, o_t is the output of the RNN at time t, f and g are activation functions, h_t is the state of the hidden layer at time t, x_t is the input at time t and Θ represents the weights and biases. Equation 2.9 shows that the output is dependent on the weights and biases and also the state of the hidden layer at time t.

However, Equation 2.10 shows that the state of the hidden layer at time t is dependent on the weights and biases, input at time t and the state of the hidden layer at time t− 1.

The latter equation is what differentiate RNNs from other neural networks since the previous state of the hidden layer, h_t₋₁, has influence on the current state of the hidden

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layer, h_t[37]. This demonstrates that the RNNs have a memory.

However, there are two major drawbacks of the RNN architecture: vanishing and exploding gradients. Both of these issues can occur only during the back-propagation phase if there are long-term dependencies, that is, it has to memorize a long sequence.

So, the vanishing or exploding gradients occur due to multiplication in the chain rule of the partial derivatives in the back-propagation through time [37]. Gradients that are less than one shrink exponentially due to continuous matrix multiplication until the gradients vanish. The same applies for the exploding gradients when the gradients are greater than one, but then the gradients start increasing and eventually cause a numerical overflow. A solution to this issue is to choose an alternative recurrent neural network, namely long short-term memory (LSTM).

Long Short-Term Memory

The LSTM architecture is a gated version of the RNN architecture, which addresses the issue of long-term dependencies [35], [38]. This implies a more complicated structure of the cell than in RNNs. As seen in Figure 2.5.4, the cell consists of three different gates: a forget gate, an update gate and an output gate. The gates consist of either a sigmoid or a tanh function (see Equation 2.4 and Equation 2.5 respectively) in order to control the flow of information through the LSTM cell. These two types of activation functions in the LSTM cell also introduce non-linearity to the neural network.

Figure 2.5.4: Illustration of the data flow through an LSTM cell. The three different gates are highlighted: input gate, update gate and output gate [39].

The inputs of the LSTM cell are the current input, x_t, the previous hidden state, h_t₋₁, and the previous memory state, c_t₋₁. The outputs are the current memory state, c_t, and the current hidden state, h_t. The core concept of an LSTM cell is that information can be passed forward on the cell state memory line, shown as the top horizontal line in Figure 2.5.4, and information can either be removed or added by

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the forget and update gates, respectively. This enables relevant information to be transferred, touched or untouched, throughout the processing of the sequence, which addresses the problem of long-term dependencies with RNNs [35]. Figure 2.5.4 shows one of many cells that can be connected in series, which can be simplified by a recurrent connection as in Figure 2.5.3. Thus, the illustration in Figure 2.5.3 can be extended by removing the recurrent connection and adding as many cells as the length of the input sequence in series. The cell outputs in Figure 2.5.4 can be expressed mathematically by:

c_t = f_t⊙ ct−1+ i_t⊙ gt, (2.11)

h_t= o_t⊙ σc(c_t) (2.12)

where the forget gate, f_t, the update gate, i_tand g_t, and the output gate, o_t, are defined as

f_t = σ_g(h_t₋₁, x_t; Θ_f), (2.13)

i_t= σ_g(h_t₋₁, x_t; Θ_i), (2.14)

g_t= σ_c(h_t−1, x_t; Θ_g), (2.15) and

o_t= σ_g(h_t₋₁, x_t; Θ_o), (2.16) respectively, where σ_g is the gate activation function and σ_c is the state activation function.

Different types of LSTM models include vanilla, stacked, encoder-decoder and bidirectional. Vanilla LSTMs are often referred to as the default or standard version of the architecture and consists of an input layer, one fully connected hidden LSTM layer and a fully connected output layer. This is the simplest version of an LSTM and is generally a good starting point for solving a problem. Models with more than one hidden LSTM layer are referred to as stacked LSTMs. The advantage of having more than one layer is that it improves the success of a neural network. Additionally, having several small layers is generally more efficient than having one large layer [40]. The layout for these two models can be seen in Figure 2.5.5a and Figure 2.5.5b.

