Journal of Intelligent Manufacturing

, Volume 24, Issue 4, pp 831–846

Feature-based generation of machining process plans for optimised parts manufacture

Open Access

DOI: 10.1007/s10845-012-0633-x

Cite this article as:
Deja, M. & Siemiatkowski, M.S. J Intell Manuf (2013) 24: 831. doi:10.1007/s10845-012-0633-x


Efficacious integration of such CAx technologies as CAD, CAM and CAPP still remains a problem in engineering practice which constantly attracts research attention. Design by feature model is assumed as a main factor in the integration effort in various engineering and manufacturing domains. It refers principally to feature clustering and consequently operation sequencing in elaborated process plan designs. The focus of this paper is on CAPP for parts manufacture in systems of definite processing capabilities, involving multi-axis machining centres. A methodical approach is proposed to optimally solve for process planning problems, which consists in the identification of process alternatives and sequencing adequate working steps. The approach involves the use of the branch and bound concept from the field of artificial intelligence. A conceptual scheme for generation of alternative process plans in the form of a network is developed. It is based on part design data modelling in terms of machining features. A relevant algorithm is proposed for creating such a network and searching for the optimal process plan solution from the viewpoint of its operational performance, under formulated process constraints. The feasibility of the approach and the algorithm are illustrated by a numerical case with regard to a real application and diverse machine tools with relevant tooling. Generated process alternatives for complex machining with given systems, are studied using models programmed in the environment of Matlab® software.


Process plan selection CAPP Generative methods Optimal design Machining features Dynamic programming 

Copyright information

© The Author(s) 2012

Authors and Affiliations

  1. 1.Department of Manufacturing Engineering and Automation, Mechanical Engineering FacultyGdansk University of TechnologyGdanskPoland

Personalised recommendations