Artificial Neural Network Based Machining Operation Selection for Prismatic Components

K.K. Natarajan, J Gokulachandran


Computer-aided process planning systems are used to assist human planners in producing better process plans. New artificial intelligence techniques play a significant role in CAPP. CAPP research includes neural network approaches, knowledge-based techniques, Petri nets, agent-based, fuzzy set theory, genetic algorithm, Standard for the Exchange of Product model data (STEP)-Compliant CAPP, and Internet-based techniques. This study deals with the application of the Artificial Neural Network techniques (ANN) in CAPP because of their learning ability and massive potential toward dynamic planning.  This study focuses on the usage of artificial neural networks machining operation selection and sequences of operations for prismatic components. The intelligent CAPP system suggests the best machining operation and its sequences for the prismatic components using tolerances, material requirements, and surface finish details. The process planning of machining features in part is the starting point. An enormous amount of knowledge is required for part feature process planning, like selecting proper material, size, stock, dimensional tolerance, and surface finish. In this work, various prismatic features, such as a hole, slot, pocket, boss, chamfer, fillet, and face are taken and details like material, size, stock, dimensional tolerance and surface finish are properly normalized and given as input to neural networks to find the required sequence of machining operation. LevenbergMarquidt algorithm was used to train the networks and was found very effective in operation sequence selection. A sample prismatic component with nine features have been analyzed and found to be more productive. Levenberg Marquidt  algorithm is then compared with the conjugant space algorithm, and it is found that the former produces less error in outputs compared to them later.


computer-aided process planning; artificial neural networks; machining operation sequencing; prismatic parts.

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D.Barschdorff and L. Monostori, “Neural networks-Their applications and perspectives in intelligent machiningâ€, Computers in Industry, Vol.17,pp.101-119 ,1991.

V. Sunil and S. Pande, “Automatic recognition of machining features using artificial neural networksâ€, The International Journal of Advanced ManufacturingTechnology, Vol.41, pp.932-947,2009.

R. BenKhalifa, N. B. Yahia and A. Zghal, “Integrated neural networks approach in cad/cam environment for automated machine tools selectionâ€, Journal of Mechanical Engineering Research, Vol 2, pp. 25-38, 2010.

Q. Feng, “A novel model of feature recognition based on rbf neural networksâ€,in: Proceedings of the 10th WSEAS International conference on Computers,World Scientifc and Engineering Academy and Society (WSEAS), pp. 109-113,2006.

J. Balic, “Neural-network-based numerical control for milling machineâ€Journal of Intelligent and Robotic Systems ,Vol.40 ,pp.343-358, 2004.

D.Zhou.D and X. Dai, “Combining granular computing and RBF neural network for process planning of part featuresâ€,International Journal of Advanced Manufacturing Technology,pp.1447-1462,2015.

L.Ding,Y. Yue, K. Ahmet, M. Jackson, and R.Parkin, “Global optimization of a feature-based sequence using GA and ANN techniquesâ€, International Journal of Production Research,Vol 15, pp. 3247-327 ,2005.

S. Deb, J. R. Parra-Castillo and K. Ghosh, “An integrated and intelligent computer-aided process planning methodology for machined rotationally symmetrical partsâ€, International Journal of Advanced Manufacturing Systems,Vol 13,pp.1-26,2011.

S.M.Amaitik and S.EnginKilic, “ An intelligent process planning system for prismatic parts using STEP featuresâ€, Journal of Intelligent Manufacture, pp.978-993,2007.

S.Deb and K.Ghosh, “An integrated and Intelligent Computer –Aided Process Planning Methodology for Machined Rotationally Symmetrical Partsâ€, International Journal of Advanced Manufacturing Technology ,2011.

Ouyang Hua-bing, “A STEP-compliant Intelligent Process Planning System for Millingâ€, The Open Automation and Control Systems Journal, Vol 7, pp.510-520,2015.

J.Gokulchandran and K. Mohandas, “Predicting remaining useful life of cutting tools with regression and ANN analysisâ€, International Journal of Productivity and Quality Management, Vol.9, pp.502-518, 2012.

J.Gokulachandran and R.Padmanaban, “Prediction of remaining useful life of cutting tools: a comparative study using soft computing methodsâ€, International Journal of Process Management and Benchmarking, Vol.8.pp.156-181, 2018.

S.Ilangovan,R.VairaVignesh,R.Padmanaban and J.Gokulachandran, “Comparison of statistical and soft computing models for predicting hardness and wear rate of Cu-Ni-Sn Alloyâ€,Advances in Intelligent Systems and Computing, pp. 559-571, 2018.

Izabela Rojek , “Technological process planning by the use of neural networksâ€, Artificial Intelligence for Engineering Design, Analysis and Manufacturing,Vol31,pp.1-15,2015

M.Amaitik, “Neural Network Approach to cutting tools selectionâ€, The International Journal of Engineering and information Technology, Vol3, 2017

R Vaira Vignesh and R Padmanaban, “Modelling Corrosion Behavior of Friction Stir Processed Aluminium Alloy 5083 Using Polynomial: Radial Basis Functionâ€, Transactions of the Indian Institute of Metals, Vol 10.2017.

S.Wang ,X.Lu,X.Li, and W.D. Li “A systematic approach of process planning and scheduling optimization for sustainable machiningâ€, Journal of Cleaner Production.pp.914-929,2015.

N. M. Nawi, A. S. Hussein, N. A. Samsudin, N. A. Hamid, M. A. Mohd Yunus, and M. F. Ab Aziz, “The Effect of Pre-Processing Techniques and Optimal Parameters selection on Back Propagation Neural Networks,†International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 3, p. 770, Jun. 2017.

Nazri Mohd Nawi, Faridah Hamzah, Norhamreeza Abdul Hamid, Muhammad Zubair Rehman, Mohammad Aamir and Azizul Azhar Ramli, “An Optimized Back Propagation Learning Algorithm with Adaptive Learning Rateâ€, International Journal on Advanced Science Engineering Information technology, Vol 7 No.5, pp1693-1700,2017.

S. B. Sahare, S. P. Untawale, S. S. Chaudhari, R. L. Shrivastava, and P. D. Kamble, “Optimization of end milling process for Al2024-T4 aluminum by combined Taguchi and artificial neural network process,†in Soft Computing: Theories and Applications, Springer, 2018, pp. 525–535.

A. T. Abbas, D. Y. Pimenov, I. N. Erdakov, T. Mikolajczyk, M. S. Soliman, and M. M. El Rayes, “Optimization of cutting conditions using artificial neural networks and the Edgeworth-Pareto method for CNC face-milling operations on high-strength grade-H steel,†Int. J. Adv. Manuf. Technol., vol. 105, no. 5, pp. 2151–2165, 2019.

N. Gupta, A. K. Agrawal, and R. S. Walia, “Soft Modeling Approach in Predicting Surface Roughness, Temperature, Cutting Forces in Hard Turning Process Using Artificial Neural Network: An Empirical Study,†in International Conference on Information, Communication and Computing Technology, 2019, pp. 206–215.

J. G. Bralla, Design for manufacturability handbook. McGraw-Hill, 1999.



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