The encoder-decoder model is useful for sequence-to-sequence problems, that is, when the input is a sequence of values and the goal is to predict the coming sequence of values. This architecture contains an encoder model which processes the input and encodes it into a vector with fixed length. This vector is then given to the decoder model which decodes the vector and gives the predicted sequence. The main use of the

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architecture is natural language processing and text translation, its layout can be seen in Figure 2.5.5c. The encoder-decoder model has been found to occasionally be more efficient when the input is reversed, and this phenomena is utilized in the bidirectional model. In this model the input is fed to two layers which are side-by-side, as can be seen in Figure 2.5.5d. The forward input sequence is given to the first layer and a reversed version of the input sequence is given to the other layer. This method has been known to increase the performance of a neural network, but it does require that the entire input is available [40].

(a) (b) (c) (d)

Figure 2.5.5: LSTM models [40]. (a) Vanilla LSTM. (b) Stacked LSTM. (c) Encoder- decoder LSTM. (d) Bidirectional LSTM.

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Chapter 3 Methodology

The following chapter presents the methodology used in this project. The chosen research strategy, the internal and external validity, as well as an overview of the procedure used are presented and discussed.

3.1 Research Strategy

It is of importance to choose a methodology in research since it explains what type of systematic approach that is being used to solve the problem. In other words, a work plan to address the research problem by defining the procedure of methods by which knowledge is obtained [41]. The methodology also defines the quality assurance of the project, that is, the validation and verification of the research material [42].

The methodology used in this project was an empirical quantitative research approach, utilizing a case study as the research strategy. In [43], the purpose of quantitative research is defined to study relationships, cause and effect. It is also mentioned in [42], [43] and [44] that quantitative research is characterized by large data sets. Considering that the purpose of this project was to gather large amounts of data and to create a neural network to examine the effect certain parameters have on the quality of jetting deposits, a quantitative research approach was deemed to be suitable.

A case study was chosen due to its usefulness when doing an empirical study of a particular phenomenon using multiple sources of evidence [42]. The phenomenon to be studied in this project was how the measured temperature and piezo current affected the accuracy in predicting the quality of jetting deposits. In [45], it is stated that a case study is beneficial if knowledge shall be obtained regarding a new phenomena. This project is the first at Mycronic that investigates the potential benefits of applying a neural network in their jet printing machine to predict the quality of the deposits. The use of a case study is also supported by [46], in which it is stated that a case study will

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CHAPTER 3. METHODOLOGY

give indications on hypothesis creation. Since the intended purpose of this project was to create a hypothesis regarding how the defined sensors improved the ability to predict quality in jet printing machines, a case study was appropriate for achieving this. Using the knowledge gained in this project, the created hypothesis can be examined further in future projects, which is discussed in Chapter 8.

An alternative research method is using experiments. Experimental methods deal with the relationship and effects between variables as they are manipulated [42]. Since it is not within the scope of this project to manipulate variables, such as temperature or current, an experimental method was not chosen. It is also mentioned in [47]

that an advantage with using case studies over experiments is that only naturally occurring cases are investigated, rather than cases created by the researcher. Thus, the realism of the project can be assured by using a case study. Additionally, the causal hypotheses generated by case studies can sometimes enable researchers to recognize causal relationships in a way that is not possible in experimental research [47].

There were two different types of jet printing jobs used for collecting the data, both of which are frequently used by Mycronic’s test engineers. One was a simpler job, while the other was a more complex and realistic job. The simpler one was a BGA test, where the shots had a constant diameter and were shot at a constant frequency. Since a majority of jobs used by customers do not have constant diameters and frequencies, this job was considered to be a simple test. The other job was the RT1 job, where the diameter of the shots varied between five different values and the frequency between three different values. Both tests are explained in more detail in Section 2.1.2. These tests are used by Mycronic to evaluate the robustness of the machine, but they can also be considered to be realistic tests since they simulate the machine usage by the customers. Varying variables are more difficult for the neural networks to predict. By performing these two types of jobs, a clearer and more unambiguous evaluation of the neural network could be made. Since the tests were performed on a real machine using real jet jobs used by test engineers, the tests could be generalized for the given combination of ejector and solder paste used in this project.

3.2 Internal and External Validity

In order to provide an answer to the research question, this project used a case study which was split into two different tests with different amounts of varying variables.

