Tien-Chien Jen, Edwell Tafara Mharakurwa, Steven Otieno Otieno, Fredrick Madaraka Mwema, Job Maveke Wambua

Injection Molding Process Modelling (eBook, PDF)

Statistics, CAE, and AI Applications

• Format: PDF

Produktbeschreibung

Injection Molding Process Modelling presents the application of CAE, statistics and AI in defect identification, control, and optimization of injection molding process for quality production. It showcases CAE in determining the optimal placement of injection points, designing cooling channels, and ensuring that the mold will produce parts with the desired specifications. The book illustrates the capability of the CAE tools to simulate molten plastic flow within a mold during the injection molding process.

Explaining how the use of CAE, statistical tools and AI enhances efficiency, accuracy, and collaboration, the book explores the contributions to injection molding in product design and visualization; prototyping and testing; mold design; and analysis and simulation. It emphasizes the integration of statistical tools for optimized efficiency and waste reduction, including statistical process control (SPC), Design of Experiments (DOE), Regression Analysis, Capability Indices, Interaction effects, and many more. The book also illustrates the predictive modelling of typical injection molded product defects using intelligent algorithms.

The book will interest industry professionals and engineers working in manufacturing, production, automation, and quality control.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 130
• Erscheinungstermin: 11. September 2024
• Englisch
• ISBN-13: 9781040116678
• Artikelnr.: 72272462

Autorenporträt

Prof. Tien-Chien Jen joined the University of Johannesburg in August 2015. He received his PhD in Mechanical and Aerospace Engineering from UCLA, specializing in thermal aspects of grinding. He has received several competitive grants for his research, including those from the US National Science Foundation, the US Department of Energy, and the EPA. Prof. Jen has brought in $3 million of funding for his research and has received various awards for his research including the NSF GOALI Award. Prof. Jen has established a Joint Research Centre with the Nanjing Tech University of China on the "Sustainable Materials and Manufacturing." Prof. Jen is also the Director of the newly established Atomic Layer Deposition Research Centre of the University of Johannesburg. In 2011, Prof. Jen was elected as a Fellow of the American Society of Mechanical Engineers (ASME), which recognized his contributions to the field of thermal science and manufacturing. In 2021, he was inducted into the Academy of Science of South Africa (ASSAf). Prof. Jen has written over 360 peer-reviewed articles, including 180 peer-reviewed journal papers. He has written 16 book chapters and published 5 books.

Dr. Edwell Tafara Mharakurwa is a Lecturer in the Department of Electrical and Electronic Engineering at Dedan Kimathi University of Technology (DeKUT), Kenya, where he has also been the Chair of the Electrical and Electronic Engineering department since 2020. He served as a Teaching Assistant (2011-2012) and Lecturer (2015-2016) in the Department of Mechatronics Engineering at Chinhoyi University of Technology, Zimbabwe. Dr. Mharakurwa has supervised more than 80 undergraduate students and 5 MSc engineering students to completion. He is currently supervising 1 PhD student and 6 MSc engineering students. Dr. Mharakurwa has published several peer-reviewed journals and conference papers in his area of interest not limited to the application of artificial intelligence in process control, fault diagnosis and condition monitoring.

Mr. Steven Otieno Otieno is currently pursuing a Master of Science in Machine Tools Design and Manufacturing Engineering. He is also a Teaching Assistant and has been a Research Assistant in the Department of Mechanical Engineering at Dedan Kimathi University of Technology since 2021. His research area is on advanced manufacturing, specifically interested in plastic processing through injection molding. He has presented his research in two peer-reviewed conferences and has published three articles in peer reviewed journals. His outstanding work on Computer Aided Engineering (CAE) modelling of plastic injection molding has earned the department a CAE modelling software license donation and collaboration from Coretech Inc. He is a registered graduate mechanical engineer with the Engineers Board of Kenya.

Dr. Fredrick Madaraka Mwema is currently a Researcher at Northumbria University, UK. He is also a Senior Lecturer at Dedan Kimathi University of Technology (DeKUT), Kenya (currently on leave). Since 2020, he has served as Chair of the Mechanical Engineering Department and Director of the Centre for Nano Materials and Nanoscience Research Centre at DeKUT. He was a Lecturer (2019-2021), Assistant Lecturer (2015-2019), and Teaching Assistant (2011-2015) of Mechanical Engineering at DeKUT. Dr. Mwema has supervised over 100 undergraduate students and 5 MSc engineering students. He is currently supervising 4 PhD students and 5 MSc engineering students. He has published over 100 articles in peer-reviewed journals, conferences, and books (according to his Scopus profile) in his fields of interest. He has almost 1,000 citations according to his Google Scholar profile. Dr. Mwema has written 4 books and successfully filed two intellectual properties (IPs) with the Kenya Industrial Property Institute (KIPI). He is a registered member of ASME, IAENG, and a graduate engineer of the Engineers Board of Kenya (EBK).

Mr. Job Maveke Wambua is currently a PhD student at Northumbria University, UK working on thin film deposition of nanocomposites. He is also a tutorial fellow at the Dedan Kimathi University of Technology (DeKUT), Kenya (on study leave). He has previously worked on the processing and machining of polymeric and composite materials and has published 8 articles in reputable journals and 3 book chapters. He is currently a graduate member of the Engineers Board of Kenya, the Institution of Engineers of Kenya, and the American Society of Mechanical Engineers.

Inhaltsangabe

1. Injection molding for plastic processing. 2. Computer Aided modelling
for plastic injection molding. 3. Statistical modelling of plastic
injection molding defects. 4. Predictive Modelling of Injection Molding
Defects. 5. State-of-the art of artificial intelligence and prospectives in
modelling of plastic injection molding.