Contract grant sponsor: This work is funded by FEDER funds through the COMPETE 2020 Programme and National Funds through FCT - Portuguese Foundation for Science and Technology under the project UID/CTM/50025/2013.

Modeling and optimization of the injection-molding process: a review

Over the years, injection molding has been a premier manufacturing technique in the production of intricate polymer components. Its molding efficiency rests on the shoulders of multiple process and machine parameters, which dictate the final product quality in terms of multiple output responses. It is imperative to state that a precise optimization of various input parameters is paramount for achieving the desired quality indices. In this article, a review of different techniques employed till date for optimizing various injection molding parameters is presented along with their advantages and limitations. It is found in the review that a complete intelligent technique operable without human interference is yet to be developed.

Process parameter optimization of plastic injection molding: a review

Analysis of forging defects for selected industrial die forging processes

This study combined the artificial neural network (ANN) with a genetic algorithm (GA) to establish an inverse model of injection molding for optical lens form accuracy. The Taguchi parameter design was used for screening experiments of the injection molding parameters, and the significant factors influencing lens form accuracy were found to be mold temperature, cooling time, packing pressure, and packing time. These significant factors were used for full factorial experiments, and the experimental data then were used as training and checking data sets for the ANN prediction model. Finally, the ANN prediction model was combined with the GA to construct an inverse model of injection molding. Lens form accuracies of 0.5, 0.7, and $$1\,\upmu \hbox {m}$$1μm were taken as examples for validation, and when the error of the set lens form accuracy target value was within 2 % there were 26, 17, and six sets of the injection molding parameters, respectively, that met the desired form accuracy obtained by using the inverse model. The result indicated that the proposed strategy was successful in identifying process parameters for products with reliable accuracy. In addition, using the GA as a global search algorithm for the optimal solution could further optimize the Taguchi optimal process parameters. The validation experiments revealed that the form accuracy of the lens was improved.

An inverse model for injection molding of optical lens using artificial neural network coupled with genetic algorithm

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.

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

Determination of the initial process parameters for injection moulding is a highly skilled task and is based on a skilled operator's know-how and intuitive sense acquired through long-term experience rather than on a theoretical and analytical approach. In the face of global competition, the current trial-and-error practice is inadequate. In this paper, a hybrid neural network and genetic algorithm approach is described to determine a set of initial process parameters for injection moulding. A hybrid neural network and genetic algorithm system for the determination of initial process parameter settings for injection moulding based on the proposed approach was developed and validated. The preliminary validation test of the system has indicated that the system can determine a set of initial process parameters for injection moulding quickly, from which good quality moulded parts can be produced without relying on experienced moulding personnel.

S.L. Mok

Papers

A hybrid neural network and genetic algorithm approach to the determination of initial process parameters for injection moulding