Prediction of cutting performance using artificial neural network during buffered impact damper-assisted boring process

In manufacturing industry tool vibration, tool wear and surface finish are the factors that affects the product quality and its production costs. During boring process, overhanging length of the tool holder generates tool vibration leading to poor surface finish, hastened tool life, and further reduction in machine tool life. For enhancing the cutting performance, Buffered Impact Dampers (BID) were designed, developed and tested in this research work. A set of 27-run cutting experiments was performed by varying particle size, material and filling. From the experimental results, Buffered Impact Dampers (BID) increases the rigidity of the tool holder which enhances the cutting performance. The particles in the boring tool will collide with one another thereby suppressing the tool vibration efficiently and enhancing the cutting performance when particle material is stainless steel, particle size phi 4 and particle filling is 75%. Artificial Neural Network (ANN) was implemented to predict the impact of buffered damper on surface roughness, tool wear, tool vibration and cutting force. The results obtained from ANN model were compared with the experimental results for MSE, AAD, MAPE and R. When comparing experimental results with ANN model, both the results concurred with each other.

Automated summary

In the manufacturing industry, tool vibration, tool wear, and surface finish are the crucial factors influencing product quality and production costs. To enhance cutting performance, Buffered Impact Dampers (BID) were designed and tested. Experimental results showed that BID increased the tool holder's rigidity and effectively suppressed tool vibration, leading to improved cutting performance. Artificial Neural Network (ANN) was employed to predict the impact of BID on various parameters, and the comparison between ANN model and experimental results yielded consistent conclusions.