Knowledge discovery approach for automated process planning

Manufacturing companies in industrialized countries are facing the challenge of achieving shorter times-to-market for their products while also coping with higher and more frequent initial planning efforts for customer specific products. Automated process planning is suited to dissolve this conflict by reducing manual planning efforts and enhancing planning productivity. However, existing computer-aided process planning (CAPP) approaches primarily shift planning efforts towards establishing and updating deterministic rules for planning algorithms manually. This paper shows the potential of using feedback data from Industrie 4.0 production systems as well as design features in a statistical approach to automatically determine initial process sheet information for new products. Feedback data from the manufacturing system is used as a digital representation of the production process. Interdependencies of component characteristics and production processes can be statistically identified via a knowledge discovery in databases (KDD) approach. These interdependencies in turn can be used to automatically deduce rules for CAPP planning algorithms. The presented integrated approach also includes further increasing the level of accuracy and comprehensiveness of the initial process sheet information, as well as updating the planning rules and assumptions following a control loop model. Necessary input and output parameters of the approach are being described, as well as the approach itself, including several steps to systematically incorporate the implications of component characteristic interdependencies on the necessary process steps. Finally, the approach and its potentials are illustrated using a set of real data from a manufacturing company. (C) 2017 The Authors. Published by Elsevier B.V.