Search

In the past, plastic injection molding (PIM) product quality was usually measured by one single quality characteristic or by multiple quality characteristic with independent parameters one another. In this study, optimization of process parameters using design of experiment (DOE), response surface methodology (RSM), and genetic algorithm (GA) were proposed to generate the optimal process parameters settings of multiple-quality characteristics. In the first stage, significant PIM process parameters can be determined by DOE screening experiments. Then the optimal process parameter settings are obtained via computer aided engineering (CAE) simulation integrated with RSM and GA, which are taken as practically initial settings of process-related parameters. The experimental results show that the propose optimization model is very successful and can be used in industrial applications.

Determining optimal process parameter settings critically influences productivity, quality, and cost of production in the plastic injection molding (PIM) industry. Previously, production engineers used either trial-and-error method or Taguchi's parameter design method to determine optimal process parameter settings for PIM. However, these methods are unsuitable in present PIM because the increasing complexity of product design and the requirement of multi-response quality characteristics. This research presents an approach in a soft computing paradigm for the process parameter optimization of multiple-input multiple-output (MIMO) plastic injection molding process. The proposed approach integrates Taguchi's parameter design method, back-propagation neural networks, genetic algorithms and engineering optimization concepts to optimize the process parameters. The research results indicate that the proposed approach can effectively help engineers determine optimal process parameter settings and achieve competitive advantages of product quality and costs.

This paper presents the development of a parameter optimization system that integrates mold flow analysis, the Taguchi method, analysis of variance (ANOVA), back-propagation neural networks (BPNNs), genetic algorithms (GAs), and the Davidon–Fletcher–Powell (DFP) method to generate optimal process parameter settings for multiple-input single-output plastic injection molding. In the computer-aided engineering simulations, Moldex3D software was employed to determine the preliminary process parameter settings. For process parameter optimization, an L25 orthogonal array experiment was conducted to arrange the number of experimental runs. The injection time, velocity pressure switch position, packing pressure, and injection velocity were employed as process control parameters, with product weight as the target quality. The significant process parameters influencing the product weight and the signal to noise (S/N) ratio were determined using experimental data based on the ANOVA method. Experimental data from the Taguchi method were used to train and test the BPNNs. Then, the BPNN was combined with the DFP method and the GAs to determine the final optimal parameter settings. Three confirmation experiments were performed to verify the effectiveness of the proposed system. Experimental results show that the proposed system not only avoids shortcomings inherent in the commonly used Taguchi method but also produced significant quality and cost advantages.

Microneedles can be used for biomedical applications such as skin prick, blood collection, and drug delivery, etc. Many of the microneedles were fabricated by traditional machining. In this study, a micro lancet needle for blood test application was fabricated using micro injection molding technique, biodegradable polylactic acid (PLA) was used as molding material. The dimension of the microneedle is 623 mum in length, 203 mum in width, and 106 mum in thickness. The mold insert for molding this microneedle was fabricated using LIGA-like techniques. ANSYS CAE software was employed to determine the microneedle's dimensions, and the strength of microneedle was designed to withstand a pressure higher than 3.5 MPa which is necessary for breaking through human skin. Micromolding of the design microneedle was carried out using battenfeld microsystem 50 microinjection molding machine. In order to achieve optimal microneedle quality, Taguchi method was utilized in the experiment. The injection molding parameters including melt temperature, mold temperature, injection speed, and holding speed have been selected as control parameters in the experiment. Experimental results showed that melt temperature and mold temperature have the most significant effects on the strength of the PLA microneedles, and this study also demonstrates that LIGA-like and micromolding techniques can been applied for mass replication of microneedles.

This research integrates Taguchipsilas parameter design method, back-propagation neural networks (BPNN), and Davidon-Fletcher-Powell (DFP) method to optimize the process parameter settings of plastic injection molding. Taguchipsilas parameter design method is used to arrange an orthogonal array experiment and to reduce the number of set-test cycles. Injection time, velocity pressure switch position, packing pressure, and injection velocity are selected as process control parameters and product weight is selected as the single product quality. The experimental data of Taguchipsilas parameter design method are used for effectively training and testing BPNN. Then, BPNN is combined with DFP to search out the final optimal process parameter settings. Finally, a confirmation experiment is performed to confirm the effectiveness of the final optimal process parameter settings. The experimental results show that the proposed effective process parameter optimization approach can avoid shortcomings inherent in the application of trial-and-error processes or the conventional Taguchi parameter design method. Furthermore, the proposed approach can effectively assist engineers in determining optimal initial process parameter settings and achieving competitive advantages on product quality and costs.

In this research, Taguchi method, back-propagation neural networks (BPNN), and genetic algorithms (GA) are applied to the problem of process parameter settings for multiple-input single-output (MISO) plastic injection molding. Taguchi method is adopted to arrange the number of experimental runs. Injection time, velocity pressure switch position, packing pressure, and injection velocity are engaged as process control parameters, and product weight as the target quality. Experimental data from Taguchi method are used to train and test BPNN. Engineering optimization concepts are employed to establish the fitness function for using in GA. Then, BPNN and GA are applied for searching the final optimal parameter settings. Two confirmation experiments are performed to verify the effectiveness of the proposed approach. Experimental results reveal that the proposed approach not only can avoid shortcomings inherent in the commonly used Taguchi method but also can result in significant quality and cost advantages.

Determining optimal initial process parameter settings critically influences productivity, quality, and costs of production in the plastic injection molding (PIM) industry. Up to now, most production engineers have either used trial-and-error or Taguchi's parameter design method to determine initial settings for a number of parameters, including melt temperature, injection pressure, injection velocity, injection time, packing pressure, packing time, cooling temperature, and cooling time. But due to the increasing complexity of product design and multi-response quality characteristics, these multiple input-multiple output (MIMO) methods have some definite shortcomings. This research integrates Taguchi's parameter design methods with back-propagation neural networks, genetic algorithms, and engineering optimization concepts, to optimize the initial process settings of plastic injection molding equipment. The research results indicate that the proposed approach can effectively help engineers determine optimal initial process settings, reduce set-test iterations, and achieve competitive advantages on product quality and costs.