Journal of Intelligent and Robotic Systems 26: 405–422, 1999. © 1999 Kluwer Academic Publishers. Printed in the Netherlands. 405 Selecting Manufacturing Technology: A Knowledge Modelling Approach Z. ZDRÁHAL Knowledge Media Institute, The Open University, Walton Hall, Milton, UK M. VALÁŠEK and J. ˇ CERMÁK Faculty of Mechanical Engineering, Czech Technical University, Keznes, Czech Republic (Received: 15 March 1999; accepted in final form: 10 June 1999) Abstract. This paper describes the development of a knowledge based system for selecting the technology for manufacturing machine parts. The problem is formally specified and analysed. The problem solving method is modelled by instantiating two Generic Task Models (GTM) in the domain of bulk metal forming. Both technological and economical criteria are considered. Static knowledge is represented as domain ontologies. The system has been implemented and experimentally tested. Key words: generic task model, heuristic classification, knowledge modelling, manufacturing tech- nology, ontology, shape class, systematic refinement. 1. Introduction This paper describes a knowledge modelling approach to the development of a sys- tem for selecting the cost-optimal technology needed for manufacturing machine parts. Based on a formal analysis the problem is defined as a functional mapping from the product description to a set of applicable technologies. The solution is based on the idea of factorising the mapping function into a sequence of simple steps, which can be described as problem solving tasks with well-understood, domain-dependent meaning. The tasks correspond to elementary decisions made by the technologist when solving the problem. Following the philosophy of Generic Task Models (GTM) (Tansley and Hayball, 1993) the most appropriate models for representing these tasks are selected and refined in the context of the technology se- lection problem. Since the solution requires complex inferences, the overall model of expertise has been composed of two component models created by instantiating two different GTMs. Generic models proved to be a useful instrument both for con- structing the overall problem solving model and for driving knowledge acquisition. Domain specific knowledge has been organised into reusable ontologies. Both the models of expertise (i.e., GTMs) and the domain knowledge are represented in the Operational Conceptual Modelling Language.