NEURAL NETWORK BASED MODELLING FOR PREDICTION OF RESPONSE VARIABLES IN MACHINING PROCESSES: A REVIEW

Authors

  • Sumit Kumar M.E Scholar, Department of Mechanical Engineering, NITTTR Chandigarh,
  • Dr. P. Sudhakar Rao Assistant Professor, Department of Mechanical Engineering, NITTTR Chandigarh

Keywords:

Artificial Intelligence, Artificial Neural Network, Turning, Surface Roughness, Fuzzy Logic

Abstract

The omnipresence of artificial intelligence in manufacturing domain draws the inspiration for the present article. ANN, Fuzzy logic, Genetic algorithm and Support Vector Machines are few of the AI techniques mostly used in manufacturing sector. Present article attempts to review the significant work carried out using Artificial Neural Networks for the prediction of various response variables in the manufacturing domain. ANN is widely used for the prediction purposes owing to its capabilities to handle complex problems without going much into mathematical computations and its similarity with the cognitive system of human beings. The ever rising competition in manufacturing industry calls for the least time investment for maintenance procedures so as to increase the productivity and hence a lot of research is going on to predict the response variables for deciding the optimum cutting parameters.

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Published

2019-04-01

How to Cite

Kumar, S. ., & Rao, D. P. S. . (2019). NEURAL NETWORK BASED MODELLING FOR PREDICTION OF RESPONSE VARIABLES IN MACHINING PROCESSES: A REVIEW. International Journal of Technical Innovation in Modern Engineering & Science, 5(4), 52–57. Retrieved from https://ijtimes.com/IJTIMES/index.php/ijtimes/article/view/2732

Issue

Vol. 5 No. 4 (2019): Vol. 5 No. 4: Volume 5 Issue 4 (April-2019)

Section

Articles