Abu Bakar / Mohamad Sidik / Öchsner Progress in Engineering Technology

Automotive, Energy Generation, Quality Control and Efficiency
1. Auflage 2019
ISBN: 978-3-030-28505-0
Verlag: Springer Nature Switzerland
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 119, 268 Seiten

Reihe: Advanced Structured Materials

This book presents recent developments in the areas of engineering and technology, focusing on experimental, numerical, and theoretical approaches. In the first part, the emphasis is on the emerging area of electromobility and its sub-disciplines, e.g. battery development, improved efficiency due to new designs and materials, and intelligent control approaches. In turn, the book's second part addresses the broader topic of energy conversion and generation based on classical (petrol engines) and more modern approaches (e.g. turbines). The third and last part addresses quality control and boosting engineering efficiency in a broader sense. Topics covered include e.g. modern contactless screening methods and related image processing.

Andreas Öchsner is a Full Professor of Lightweight Design and Structural Simulation at Esslingen University of Applied Sciences, Germany. After receiving his Dipl.-Ing. degree in Aeronautical Engineering from the University of Stuttgart (1997), Germany, he served as a research and teaching assistant at the University of Erlangen-Nuremberg from 1997 to 2003 while working to complete his Doctor of Engineering Sciences degree. From 2003 to 2006, he was an Assistant Professor at the Department of Mechanical Engineering and Head of the Cellular Metals Group affiliated with the University of Aveiro, Portugal. He spent seven years (2007-2013) as a Full Professor at the Department of Applied Mechanics, Technical University of Malaysia, where he was also Head of the Advanced Materials and Structure Lab. From 2014 to 2017, he was a Full Professor at the School of Engineering, Griffith University, Australia, and Leader of the Mechanical Engineering Program (Head of Discipline and Program Director). Muhamad Husaini Abu Bakar is Director of System Engineering and the Energy Laboratory and Head of the Research and Innovation Section at the Universiti Kuala Lumpur - Malaysian Spanish Institute, Malaysia. After receiving his Bachelor's degree in Manufacturing Engineering with Management from the Universiti Sains Malaysia (2007), Malaysia, he worked with the university's Underwater Robotic Research from 2007 to 2012, and was awarded a Master of Science in Advanced Manufacturing in 2011. Since 2012 he has been a lecturer at the Universiti Kuala Lumpur - Malaysian Spanish Institute, and completed his Doctor of Philosophy in Advanced Manufacturing from Universiti Sains Malaysia in 2017. His research interests are related to smart manufacturing, energy, and atomistic modeling. He has published over 50 scientific publications on Deep Learning, computational atomistic and metal-air batteries. His inventions and contributions to smart materials and smart monitoring systems have been recognized with innovation awards from PECIPTA, IIDEX, ICOMPEX, I-ENVEX  and MARA. Mohamad Sabri Mohamad Sidik completed his Bachelor's degree in Mechanical Engineering at Universiti Sains Malaysia, Pulau Pinang, in 2005. In the same year, he began working as a mechanical design engineer and handled several projects at a local company until 2009. He subsequently worked as an assistant lecturer at the Universiti Kuala Lumpur Malaysian Spanish Institute, Kedah, Malaysia and was appointed as a Program Coordinator of Diploma of Engineering Technology in Mechanical Design and Development. He received his Master's degree in Manufacturing System Engineering from the Universiti Putra Malaysia, Selangor, Malaysia in 2012. His research interests in mechanical engineering are in the areas of natural fiber composites, finite element methods for mechanical structures, electric and underwater vehicle design, and metal-air batteries. Currently, he is a doctoral student at the Universiti Kuala Lumpur and is exploring the capabilities of metal clusters, especially in the context of alternative energies.

Autoren/Hrsg.

