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Thus, current approaches for plastic part design and manufacturing focus primarily on establishing the minimum part thickness to reduce material usage.
The demand for small and thin plastic components for miniaturization assembly has considerably increased in recent years.
Other factors besides minimal material usage may also become important when manufacturing thin plastic components.
Minimizing manufacturing costs for thin injection
moldFra Baidu bibliotekd plastic components
1. Introduction
In most industrial applications, the manufacturing cost of a plastic part is mainly governed by the amount of material used in the molding process.
The assumption is that designing the mold and molding processes to the minimum thickness requirement should lead to the minimum manufacturing cost.
Nowadays, electronic products such as mobile phones and medical devices are becoming ever more complex and their sizes are continually being reduced.
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sink mark [11] and the part deformation after molding [12], analyzing the effects of wall thickness and the flow length of the part [13], and analyzing the internal structure of the plastic part design and filling materials flows of the mold design [14]. Reifschneider [15] has compared three types of mold filling simulation programs, including Part Adviser, Fusion, and Insight, with actual experimental testing. All these approaches have established methods that can save a lot of time and cost. However, they just tackled the design parameters of the plastic part and mold individually during the design stage. In addition, they did not provide the design parameters with minimum manufacturing cost. Studies applying various artificial intelligence methods and techniques have been found that mainly focus on optimization analysis of injection molding parameters [16,17]. For in-stance He et al. [3] introduced a fuzzy- neuro approach for automatic resetting of molding process parameters. By contrast , Helps et al. [18,19] adopted artificial neural networks to predict the setting of molding conditions and plastic part quality control in molding. Clearly, the development of comprehensive molding process models and computer-aided manufacturing provides a basis for realizing molding parameter optimization [3 , 16,17]. Mok et al. [20] propose a hybrid neural network and genetic algorithm approach incorporating Case-Based Reasoning (CBR) to derive initial settings for molding parameters for parts with similar design features quickly and with acceptable accuracy. Mok’s approach was based on past product processing data, and was limited to designs that are similar to previous product data. However, no real R&D effort has been found that considers minimizing manufacturing costs for thin plastic components. Generally, the current practical approach for minimizing the manufacturing cost of plastic components is to minimize the thickness and the dimensions of the part at the product design stage, and then to calculate the costs of the mold design and molding process for the part accordingly, as shown in Fig. 1. The current approach may not be able to obtain the real minimum manufacturing cost when handling thin plastic components. 1.2Manufacturing requirements for a typical thin plastic component As a test example, the typical manufacturing requirements for a thin square plastic part with a center hole, as shown in Fig. 2, are given in Table 1.
In particular, for thin parts, the injection molding pressure may become significant and has to be considered in the first phase of manufacturing.
Employing current design approaches for plastic parts will fail to produce the true minimum manufacturing cost in these cases.
Thus, tackling thin plastic parts requires a new approach, alongside existing mold design principles and molding techniques.
1.1 Current research
Today, computer-aided simulation software is essential for the design of plastic parts and molds. Such software increases the efficiency of the design process by reducing the design cost and lead time [1]. Major systems, such as Mold Flow and C-Flow, use finite element analysis to simulate the filling phenomena, including flow patterns and filling sequences. Thus, the molding conditions can be predicted and validated, so that early design modifications can be achieved. Although available software is capable of analyzing the flow conditions, and the stress and the temperature distribution conditions of the component under various molding scenarios, they do not yield design parameters with minimum manufacturing cost [2,3]. The output data of the software only give parameter value ranges for reference and leaves the decision making to the component designer. Several attempts have also been made to optimize the parameters in feeding [4–7], cooling [2,8,9], and ejection These attempts were based on maximizing the flow ability of molten material during the molding process by using empirical relation ships between the product and mold design parameters. Some researchers have made efforts to improve plastic part quality by Reducing the