An approach for composing predictive models from disparate knowledge sources in smart manufacturing environments

• Published in: Journal of intelligent manufacturing Vol. 30; no. 4; pp. 1999 - 2012
• Main Author: Kim, Duck Bong
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-04-2019; Springer Nature B.V
• Subjects: Business and Management; Collection; Composition; Control; Decision making; Machines; Manufacturing; Manufacturing industry; Mathematical models; Mechatronics; Multiple criteria decision making; Multiple criterion; Optimization; Prediction models; Process parameters; Processes; Production; Robotics; Data analytics; Additive manufacturing; Decision making; Compositionality; Smart manufacturing
• ISSN: 0956-5515; 1572-8145
• DOI: 10.1007/s10845-017-1366-7

This paper describes an approach that can compose predictive models from disparate knowledge sources in smart manufacturing environments. The capability to compose disparate models of individual manufacturing components with disparate knowledge sources is necessary in manufacturing industry, because this capability enables us to understand, monitor, analyze, optimize, and control the performance of the system made up of those components. It is based on the assumption that the component models and component sources used in any particular composition can be represented using the same collection of system ‘viewpoints’. With this assumption, creating this integrated collection is much easier than it would be. This composition capability provides the foundation for the ability to predict the performance of the system from the performances of its components—called compositionality. Compositionality is the key to solve decision-making/optimization problems related to that system-level prediction. For those problems, compositionality can be achieved using a three-tiered, abstraction architecture. The feasibility of this approach is demonstrated in an example in which a multi-criteria decision making method is used to determine the optimal process parameters in an additive manufacturing process.