Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157590
Title: Process optimization for manufacturing process using machine learning approach
Authors: Wu, Jiaze
Keywords: Engineering::Manufacturing::Quality control
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Wu, J. (2022). Process optimization for manufacturing process using machine learning approach. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157590
Abstract: In manufacturing processes, sensors are often implemented to collect data and achieve defect detection. To establish correlation between collected data and quality measurements, machine learning methods are widely used. However, when a new process is developed, it often require sufficient data instances for training to build a model with high accuracy, which costs time and resources. In this paper, transfer learning methods including TrAdaboost and Joint Domain Adaption (JDA) are used to establish correlation for a new manufacturing process. Data collected from previous processes can be utilized in transfer learning and therefore could save time and resources in data collection. Also, process optimization will be achieved through the application of optimization algorithms. Traditional optimization method for manufacturing like Design of Experiment (DOE) would require the experiment to run multiple times to determine the optimal parameters. By using optimization algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE), we are able to time and resources and achieve in-process monitoring for process optimization.
URI: https://hdl.handle.net/10356/157590
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
FYP_Wu_Jiaze_Final_Report.pdf
  Restricted Access
1.16 MBAdobe PDFView/Open

Page view(s)

28
Updated on Dec 5, 2022

Download(s)

3
Updated on Dec 5, 2022

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.