A Survey of Automated Process Planning Approaches in Machining

  • S.K. Singh
  • A.M. Mohanty
Keywords: Computer Aided Process Planning, Computer Integrated Manufacturing, Smart Manufacturing


Global industrial trend is shifting towards next industrial revolution Industry 4.0. It is becoming increasingly important for modern manufacturing industries to develop a Computer Integrated Manufacturing (CIM) system by integrating the various operational and information processing functions in design and manufacturing. In spite of being active in research for almost four decades, it is clear that new functionalities are needed to integrate and realize a completely optimal process planning which can be fully compliant towards Smart Factory. In order to develop a CIM system, Computer Aided Process Planning (CAPP) plays a key role and therefore it has been the focus of many researchers. In order to gain insight into the current state-of-the-art of CAPP methodologies, 96 research papers have been reviewed. Subsequent sections discuss the different CAPP approaches adopted by researchers to automate different process planning tasks. This paper aims at addressing the key approaches involved and future directions towards Smart Manufacturing.


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Dipper, T., Xu, X., & Klemm, P. (2011). Defining, recognizing and representing feature interactions in a feature-based data model. Robotics and Computer-Integrated Manufacturing, 27(1), 101-114.

Li, X., Gao, L., & Li, W. (2012). Application of game theory based hybrid algorithm for multi-objective integrated process planning and scheduling. Expert Systems with Applications, 39(1), 288-297.

Zheng, Y., Taib, J. M., & Tap, M. M. (2012). Decomposition of interacting machining features based on the reasoning on the design features. The International Journal of Advanced Manufacturing Technology, 58(1-4), 359-377.

Azmi, A. I. & Muhammad, Z. (2003). Feature Extraction from STEP AP224 file sets. Jurnal Mekanikal, (16), 31-46.

Arivazhagan, A., Mehta, N. K., & Jain, P. K. (2008). Development of a feature recognition module for tapered and curved base features. The International Journal of Advanced Manufacturing Technology, 39(3-4), 319-332.

Sunil, V. B. & Pande, S. S. (2009). Automatic recognition of machining features using artificial neural networks. The International Journal of Advanced Manufacturing Technology, 41(9-10), 932-947.

Arivazhagan, A., Mehta, N. K., & Jain, P. K. (2009). A STEP AP 203–214-based machinable volume identifier for identifying the finish-cut machinable volumes from rough-machined parts. The International Journal of Advanced Manufacturing Technology, 42(9-10), 850-872.

Bok, A. Y., & Mansor, M. S. A. (2013). Generative regular-freeform surface recognition for generating material removal volume from stock model. Computers & Industrial Engineering, 64(1), 162-178.

Gologlu, C. (2004). A constraint-based operation sequencing for a knowledge-based process planning. Journal of intelligent manufacturing, 15(4), 463-470.

Phing, G. Y. W., Taib, J. M., & Tap, M. M. (2012). Efficient vertex classification method to recognise the orthogonal and non-orthogonal prismatic. Jurnal Mekanikal, 34, 83-94.

Sivakumar, S. & Dhanalakshmi, V. (2012). An approach towards the integration of CAD/CAM/CAI through STEP file using feature extraction for cylindrical parts. International Journal of Computer Integrated Manufacturing, [ahead-of-print], 1-10.

Zhang, Y., Bai, X. L., Xu, X., & Liu, Y. X. (2012). STEP-NC based high-level machining simulations integrated with CAD/CAPP/CAM. International Journal of Automation and Computing, 9(5), 506-517.

How to Cite
S.K. Singh, & A.M. Mohanty. (2018). A Survey of Automated Process Planning Approaches in Machining. International Journal of Engineering and Management Research, 8(6), 81-88. https://doi.org/10.31033/ijemr.8.6.02