Meng-Hua Li

Construction of a 3C Product Mold Manufacturing Process Predictive Optimization Model

Mold trials are critical in the mold development process; therefore, it is necessary to develop predictive models that can control processing results and solve problems in processing parameter optimization to ensure manufacturing efficiency and processing quality. Using the six sigma method, this research constructed an optimized 3C (Computer, Communication, and Consumer electronic) product mold manufacturing process predictive model, and conducted an empirical study on the largest electronic products foundry. The Taguchi parameter design method, the back propagation network (BPN) prediction method, and genetic algorithms (GAs) were used to establish an optimization search module. The surface quality was inferred by the network predictive model as a limiting condition for acquiring the maximized material removal rate in milling. The optimized milling processing parameters of the maximum fitness degree can be determined by GA. The findings can serve as a practical reference for quality improvements and decision planning.

An analysis of TFT-LCD industry success factors by integrating FDAHP and gray sequencing

As Taiwan’s TFT-LCD (thin film transistor liquid crystal display) industry plays a vital global role, discussion of its success factors is importance. Previous studies of the TFT-LCD industry success factors were usually based on personal experience and subjective judgment, and did not provide specifically effective success factors assessing methods. Therefore, it is important to establish a set of TFT-LCD industry success factor evaluation methods. Based on literature review and expert interviews, this study selected and summarized 5 major perspectives, and 18 evaluation indices applicable to the TFT-LCD industry. It also established a qualitative (semantics) and quantitative (real data) integrated evaluation model of TFT-LCD industry success factors and their sequencing, using the FDAHP (fuzzy Delphi analytic hierarchy process) and gray sequencing method, in order to provide reference for the TFT-LCD industry. The result showed the top three key success factors which are innovation and R&D (research and development) capabilities, the industry chain support, and manufacturing equipment upgrades. Finally, the top three success factors are employed in sequence to illustrate managerial implications.

Optimization of a light guide plate injection molding stability process by integrating Taguchi quality engineering and the gray sequencing method

The light guide plate is an essential key component in the development of the panel industry. In pursuit of lighter and thinner computer, communication, consumer (3C) electronics products, the panel industry continuously introduces new types of products to meet contemporary needs. Applications of a thinner light guide plate have become increasingly important. However, in the thin injection molding process, the control of geometric size and surface quality may be difficult to achieve owing to variations in conditions, such as injection pressure and temperature. Under the pressure of pursuing higher injection molding speed and lower cost, the injection industry strives to improve injection process capabilities to achieve higher quality yield, reduced production costs and processing time, and enhanced processing quality to maintain profits. Regarding the control of the Y-axis size of injection molding, this study proposed an experimental design method for injection quality improvement and injection process performance enhancement. The relevant injection parameters included temperature, back pressure, holding (dwell) pressure, and length measurement. The purpose of this study is to identify the best light guiding injection parameters to establish stable injection conditions and improve process capability. By employing the five stages of define, measure, analyze, improve, and control, this study conducted empirical research on the injection molding size stability processes of a photoelectric light guide plate, in a specific injection molding plant in Taiwan, in order to establish a process optimization model for injection molding Y-axis size stability. The research methodology integrated the parameter design method of Taguchi quality engineering and the gray sequencing method to identify the combinations of optimization parameters and experimental levels in injection molding. The experimental results suggested that C pk has been improved from 0.6 to 1.44, with a significant increase in process capability. The prediction result error rate meets accuracy requirements, which can help improve the control of capability, stability, and quality of the light guide plate injection molding size process. The research findings can provide Taiwan’s injection molding precision processing industry with a reference in quality.

An analysis of an optical coating process capability prediction model in a Six Sigma procedure by integrating APIBPN and K-means

This study focused on plastic panel products, and treated product penetration rate as a quality characteristic to construct an optical coating process capability prediction model. This study applied Six Sigma to conduct an empirical study on an optical film processing plant.Combining the Taguchi’s parameter design method and the back propagation neural network (BPNN) prediction method, this study used the orthogonal array in experimental design, and employed the Taguchi method to analyse the data obtained from the orthogonal array experiments to study the key factors and their levels, in order to determine the optimal process parameter combinations. The influential process parameters were input into the after ‘apicalis’ in Pachycondyla apicalis optimise back propagation network (APIBPN) and K-means, the outputs of which were the prediction results of the coating film process capability. The results can provide engineers a reference in quality improvement and decision-making planning. The experimental results suggested that the Cpk was improved from 0.89 to 1.63, indicating a significant improvement in process capability value. The prediction accuracy rate is over 97.6%, which is better than that of the trial and error method, and can improve coating film process capabilities and product quality.

