A Review on Machine Learning Models in Injection Molding Machines. (5th January 2022)

• Record Type: Journal Article
• Authors: Selvaraj, Senthil Kumaran; Raj, Aditya; Rishikesh Mahadevan, R.; Chadha, Utkarsh; Paramasivam, Velmurugan
• Other Names: Kara Fuat Academic Editor.
• Is Part Of: Advances in materials science and engineering. Volume 2022(2022)
• Publication Date: 2022-01-05
• Subjects: Materials science -- Periodicals; Materials science; Periodicals; 620.11
• DOI: 10.1155/2022/1949061
• Languages: English
• ISSNs: 1687-8434

Abstract

Abstract : One of the most suitable methods for the mass production of complicated shapes is injection molding due to its superior production rate and quality. The key to producing higher quality products in injection molding is proper injection speed, pressure, and mold design. Conventional methods relying on the operator's expertise and defect detection techniques are ineffective in reducing defects. Hence, there is a need for more close control over these operating parameters using various machine learning techniques. Neural networks have considerable applications in the injection molding process consisting of optimization, prediction, identification, classification, controlling, modeling, and monitoring, particularly in manufacturing. In recent research, many critical issues in applying machine learning and neural network in injection molding in practical have been addressed. Some problems include data division, collection, and preprocessing steps, such as considering the inputs, networks, and outputs, algorithms used, models utilized for testing and training, and performance criteria set during validation and verification. This review briefly explains working on machine learning and artificial neural network and optimizing injection molding in industries.