The two cases were chosen such that the performance of the neural network could be verified and also so that the realism and internal validity of the project would increase.

As mentioned in [48], the internal validity of a project can increase if multiple data sources of the same method are used. By using the two different jobs, the project

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covered a broader range of different usages of the MY700. The BGA job was useful to first find potential correlations between the sensor data and the quality of the deposits, while the RT1 both checked the robustness of the neural network and simulated the real environment it would be active in. Another factor that was important for the internal validity of this project was that the created neural network was capable of finding correlations in the data if there were any. The ability of the neural network to recognize patterns was verified and is described in Chapter 4, but it is possible that other architectures than the one chosen would be even better at finding correlations.

In [49], it is mentioned that external validity relates to how generalizable a study is. After discussion with the stakeholders at Mycronic, it was decided that the main focus of the project was hypothesis creation and to create a framework upon which further studies can be performed. Due to time and resource limitations, it was not in the scope of the project to evaluate with other hardware setups, such as using different machines, ejectors or solder pastes. This could however be done in future projects to further examine the generalizability of the results and to improve the external validity.

3.3 Procedure

The first phase of the project was to create the project formulation, decide on a methodology and create a project time plan. After that, a background study about ejector technology, the sensors that were used and neural network architectures was done. Since data collection was one of the main components of this project, it was of high importance to carefully study both what data points to get and also how to process them. After the prestudy was finished, the test setup was created and data was gathered from a MY700. The temperature sensor data was gathered from three different places on the ejector, as explained in Section 1.2 and shown in Figure 1.2.1, and a shunt resistor was used to measure the current through the piezoelectric actuator. When this configuration had been built, the two types of jobs explained above were run. The data from the sensors and the jetting deposits was given to the neural network and training was performed. Finally, the accuracy of the neural network was evaluated and conclusions were drawn.

During the design of the project the replicability has been thought of to enable future improvements to the project. It is mentioned in [42] that replicability is one of several quality assurances to be aware of when designing the methodology. For this project, the procedure for how to setup the hardware components in order to gather data is documented and also the verification and validation process of each component.

Furthermore, a seminar has been held at Mycronic for the engineers, for the purpose of transferring knowledge of the project and how to replicate the tests.

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Chapter 4 Implementation

The following chapter will first explain the implementation of the hardware and software that have been developed and used throughout the project in Section 4.1 and Section 4.2, respectively. Furthermore, the procedure to perform the cases is clarified in Section 4.3. Lastly, verification and validation of units of the system will be presented in Section 4.4.

4.1 Hardware Configuration

This section describes how the machine, cassette and ejector were modified in order to install the sensors. An overview of the hardware system is shown in Figure 4.1.1 and highlighted components are the Red Pitaya, the trigger signal from the piezo PCB driver and the cassette containing the solder paste and ejector. The cassette contains the current sensor and the ejector contains the temperature sensors. Furthermore, a PCB was developed in order to enable the Red Pitaya to read the values from the temperature sensors as well as the trigger signal from the MY700.

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CHAPTER 4. IMPLEMENTATION

Figure 4.1.1: The hardware setup in the MY700 for data gathering.

Modifications to the MY700 included two Bayonet Neill–Concelman (BNC) coaxial connectors that were connected to the debug pins of the piezo PCB to extract the trigger signal, which is marked as ”Trigger signal” in Figure 4.1.1. This signal is used by the Red Pitaya in order to initiate sampling from the sensors. The Red Pitaya was mounted on top of the horizontal beam which moves the printing head back and forth, as shown in Figure 4.1.1. A power cable and an Ethernet cable were run via the wiring harness out through the back of the machine. The wires for the sensors along with the coaxial cable for the trigger signal were run the opposite direction up to the printing head.

Cable ties were used to attach the cables and extra caution was used to ensure that no cables came in the way of the machinery.

Figure 4.1.2 illustrates the data transfer between the different hardware components. The connection between the MY700 and the database, labeled ”Job data”, represent the quality measurements taken by the machine. The data is obtained through a series of photographs taken by the machine on the deposits. The images undergo a processing stage to extract quality measurements, such as diameter, positioning, shape and satellites. These measurements are then sent to the database to be stored together with its corresponding sensor data.

