An improved approach for automatic process plan generation of complex borings

Alain Etienne (1) , Jean-Yves Dantan (1) , Ali Siadat (1) , Patrick Martin (1)

1 LCFC - Laboratoire de Conception Fabrication Commande

The research concerns automated generation of process plans using knowledge formalization and capitalization. Tools allowing designers to deal with issues and specifications of the machining domain are taken into account. The main objective of the current work is to prevent designers from designing solutions that would be expensive and difficult to machine. Among all available solutions to achieve this goal, two are distinguished: the generative approach and the analogy approach. The generative approach is more adapted to generate the machining plans of parts composed of numerous boring operations in interaction. However, generative systems have two major problems: proposed solutions are often too numerous and are only geometrically but not technologically relevant. In order to overcome these drawbacks, two new concepts of feature and three control algorithms are developed. The paper presents the two new features: the Machining Enabled Geometrical Feature (MEGF) and the Machinable Features (MbF). This development is the result of the separation of the geometrical and the technological data contained in one machining feature. The second objective of the paper is to improve the current Process Ascending Generation (PAG) system with control algorithms in order to limit the combinatorial explosion and disable the generation of unusable or not machinable solutions.

Mots clés: Process planning; Knowledge modeling; Machining features; CAPP

Domaines: Génie des procédés

Alain Etienne, Jean-Yves Dantan, Ali Siadat, Patrick Martin. An improved approach for automatic process plan generation of complex borings. Computers in Industry, 2006, 7 (57), pp.663-675. ⟨10.1016/j.compind.2006.03.002⟩. ⟨hal-01022739⟩