K. S. Jai Aultrin ¹, M. Dev Anand ¹

Affiliations
1 Department of Mechanical Engineering, Noorul Islam Centre for Higher Education, Kumaracoil – 629180, Thuckalay, Kanyakumari District, Tamil Nadu, IN

Abstract

Background/Objectives: Last decades have witnessed a rapid growth in the development of harder, difficult and complexity to machine metals and alloys. AWJM is one of the most freshly built up nontraditional machining processes in processing various types of hard-to-cut materials nowadays. It is an economical method for heat sensitive materials that cannot be machined by processes that produce heat while machining. Machining parameters play a lead role in determining the economics of machine and machining quality. This paper investigates the prediction of MRR and Surface roughness on Lead Tin Alloy using the Artificial Neural Network (ANN). Methods/Statistical analysis: In this work, the influence of five AWJM parameters of the process on SR and MRR of an American element referred as Lead Tin Alloy which is machined by AWJM was experimentally performed and analyzed. According to RSM design, different experiments have been performed with the combination of input parameters on this American element. Findings: Outcome depicts the minimum error attained for data belonging to test is 1.063814%% for MRR and 0.208967018% for SR. Also the maximum error obtained is about 9.475104% for MRR and 9.070886429% for SR. By training the network deviations may occur but error is reduced because this technique is heuristic.

Keywords

Artificial Neural Network, Material Removal Rate, Response Surface Methodology, Surface Roughness

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K. S. Jai Aultrin ¹, M. Dev Anand ²

Affiliations
1 Noorul Islam Centre for Higher Education, Kumaracoil - 629180, Thuckalay, Tamil Nadu, IN
2 Department of Mechanical Engineering, Noorul Islam Centre for Higher Education, Kumaracoil - 629180, Thuckalay, Tamil Nadu, IN

Abstract

Background/Objectives: Last decades have witnessed a rapid growth in the development of harder, difficult and complexity to machine metals and alloys. Abrasive Water Jet Machining (AWJM) is one of the recently developed nontraditional mechanical type hybrid machining processes in processing various kinds of hard-to-cut materials. It is an economical method for heat sensitive materials that cannot be machined by processes that produce heat while machining. Machining parameters play the lead role in determining the machine economics and quality of machining. This paper investigates the prediction of MRR and SR on Aluminum, Copper and Lead alloys using the combination of Artificial Neural Network (ANN) and Fuzzy Logic (FL). Methods/Statistical Analysis: In this study, the consequence of different AWJM process parameters on Material Removal Rate (MRR) and Surface Roughness (SR) of three nonferrous alloys namely Aluminum, Copper and Lead which are machined by AWJM was experimentally performed and analyzed. According to Response Surface Methodology (RSM) design, different experiments were conducted with the combination of input parameters on these alloys. Findings: A combinational method called as Adaptive Neuro Fuzzy Inference System provides effective knowledge based training to process parameters, to make its enhancement of process performance.

Keywords

Abrasive Water Jet Machining, Adaptive Neuro Fuzzy Inference System, Material Removal Rate, Response Surface Methodology, Surface Roughness

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