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Figure 4.1.2: Flow chart illustrating the data transfer between different components.

4.1.1 Sensors

In Chapter 1, it was explained that three positions for the temperature measurement would be used, as shown in Figure 1.2.1. The positioning of the sensors was determined by the work in [4], but a more extensive explanation of the importance of the positions has been made in Chapter 1. Since these positions include components and mechanisms that can be affected by changes in the rheological properties, which is a function of temperature, it was decided to maintain these temperature sensor positions. However, it was noticed that the mounting of the sensors could be improved.

The previous assembly was such that the sensors were inserted into the drilled holes and a glue gun was used to install them in that position. This setup ran the risk of potentially insulating the sensors from the ejector chassis if some glue had entered the hole. Thus, a new ejector was modified with the same configuration as in [4], but a heat conductive paste was added into the holes with the sensors before fastening them with a glue gun. This ensured a more accurate temperature reading from the sensors.

To measure the temperature, IT-18 thermocouples [50] were used which have an accuracy of ±0.1 °C. These were the same type of sensors as the ones used in [4], and the blue arrows in Figure 4.1.2 show the data measured by the thermocouples being transferred to the Red Pitaya. In this project, the thermocouples measured temperatures in the range of 20 °C to 40 °C. The signals from the thermocouples in that range were between 1.196 mV to 1.612 mV [51]. These signals had to be amplified, which is explained in Section 4.1.2 below. The thermocouple that was placed by the ejector nozzle protruded slightly from the bottom of the ejector. This meant that the distance between the nozzle and the substrate had to be increased from the default value of 650 μm to 800 μm when operating the MY700.

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To measure the current, it was decided to keep the setup used in [4] which was to have a shunt resistor on the low side of, and in series with, the piezoelectric actuator.

This reduced any issues with the common-mode voltage. If the current and resistance are not too high, a shunt resistor also dissipates low amounts of power since

P_D = I²· R, (4.1)

where P_D is the dissipated power, I is the current and R is the resistance. Thus, there is less possible influence on the circuit. Two shunt resistors were mounted in parallel, each with a resistance of R_s = 0.1 Ω, which resulted in a total resistance of R_s = 0.05 Ω. The green arrow in Figure 4.1.2 shows how the data measured by the shunt resistor is transferred to the Red Pitaya.

4.1.2 Red Pitaya

It was decided that the same Red Pitaya as the one used in [4] would be used for this project as well. The three thermocouples were connected to the Red Pitaya via a PCB which was mounted on top of the Red Pitaya, as can be seen in Figure 4.1.3.

Three thermocouple amplifiers with cold junction compensation were used to amplify the signals from the thermocouple. Cold junction compensation means that these integrated circuits (ICs) use an ice point reference to provide a temperature reference for the thermocouples, which was needed in order to make temperature readings.

Once amplified, a 10 mV change of the output signal corresponded to a 1 °C change in temperature [52]. All output signals were then given to the analog input pins of the Red Pitaya. A BNC coaxial connector was mounted on top of the PCB and connected to one of the digital input pins of the Red Pitaya. This was used to register a trigger signal which was sent out from the machine every time a shot was ejected. This trigger signal let the Red Pitaya know when to take a measurement and is shown as the red arrow in Figure 4.1.2. The temperature and trigger signals, along with ground and 5 V signals, were connected between the PCB and Red Pitaya using two flat cables with a 26-way insulation-displacement contact (IDC) connector plug at each end. The current sensor was connected directly to one of the 14-bit fast channels of the Red Pitaya which had a sampling rate set to 15.6 MHz. This was done using another BNC type connector, which is marked as the green arrow in Figure 4.1.2. The low voltage input (±1 V) of the fast channel was used, since the signal would never exceed ±1 V with the chosen shunt resistor. The signal was further downsampled to 3.9 MHz to reduce memory usage and increase efficiency. A sampling rate of 3.9 MHz was deemed to be sufficient since this would give 390 samples, which for each current curve would resolve the important trends. A 14-bit resolution was also deemed to be enough

Quality Prediction in Jet Printing Using Neural Networks