Yung-Kuang Yang

Optimization of wire electrical discharge machining for pure tungsten using a neural network integrated simulated annealing approach

This study analyzed variation of cutting velocity and workpiece surface finish depending on wire electrical discharge machining (WEDM) process parameters during manufacture of pure tungsten profiles. A method integrating back-propagation neural network (BPNN) and simulated annealing algorithm (SAA) is proposed to determine an optimal parameter setting of the WEDM process. The specimens are prepared under different WEDM process conditions based on a Taguchi orthogonal array table. The results of 18 experimental runs were utilized to train the BPNN predicting the cutting velocity, roughness average (Ra), and roughness maximum (Rt) properties at various WEDM process conditions and then the SAA approaches was applied to search for an optimal setting. In addition, the analysis of variance (ANOVA) was implemented to identify significant factors for the WEDM process and the proposed algorithm was also compared with respect to the confirmation experiments. The results of proposed algorithm and confirmation experiments are show that the BPNN/SAA method is effective tool for the optimization of WEDM process parameters.

Optimization of Dry Machining Parameters for High-Purity Graphite in End-Milling Process

The machining factors affecting the tool wear and surface finish produced in the end milling process are generally the cutting speed, the feed rate, the depth of cut, etc. This paper describes a study that identifies the influence of the machining parameters on the groove width and the surface roughness average for the end-milling of high-purity graphite under dry machining conditions. The experiments are based on an orthogonal arrays and grey relational analysis method is then applied to determine an optimal machining parameter setting. The dimensional accuracy of the groove width and the surface roughness average are selected as the quality targets. In this study, the feed rate is the most significant controlled factors for the machining process according to the weighted sum grade of the Δ and the R a .

Experimental analysis and optimization of a photo resist coating process for photolithography in wafer fabrication

This investigation is applied the Taguchi method and combination the analysis of variance (ANOVA) to the photo resist (PR) coating process for photolithography in wafer manufacturing. Plans of experiments via nine experimental runs are based on the orthogonal arrays. In this study, the thickness mean and the uniformity of thickness of the PR are adopted as the quality targets of the PR coating process. This partial factorial design of the Taguchi method provides an economical and systematic method for determining the applicable process parameters. Furthermore, the ANOVA prediction of the thickness mean and the uniformity of thickness for the PR has been applied in terms of the PR temperature, chamber humidity, spinning rate, and dispensation rate by means of the designs of experiments (DOE) method. The PR temperature and the chamber humidity are found to be the most significant factors in both the thickness mean and the uniformity of thickness for a PR coating process. Finally, the sensitivity study of optimum process parameters was also discussed.

Modeling and Optimization of Injection Molding Process Parameters for Thin-Shell Plastic Parts

This paper deals with the application response surface methodology (RSM) integrating with statistical technique to discuss variation of warpage and tensile stress properties depended on injection molding parameters during production of thin-shell plastic components. By applying RSM analysis, a mathematical predictive model of the warpage and tensile stress properties was developed in terms of the injection molding parameters. The trim operation has been optimized for a given injection molding condition by desirability function approach and the response surface contours were constructed for determining the optimum conditions. Additionally, the analysis of variance is also applied to identify the most significant factor.

Optimization of Injection-Molding Process for Mechanical and Tribological Properties of Short Glass Fiber and Polytetrafluoroethylene Reinforced Polycarbonate Composites with Grey Relational Analysis: A Case Study

This study analyzes the mechanical properties and tribological behaviors of polycarbonate (PC) reinforced with 20% short glass fiber (SGF) and 6% polytetrafluoroethylene (PTFE), which is applied to the bottom cover of the card reader body. The specimens were prepared under different injection-molding conditions, by varying the filling time, the melt temperature, the mold temperature and the packing pressure. Grey relational analysis is then applied to obtain an optimal parameter setting. Plans of experiments via nine experimental runs are based on the orthogonal arrays to determine the optimum factor level condition. The mechanical properties of ultimate stress, and the tribological behaviors of surface roughness and friction coefficient variation are adopted as the quality targets. The tensile test were performed with a 25 kN computerized MTS. Simultaneously, friction and wear tests were performed with a Schwingum Reibung Verschleiss (SRV oscillation friction wear) ball-on-plane tester. Additionally, the worn surfaces were examined with scanning electron microscopy (SEM). Melt temperature was found to be the most influenced factor inultimate stress, surface roughness, and friction coefficient for the injection molding process in this study.