Abu Bakar, Muhamad Husaini

Mohamad Sidik, Mohamad Sabri

Öchsner, Andreas

Inhaltsverzeichnis

1;Preface;6
2;Contents;7
3;1 Study the Effect of Acetone as an Inhibitor for the Performance of Aluminium-Air Batteries;10
3.1;Abstract;10
3.2;1 Introduction;11
3.3;2 Experimental;12
3.3.1;2.1 Computational Study;12
3.3.2;2.2 Materials;12
3.3.3;2.3 Weight Loss;13
3.3.4;2.4 Battery Test;14
3.4;3 Results and Discussion;15
3.4.1;3.1 Optimised Geometry;15
3.4.2;3.2 HOMO-LUMO Energy;15
3.4.3;3.3 Inhibition Efficiency;17
3.4.4;3.4 Voltage Discharge Rate;19
3.4.5;3.5 Capacity Discharge Rate;21
3.5;4 Conclusions;22
3.6;Acknowledgements;22
3.7;References;22
4;2 Performance Characteristics of Palm Oil Diesel Blends in a Diesel Engine;25
4.1;Abstract;25
4.2;1 Introduction;25
4.3;2 Literature Review;27
4.4;3 Experimental Set-up and Procedures;28
4.4.1;3.1 The Engine;28
4.4.2;3.2 Palm Oil Diesel Blends;28
4.4.3;3.3 Experimental Flow;30
4.5;4 Results and Discussion;30
4.5.1;4.1 Performance of the Engine on Diesel Fuel and Palm Oil Blends;30
4.6;5 Conclusion;35
4.7;Acknowledgements;36
4.8;References;36
5;3 Optimization of Palm Oil Diesel Blends Engine Performance Based on Injection Pressures and Timing;38
5.1;Abstract;38
5.2;1 Introduction;39
5.3;2 Literature Review;39
5.4;3 Experimental Set-up and Procedures;40
5.4.1;3.1 The Engine;40
5.4.2;3.2 Palm Oil Diesel Blends;40
5.4.3;3.3 Performance Measurements;42
5.4.4;3.4 Optimization;43
5.5;4 Results and Discussion;43
5.5.1;4.1 Engine Performance at the Original Setting;43
5.5.2;4.2 Optimization of Palm Oil Diesel Blends Using DOE;45
5.6;5 Conclusion;47
5.7;Acknowledgements;47
5.8;References;48
6;4 The Potential of Improving the Mg-Alloy Surface Quality Using Powder Mixed EDM;49
6.1;Abstract;49
6.2;1 Introduction;50
6.3;2 Working Principle of PMEDM;50
6.4;3 Research Articles on PMEDM;53
6.5;4 Improving Mg-Alloy Surface Quality Using PMEDM;55
6.6;5 Conclusion;56
6.7;References;57
7;5 Validation of Driver’s Cognitive Load on Driving Performance Using Spectral Estimation Based on EEG Frequency Spectrum;60
7.1;Abstract;60
7.2;1 Introduction;60
7.3;2 Literature Review;61
7.4;3 Methodology;62
7.4.1;3.1 Experimental Setup for Data Recording;62
7.4.2;3.2 Driving Tasks;63
7.4.3;3.3 Data Validation;63
7.4.4;3.4 Pre-processing;64
7.4.5;3.5 Feature Extraction;65
7.4.6;3.6 Feature Classifiers;65
7.5;4 Results and Conclusion;67
7.6;Acknowledgements;69
7.7;References;69
8;6 Analytical Study of a Cylindrical Linear Electromagnetic Pulsing Motor for Electric Vehicles;71
8.1;Abstract;71
8.2;1 Introduction;72
8.3;2 Cylinder Linear EMPM Structure;73
8.4;3 Magnetic Circuit Model Analysis;73
8.4.1;3.1 Predicted Magnetic Equivalent Circuit;73
8.4.2;3.2 Predicted Magnetic Analysis;76
8.5;4 Experimental Setup;77
8.6;5 Results and Discussion;79
8.6.1;5.1 Plunger Force;79
8.6.2;5.2 Thrust;79
8.6.3;5.3 Plunger Distance;80
8.6.4;5.4 Speed;82
8.6.5;5.5 Power Motor;84
8.6.6;5.6 Comparison Between Experimental Result and FEM Data;85
8.7;6 Conclusions;85
8.8;Acknowledgements;86
8.9;References;86
9;7 Investigation on Effective Pre-determined Time Study Analysis in Determining the Production Capacity;87
9.1;Abstract;87
9.2;1 Introduction;88
9.3;2 The Background of Pre-determined Time Study;89
9.4;3 The Research Framework;89
9.5;4 Data Collection, Analysis and Discussions;90
9.5.1;4.1 Case Study Selection: Process Familiarization;90
9.5.2;4.2 Analysis of the Production Cycle Time: Basic MOST® Versus Stop Watch;91
9.5.2.1;4.2.1 Case Study 1: The Vendor of Automotive Industry;92
9.5.2.2;4.2.2 Case Study 2: The Refurbishment of Petroleum Gas and Refurbishment Services;93
9.5.3;4.3 Results Analysis: Comparison Between Basic MOST® Versus Stop Watch;93
