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Journal of Intelligent Manufacturing

Erschienen in:

01.07.2019

Injection molding manufacturing process: review of case-based reasoning applications

verfasst von: Mohammad Reza Khosravani, Sara Nasiri

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 4/2020

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Abstract

Although manufacturing technology has been developing rapidly, injection molding is still widely used for fabricating plastic parts with complex geometries and precise dimensions. Since the occurrence of faults in injection molding is inevitable, process optimization is desirable. Artificial intelligence (AI) methods are being successfully used for optimization in different branches of science and technology. In this paper, we review the application of one such method, case-based reasoning (CBR), to injection molding. CBR is an AI approach for knowledge representation and manipulation which considers successful solutions of past problems that are likely to serve as candidate solutions for a given problem. This method is being used increasingly in academic and industrial applications. Here, we review CBR systems that are used in injection molding for different purposes, such as process design, processing parameters, fault diagnose, and enhancement of quality control. In addition, we discuss trends for utilization of CBR in different phases of injection molding. The most significant challenges associated with application of CBR to injection molding are also discussed. Finally, the review is concluded by contemplating on some open research areas and future prospects.

Vorheriger Artikel A data-driven decision-making optimization approach for inconsistent lithium-ion cell screening

Nächster Artikel Machine learning technique for data-driven fault detection of nonlinear processes

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Titel: Injection molding manufacturing process: review of case-based reasoning applications
verfasst von: Mohammad Reza Khosravani
Sara Nasiri
Publikationsdatum: 01.07.2019
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 4/2020
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-019-01481-0

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