Multi-Criteria Decision Analysis Method and Management Information System Reengineering for Solving Vendor-Managed Inventory Bottlenecks in the Notebook Computer Industry

Due to increased uncertainties in corporate demands the vendor-managed inventory method was developed; however, relevant literature indicates that the vendor managed inventory system has bottlenecks in implementation. Thus, this study applied the Fuzzy Delphi Analytic Hierarchy Process for selecting objective attributes, and constructed a vendor managed objective hierarchy structure. Using ABC Classification, category A multi-objective attributes was selected. Dynamic multi-criteria decision analysis was used to analyze category A multi-objective attributes, as selected by different sectors in the first year. If all the objectives have quantitative attributes, multi-criteria programming was used to construct the model. Further, comparisons were conducted for the results obtained from the Non-dominated Sorting Genetic Algorithm, the Particle swarm optimization method, and the modified multi-criteria planning method. Modified Data Envelopment Analysis was used for performance evaluation. ARIS (Architecture of Integrated Information System) analyzed relevant corporate process reengineering, and the decision results were incorporated into the management information system.

An evaluation model for process capability of multiple-process machinery: a case study on the transfer machinery

Abstract The major requirements for products machining are speed, cost and quality, in which quality is the most fundamental demand. Process capability index (PCI) is a powerful tool in measuring the process capability of a product. Since the process capability of machinery can’t be evaluated by directly measuring the machinery, we appraise its process capability by the qualities of the product produced by the machine. Since a machined product is normally tolerance asymmetric and nominal-the-best, we developed a generalized measuring index to represent the process capability of a machine, by which its products can be evaluated for both the process yield and quality loss. First, by considering the relationship between process capability index and process yield, we developed a process capability index of a workstation in a machine tool. Secondly, we integrated the process capability indices of all machining workstations to acquire the process capability index for this machinery. Next, an analytic chart for machining with asymmetric tolerances was developed to help to effectively evaluate the process capability of a machine. Finally, a procedure to perform this evaluation model was proposed, and a case study of a transfer machine is given to show the feasibility of this evaluation model.

Improved process performance in new product development of optical communications manufacturing by the integration of DFSS and ANP

In the trend of increasingly fierce global competition of optical communications industries, design for Six Sigma (DFSS) provides a systematic management approach using tools, training, and measurement methods to achieve products, services, and process designs that meet customer expectations. Taking transnational optical communications original equipment manufacturer (OEM) as the research objects, this study applied IDEF0, analytical network process (ANP), performance matrix analysis, and system simulation to propose a management model, which integrates DFSS with the development processes of new products for improvement and reengineering. The proposed research analysis model can reduce new product development process time by 30.24%. The findings of this research can serve as reference for the introduction of new product development process performance improvements.

Intelligent Manufacturing System of Product Life Cycle Management in Value Engineering

This study first introduced product life cycle management procedures for designing expert questionnaire, and employed the Delphi method to investigate managerial functions of an intelligent manufacturing system in order to demonstrate performance of preliminary research results. Following that, the intelligent manufacturing management system was constructed, and weighted values of management functions were collected. The management function performance was evaluated through value analysis in order to determine whether system functions meet corporate goals. All functions were obtained using the hierarchic analysis method in value engineering analysis. The rational function weights were guaranteed through hierarchy consistency, and compared with the labor costs of operating such system functions to analyze whether the functions were in a suitable domain. After a two-phase investigation, based on expert comments and application functions, production control based on an intelligent manufacturing system can reduce personnel and time costs, thus, effectively increasing corporate competitive power.

Optimal Target Value Process of Fermented Soybean Liquid Polysaccharides from Ganoderma lucidum Using the Taguchi Method

This study developed a lactic-acid beverage of Ganoderma lucidum fermented soybean liquid, and used DMAIC to improve soybean fermentation with Ganoderma lucidum. The Taguchi method was utilized to determine the important factors for improvement. The range of parameters (inoculation amount, dissolved oxygen content, and temperature) greatly affecting the process served as reference for controlled factor levels in the Taguchi method. The Taguchi method analyzed the contribution rates of various control parameters and obtained the optimal control factor combinations, with the highest Polysaccharides target value. The inoculation amount was 6%, dissolved oxygen content was 90%, and temperature was 30°C. By using the same fixed factors, Ganoderma lucidum Polysaccharides was 2.59-4.56 g/kg and 4.46-4.63 g/kg, before and after improvement, respectively. The process can increase Polysaccharides yield from Ganoderma lucidum and stabilize the yield rate.

Using Taguchi Method to Improve 3D Polishing Process Capability for Optical Fiber

This study used Six Sigma DMAIC five-phase improvement procedures for an empirical research on a multinational optical interconnect manufacturer. The Taguchi method was used for the design of experiment, and the data obtained from the orthogonal table were used to determine the key factors and levels, in order to propose the optimal combination of processing parameters and levels for fiber patch cord 3D polishing. The combination of rough polishing time of 30s, polishing pressure of 0.02pa, polishing pad hardness of 65 degrees, and the fine polishing time of 80s could produce the fittest parameters for a satisfactory level, reduce the defect rate and waste of production cost, and reach the Six Sigma standard. The results proved that the improved process capability model, as constructed by this study, can effectively improve the apex offset quality of fiber patch cords. The findings can serve as references for engineers to improve the quality and production process capability.