Determination of Optimal Parameters for SKD11 CNC Turning Process

Tzeng

Int J Adv Manuf Technol

et al. 2011

This study analyzes variations in metal removal rate (MRR) and quality performance of roughness average (R a ) and corner deviation (CD) depending on parameters of wire electrical discharge machining (WEDM) process in relation to the cutting of pure tungsten profiles. A hybrid method including response surface methodology (RSM) and back-propagation neural network (BPNN) integrated simulated annealing algorithm (SAA) were proposed to determine an optimal parameter setting. The results of 18 experimental runs via a Taguchi orthogonal table were utilized to train the BPNN to predict the MRR, R a , and CD properties. Simultaneously, RSM and SAA approaches were individually applied to search for an optimal setting. In addition, analysis of variance was implemented to identify significant factors for the processing parameters. Furthermore, the field-emission scanning electron microscope images show that a lot of built-edge layers were presented on the finishing surface after the WEDM process. Finally, the optimized result of BPNN with integrated SAA was compared with that obtained by an RSM approach. Comparisons of the results of the algorithms and confirmation experiments show that both RSM and BPNN/SAA methods are effective tools for the optimization of parameters in WEDM process.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of wire electrical discharge machining process parameters for cutting tungsten

Tzeng

Int J Adv Manuf Technol

et al. 2011

“…In simultaneous optimization methods, such as mixture designs and factorial designs, the experiments to be conducted have been previously described. ,, The experimental results are collected, and then the optimum condition can be located by constructing a response surface or by retention mapping. One obvious disadvantage of the full factorial designs is that the number of experimental trials required increases geometrically with an increasing number of variables.…”

Section: Resultsmentioning

confidence: 99%

Statistical Optimization of Reaction Parameters for the Synthesis of 2,4,6,8,10,12-Hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane

Bayat

Hajimirsadeghi

Pourmortazavi

2011

Org. Process Res. Dev.

An orthogonal array design (OA 32 ) was employed as a statistical optimization method to enhance the performance of the synthesis of 2,4,6,8,10,4,6,8,10,. The current application of the Taguchi method was successful in optimizing the experimental parameters of HNIW synthesis. High-purity HNIW was synthesized on a relativity large scale by the one-pot method using addition of N 2 O 4 , HNO 3 , and H 2 SO 4 to tetraacetyldibenzylhexaazaisowurtzitane (TADB). The effects of reaction conditions such as the TADB:N 2 O 4 , N 2 O 4 :HNO 3 , and HNO 3 :H 2 SO 4 ratio, the temperature of nitrosation, the temperature of addition of HNO 3 and addition of H 2 SO 4 , the time of nitrosation, and the time of addition of HNO 3 and H 2 SO 4 on the yield of synthesized HNIW were investigated. The effects of these factors on the yield of HNIW were quantitavely evaluated by the analysis of variance (ANOVA). The results showed that the yield of HNIW synthesis can be enhanced significantly by controlling the temperature of addition of H 2 SO 4 and the time of nitrosation. Finally, optimal conditions for synthesis of HNIW by the one-pot method were proposed. The results of ANOVA showed that the optimal TADB:N 2 O 4 , N 2 O 4 :HNO 3 , and HNO 3 : H 2 SO 4 ratios are 6, 1:2, and 1:1, respectively; also, the optimal temperatures for nitrosation, addition of HNO 3 , and addition of H 2 SO 4 are 60, 20, and 60 °C, respectively, and the optimal times for nitrosation, addition of HNO 3 , and addition of H 2 SO 4 are 10, 0.5, and 2.5 h, respectively, for producing HNIW. Finally, under optimal conditions, the yield of the reaction is ∼96%.

“…Kung and Chiang [26] applied the RSM to modeling and analysis of machinability evaluation in the wire electrical discharge machining (WEDM) process of aluminum oxide-based ceramic. Tzeng and Yang [27] implemented the RSM to determine the optimal parameters for SKD11 CNC turning process. Patel et al [28] also performed the RSM to investigate the influence of parametric setting on surface roughness during EDM process for Al 2 O 3 =SiC w = TiC ceramic composites.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Adjustment of Injection Molding Process for Mechanical Characteristics Using the Taguchi Method and Response Surface Methodology

Tzeng

Polymer-Plastics Technology and Engineering

Hsieh

et al. 2011

Self Cite

This study is analyzed variations of mechanical characteristics that depend on the injection molding techniques during the blending of short glass fiber (SGF) and polytetrafluoroethylene (PTFE) reinforced polycarbonate (PC) composites. Planning of experiment is based on a Taguchi orthogonal array table, and applied signal-tonoise ratios to determine an optimal setting. Simultaneously, applying response surface methodology (RSM) analysis, a mathematical predictive model of the tensile strength and flexural strength properties of mechanical characteristics were developed in terms of the injection molding process parameters. In addition, analysis of variance (ANOVA) also was applied to identify the effect of process parameters of SGF and PTFE reinforced PC composites for the tensile strength and flexural strength. Additional runs were conducted in order to validate the optimal setting and compare the performance of Taguchi method and RSM approach.