9.6;5 Conclusions;94
9.7;Acknowledgements;94
9.8;References;94
10;8 Vibration Measurement on the Electric Grass Trimmer Handle;96
10.1;Abstract;96
10.2;1 Introduction;97
10.3;2 Methodology;98
10.3.1;2.1 Vibration Measurement;98
10.3.2;2.2 Experimental Modal Analysis;98
10.3.3;2.3 New Handle Design;100
10.3.4;2.4 Active Vibration Control;100
10.4;3 Results and Discussion;102
10.4.1;3.1 Natural Frequencies;103
10.4.2;3.2 Reduction of Vibration Level;103
10.5;4 Conclusions;104
10.6;Acknowledgements;104
10.7;References;104
11;9 Low Harmonics Plug-in Home Charging Electric Vehicle Battery Charger Utilizing Multi-level Rectifier, Zero Crossing and Buck Chopper;105
11.1;Abstract;105
11.2;1 Introduction;106
11.3;2 Methodology;106
11.3.1;2.1 Proposed Battery Charger with Multi Level Rectifier;106
11.3.2;2.2 Proposed Multi Level Rectifier and Buck Converter for Battery Charging;107
11.3.2.1;2.2.1 Zero Crossing;107
11.3.2.2;2.2.2 The Bridge Rectifier Circuit;110
11.3.2.3;2.2.3 The Multi-level Inverter;112
11.3.2.4;2.2.4 The Buck Chopper;114
11.4;3 Results and Discussion;115
11.5;4 Conclusions;118
11.6;References;119
12;10 A New Four Quadrants Drive Chopper for Separately Excited DC Motor in Low Cost Electric Vehicle;121
12.1;Abstract;121
12.2;1 Introduction;122
12.3;2 Methodology;124
12.3.1;2.1 Four Quadrants Drive DC Chopper Operation Modes;124
12.3.2;2.2 Driving Mode;125
12.3.3;2.3 Field Weakening Mode;126
12.3.4;2.4 Generator Mode;127
12.3.5;2.5 Regenerative Braking Mode;127
12.3.6;2.6 Resistive Braking;128
12.3.7;2.7 Experimental Setup;130
12.3.8;2.8 Results and Discussion;130
12.4;3 Conclusions;139
12.5;References;139
13;11 Genetics Algorithm for Setting Up Look Up Table in Parallel Mode of Series Motor Four Quadrants Drive DC Chopper;141
13.1;Abstract;141
13.2;1 Introduction;142
13.3;2 Methodology;142
13.3.1;2.1 A Proposed Design of Four-Quadrants Drive DC Chopper;142
13.3.2;2.2 The Proposed Four-Quadrant DC Chopper Design;143
13.3.3;2.3 DC Series Motor and Control Strategy During Parallel Mode;144
13.3.4;2.4 The Control Strategy in Parallel Mode;144
13.3.5;2.5 Genetic Algorithm;147
13.4;3 Simulation Model and Results;149
13.5;4 Conclusions;155
13.6;References;155
14;12 Series Motor Four Quadrants Drive DC Chopper;157
14.1;Abstract;157
14.2;1 Introduction;158
14.3;2 Four Quadrants Drive DC Chopper Operation Modes;158
14.3.1;2.1 Methods of Excitation for Generator Mode;160
14.3.2;2.2 Experimental Setup;164
14.4;3 Results and Discussion;166
14.5;4 Conclusions;168
14.6;References;168
15;13 Relationship Between Electrical Conductivity and Total Dissolved Solids as Water Quality Parameter in Teluk Lipat by Using Regression Analysis;170
15.1;Abstract;170
15.2;1 Introduction;171
15.3;2 Study Area;171
15.4;3 Methodology;171
15.5;4 Result and Discussion;173
15.6;5 Conclusion;173
15.7;References;174
16;14 A Study of the Region Covariance Descriptor: Impact of Feature Selection and Precise Localization of Target;175
16.1;Abstract;175
16.2;1 Introduction;176
16.3;2 Related Work;176
16.4;3 Our Approach;177
16.4.1;3.1 Region Covariance Descriptor;177
16.5;4 Experiments and Comparison;180
16.5.1;4.1 Evaluation Methodology;180
16.5.2;4.2 Feature Selection;180
16.5.3;4.3 Quantitative Analysis;181
16.6;5 Conclusions;182
16.7;References;182
17;15 Analysis of a Micro Francis Turbine Blade;183
17.1;Abstract;183
17.2;1 Introduction;184
17.3;2 Literature Review;184
17.4;3 Methodology;186
17.4.1;3.1 3D Modelling of the Proposed Design;186
17.4.2;3.2 ANSYS Simulation;186
17.4.3;3.3 Paired Comparison Design;188
17.5;4 Results and Discussion;188
17.5.1;4.1 Analysis Graph Result;188
17.5.2;4.2 Paired Comparison Design;191
17.6;5 Conclusions;192
