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- W2282535243 abstract "Surface roughness, tool wear and cutting force are major concerns in today's Computer Numerical Control (CNC) machining industry. Nevertheless, the parameter setting in the process design is usually a non-linear and complicated issue. Based on the full Taguchi experimental combinations of all factors and levels by L9(3^4) orthogonal arrays, the Back-Propagation Neural Network (BPN) is then utilized to predict the objectives of process qualities. The four cutting parameters (cutting depth, feed rate, speed and tool nose runoff) with three levels (low, medium and high) are selected to determined the 3^4=81 sets of full experimental combinations. The ECOCA-PC3807 CNC lathe is utilized to diameter finishing turn the S45C. The surface roughness (Ra), tool wear ratio (μm^(-2)), and cutting force are additionally measured as quality objectives. The BPN is moreover introduced to learn the selected 45 sets of experimental results. The remaining 36 sets of experimental results are furthermore used to verify and construct a criteria predictor of CNC turning. After measuring the objective of each quality, we combined them with BPN by using the experimental results of 45-group orthogonal arrays in BPN learning and verifying them with the remaining 36-group experiments to construct a multi- quality predicator for CNC lathe cutting and confirm the accuracy of BPN. We also consider the criteria predictor as the optimal objective function for genetic algorism. It could find out a group of design parameters for a single quality through genetic algorism. In the four cutting parameters and the weight and bias between three objectives of quality in BPN training and learning, the learning rate was fixed at 1, momentum factor as 0.5. Through a hidden layer and using 4500 times of BPN training, the results found that the multi-quality process predictor could reach 94.16% of prediction accuracy from the global aspect. Through the combination of the predictor and genetic algorism, we may inversely obtain the setting cutting parameters of quality in quality prediction needed by the industry. The results of the research could not only provide operators practical numerical control cutting operation with a set of economic and prospective multi-quality analytic methods and prediction scheme but also establish a reference basis for the cutting parameters that certainly is a positive encouragement for the competitiveness and development of CNC cutting industry." @default.
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- W2282535243 date "2008-12-31" @default.
- W2282535243 modified "2023-09-23" @default.
- W2282535243 title "A Neural-Based GA Parameter Design for Multiple CNC Turning Criteria" @default.
- W2282535243 hasPublicationYear "2008" @default.
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