Determination of Optimal Parameters for SKD11 CNC Turning Process

This study investigated the optimization of a CNC turning process for SKD11 (JIS). The design of experiments (DOE) method with an orthogonal array was applied. Nine experimental runs were performed based on the orthogonal array. The surface properties of roughness average and roughness maximum and the roundness were studied; the analysis of variance (ANOVA) was adapted to investigate which parameters had the most influence on the CNC turning process for SKD11 (JIS). Also, models for predictions of the roughness average, roughness maximum, and roundness had been developed in terms of cutting speed, feed rate, depth of cut, and the mixture ratio of cutting fluids.

Optimization of Injection Molding Process for Tensile and Wear Properties of Polypropylene Components via Taguchi and Design of Experiments Method

This study analyzes the wear and the tensile properties of polypropylene (PP) components, which are applied to the interior coffer of automobiles. The specimens are prepared under different injection molding conditions by changing the melting temperature, the injection speed, and the injection pressure via three computer-controlled progressive strokes. The wear and tensile properties are adopted as the quality targets. Experiments of 16 experimental runs are based on an orthogonal array table, and apply the Taguchi method and the design of experiments (DOE) approach to determine an optimal parameter setting. In addition, a side-by-side comparison of two different approaches is provided. In this study, regression models that link the controlled parameters and the targeted outputs are developed, and the identified models can be used to predict the tensile and wear properties at various injection molding conditions.

Optimization of Injection-Molding Process of Short Glass Fiber and Polytetrafluoroethylene Reinforced Polycarbonate Composites via Design of Experiments Method: A Case Study

This study analyses the tribological behaviors and mechanical properties of polycarbonate (PC) reinforced with 20% short glass fiber (SGF) and 6% polytetrafluoroethylene (PTFE), which is applied to the bottom cover of the card reader body. In order to reduce the number of experiments, the design of experiments (DOE) method based on Taguchis orthogonal arrays were applied. The specimens were prepared under different injection molding conditions by varying four controlled factors including the filling time, the melt temperature, the mold temperature and the packing pressure with three levels for each factor. The wear mass loss, the friction coefficient, and the strain are adopted as the quality targets. In addition, an optimal parameter setting is obtained through a desirability function that balances three desired quality targets. Furthermore, analysis of variance (ANOVA) is conducted to identify the factors that have significant influence on the quality of the products. The proposed approach offered an effective and systematic way to identify an optimal setting of the injection molding process.

A Study of Taguchi and Design of Experiments Method in Injection Molding Process for Polypropylene Components

This study analyzes contour distortions, wear mass losses and tensile properties of polypropylene (PP) composite components applied to the interior coffer of automobiles. The specimens are prepared under different injection molding conditions by changing melting temperatures, injection speeds, and injection pressures via three computer-controlled progressive strokes. The contour distortions, wear and tensile properties are selected as quality targets. The arrangement of sixteen experiments is based on an orthogonal array table. Both the Taguchi method and the design of experiments (DOE) method are applied to determine an optimal parameter setting. In addition, a side-by-side comparison of two different approaches is provided. In this study, regression models that link the controlled parameters and the targeted outputs are developed, and the mathematic models can be utilized to predict the contour distortions, wear and tensile properties at various injection molding conditions.

Optimization of a photo resists coating process for photolithography in wafer manufacturing via design of experiments method

Purpose – The investigation uses design of experiments (DOE) approach to determine the optimal parameters of photo resist (PR) coating process for photolithography in wafer manufacturing.Design/methodology/approach – Plans of experiments via nine experimental runs are based on the orthogonal arrays to determine the optimum factor condition. In this study, the mean thickness and the uniformity of thickness of the PR are adopted as the quality targets of the PR coating process. This partial factorial design of the DOE method provides an economical and systematic method of determining the optimal process parameter.Findings – A model for the mathematical prediction of the mean thickness and the uniformity of thickness for the PR has been developed in terms of the PR temperature, the chamber humidity, the spinning rate, and the dispensation rate by means of the DOE method. The PR temperature and the chamber humidity are found to be the most significant factors in both the mean thickness and the uniformity of t...