Abstract
Determining the optimal processing parameter is routinely performed in the plastic injection moulding industry as it has a direct and dramatic influence on product quality and costs. In this volatile and fiercely competitive market, traditional trial-and-error is no longer sufficient to meet the challenges of globalization. This paper aims to review the research of the practical use of Taguchi method in the optimization of processing parameters for injection moulding. Taguchi method has been employed with great success in experimental designs for problems with multiple parameters due to its practicality and robustness. However, it is realized that there is no single technique that appears to be superior in solving different kinds of problem. Improvements are to be expected by integrating the practical use of the Taguchi method into other optimization approaches to enhance the efficiency of the optimization process. The review will shed light on the standalone Taguchi method and integration of Taguchi method with various approaches including numerical simulation, grey relational analysis (GRA), principal component analysis (PCA), artificial neural network (ANN), and genetic algorithm (GA). All the features, advantages, and connection of the Taguchi-based optimization approaches are discussed.
Highlights
Injection moulding has the highest efficiency, largest yield, and highest dimensional accuracy among all the processing methods
To resolve the multi-output parameter design optimization problem, [60] formulated another type of artificial neural network (ANN) systems which is known as generalized regression neural network (GRNN) based on 16 training data from L16 experimental design to represent a function of three quality characteristics of a moulded part, namely, contour distortions, wear property, and tensile strength
This paper presents a review of research in the optimization of processing parameters for injection moulding
Summary
Injection moulding has the highest efficiency, largest yield, and highest dimensional accuracy among all the processing methods. The complexity of injection moulding process creates a very intense effort to keep the quality characteristics under control. As noted by [4], there are many factors contributing to the occurrence of defects that affect the quality of injection-moulded parts during the production. The material selection, part and mould designs, and the processing parameters interact to determine the quality of plastic product [5]. Inappropriate combination of material selection, part and mould design, and the processing parameters can cause numerous production problems (e.g., product defects, long lead time, much scrap, high production costs, etc.), reduce the competitive price advantage, and decrease the company’s profitability. There are enormous processing parameters to be controlled during injection moulding as illustrated in an Ishikawa cause-effect diagram (Figure 1). The processing parameters involved in injection moulding can be grouped into four basic categories: temperature, pressure, time, and distance [6]
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