Citation:

Eyituoyo Amorighoye Lucky, Achebo Joseph, Obahiagbon Kessington, Uwoghiren Frank Omos, 2023. Prediction and Optimization of Chip Removal Rate Required to enhance the Weld’s Tool Life using Response Surface Methodology (RSM) and Artificial Neural Network (ANN), ESP Journal of Engineering & Technology Advancements  3(4): 36-44.

Abstract:

The rise in the failure of mechanical components, some of which are attributable to poor weld joints has given rise to research study on the optimization of weld joint strengths. Irrespective of the welding process, the need for the right combination of input process parameters cannot be over emphasized. To achieve a desired weld quality, a weld mechanical property such as the Chip Removal Rate was examined and related to weld input parameters such as depth of cut, cutting speed and feed rate. The Response Surface Methodology (RSM) and Artificial Neural Network (ANN) were used to predict and optimize the Chip Removal Rate of a required to enhance the tool life of some Selected Material. Model adequacy checks, was done using analysis of variance (ANOVA) and found to be adequate. The experimental setup adheres to the central composite design, meticulously constructed using Design expert software (version 13.0). The Response Surface Methodology analysis yields optimal outcomes, depth of cut of 0.400mm, cutting speed of 250m/min and feed rate of 0.5Mm/rev. These parameters collectively yielded a welded joint with chip removal rate of 8.638 Mm/min achieving a desirability value of 0.973. In the Artificial neural network (ANN), 70% of the data was used for training, 15 % was used for validating and the remaining 15% for the actual test. From the results obtained, the RSM in this case had better predicted values. The findings underscore the pivotal role of optimizing non-elastic performance factors in weldment joints. The study showcases performance factors critical for augmenting the strength and structural integrity of machined components.

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Keywords:

Weld Joint, Chip Removal Rate, Response Surface Methodology, Artificial Neural Network.