Quality Improvement in Turning Process using Taguchi’s Loss Function
L. Savadamuthu1, S. Muthu2, P. Vivekanandan3
1L. Savadamuthu, Assistant Professor, SNS College of Technology, Affiliated to Anna University Coimbatore, India.
2Dr. S. Muthu, Principal, Srividya College of Engineering and Technology, Affiliated to Anna University Tirunelveli, India.
3P. Vivekanandan, Associate Professor, SNS College of Technology, Affiliated to Anna University Coimbatore, India.
Manuscript received on August 19, 2011. | Revised Manuscript received on August 29, 2011. | Manuscript published on September 05, 2011. | PP: 202-205 | Volume-1 Issue-4, September 2011. | Retrieval Number: D0122081411/2011©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper presents a advanced technique for quality improvement in turning operations. In this study, the Taguchi method is used to find the optimal cutting parameters in turning operations. The orthogonal array, the signal-to-noise ratio, and analysis of variance are employed to study the performance characteristics in turning operations of AISI 1030 steel bars using TiN coated tools. The model was developed initially for unidiameter case and then adapted to other workpiece geometries. An Adaptive Neuro Fuzzy Inference System (ANFIS) is proposed in this paper to control a constant cutting force turning process under various cutting conditions. The ANFIS consists of two parts: predictor and the fuzzy logic controller. The step size of the predictor, and the scaling factors of the fuzzy controller are adjusted for ensuring stability and obtaining optimal control performances. The Taguchi-genetic method is applied in this paper to search for the optimal control parameters of both the predictor and the fuzzy controller such that the ANFIS controller is an optimal controller. Computer simulations are performed to verify the effectiveness of the above optimal fuzzy control scheme designed by the Taguchi-genetic method. Experimental results are provided to illustrate the effectiveness of this approach.
Keywords: Adaptive Neuro Fuzzy Inference System (ANFIS), Taguchi-genetic method, Fuzzy controller.