17.7;Acknowledgements;192
17.8;References;192
18;16 Deep Contractive Autoencoder-Based Anomaly Detection for In-Vehicle Controller Area Network (CAN);194
18.1;Abstract;194
18.2;1 Introduction;195
18.3;2 Methodology;196
18.3.1;2.1 The DCAEs Design;196
18.3.2;2.2 Experimental Setup;197
18.4;3 Results and Discussion;200
18.5;4 Conclusions;203
18.6;Acknowledgments;203
18.7;References;203
19;17 Design and Temperature Analysis of an Aluminum-Air Battery Casing for Electric Vehicles;205
19.1;Abstract;205
19.2;1 Introduction;206
19.3;2 Methodology;207
19.3.1;2.1 Design and Fabrication Process of the Aluminum-Air Battery Casing;207
19.3.2;2.2 Experimental Setup;207
19.3.3;2.3 Single Cell and 4 Cell Battery Performance Analysis;208
19.3.4;2.4 Temperature Distribution Analysis;209
19.4;3 Discussion;209
19.4.1;3.1 Single Cell Battery Performance;209
19.4.2;3.2 Battery Performance;210
19.4.3;3.3 Thermography Test;211
19.5;4 Conclusion;212
19.6;Acknowledgments;214
19.7;References;214
20;18 Corrosion Analysis of Aluminum-Air Battery Electrode Using Smoothed Particle Hydrodynamics;215
20.1;Abstract;215
20.2;1 Introduction;215
20.3;2 Methodology;216
20.4;3 Results and Discussion;217
20.4.1;3.1 Weight Loss Method;217
20.4.2;3.2 Simulation of Anode Corrosion;218
20.4.3;3.3 Velocity of Particles;219
20.4.4;3.4 Battery Performance;220
20.5;4 Conclusions;221
20.6;Acknowledgements;221
20.7;References;221
21;19 Development of an Aluminum-Air Battery Using T6-6061 Anode as Electric Vehicle Power Source;223
21.1;Abstract;223
21.2;1 Introduction;224
21.3;2 Methodology;225
21.3.1;2.1 Hydrogen Gas Release Experiment;225
21.3.2;2.2 Rate of Corrosion Experiment;226
21.4;3 Results and Discussion;227
21.4.1;3.1 Rate of Hydrogen Gas Release;227
21.4.2;3.2 Rate of Corrosion;228
21.4.3;3.3 Overall;228
21.5;4 Conclusions;229
21.6;Acknowledgements;229
21.7;References;229
22;20 Synthesis and Thermal Characterization of Graphite Polymer Composites for Aluminium Ion Batteries;231
22.1;Abstract;231
22.2;1 Introduction;232
22.3;2 Methodology;232
22.4;3 Results and Discussion;234
22.4.1;3.1 Density and Porosity;234
22.4.2;3.2 Thermal Conductivity;235
22.5;4 Conclusions;235
22.6;Acknowledgements;235
22.7;References;235
23;21 Design and Analysis of an Aluminium Ion Battery for Electric Vehicles;237
23.1;Abstract;237
23.2;1 Introduction;238
23.3;2 Methodology;238
23.4;3 Results and Discussion;240
23.4.1;3.1 Design of the Battery;240
23.4.2;3.2 Battery Discharging Curves;240
23.4.3;3.3 Thermal Distribution Analysis;240
23.5;4 Conclusions;243
23.6;Acknowledgements;243
23.7;References;243
24;22 Automotive Metallic Component Inspection System Using Square Pulse Thermography;245
24.1;Abstract;245
24.2;1 Introduction;246
24.3;2 Literature Review;247
24.3.1;2.1 Non-destructive Test;247
24.3.2;2.2 Thermography;247
24.3.3;2.3 Active Thermography and Passive Thermography;248
24.3.4;2.4 Image Processing;248
24.4;3 Methodology;249
24.4.1;3.1 Square Pulse Thermography Experiment;249
24.4.2;3.2 Image Processing;250
24.4.3;3.3 Diameter Estimation Method;252
24.5;4 Result and Discussion;252
24.5.1;4.1 Experiment Setup;252
24.5.2;4.2 Image Processing;254
24.5.3;4.3 Results;255
24.6;5 Conclusion;255
24.7;Acknowledgements;256
24.8;References;256
25;23 Deep Neural Network Modeling for Metallic Component Defects Using the Finite Element Model;257
25.1;Abstract;257
25.2;1 Introduction;258
25.3;2 Methodology;260
25.3.1;2.1 Project Process;260
25.3.2;2.2 Finite Element Analysis;260
25.3.3;2.3 Deep Neural Network Modelling;263
25.4;3 Result and Discussion;263
25.4.1;3.1 Performance Analysis of Proposed Convolution Neural Network Model;263
25.5;4 Conclusion;267
25.6;Acknowledgements;268
25.7;References;268