Conceptual model for semantic representation of industrial manufacturing processes

• Published in: Computers in industry Vol. 61; no. 7; pp. 595 - 612
• Main Authors: Garcia-Crespo, A., Ruiz-Mezcua, B., Lopez-Cuadrado, J.L., Gomez-Berbis, J.M.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier B.V 01.09.2010; Elsevier; Elsevier Sequoia S.A
• Subjects: Applied sciences; Artificial intelligence; Automation; Computer science; control theory; systems; Computer simulation; Computer systems and distributed systems. User interface; Conceptual model; Decision making; Decision support systems; Exact sciences and technology; Human behavior; Information systems. Data bases; Intelligent systems; Knowledge management; Knowledge representation; Manufacturing; Manufacturing engineering; Mathematical models; Memory organisation. Data processing; Ontology; Process representation; Representations; Semantic web; Semantics; Software; Speech and sound recognition and synthesis. Linguistics; Studies; Ontology; Conceptual model; Process representation; Knowledge representation; Validation; Automation; Decision making; Factory; Conceptual analysis; Behavior model; Interoperability; Semantic web; Behavioral analysis; Semantics; Added value; Artificial intelligence; Intelligent system; Production process
• ISSN: 0166-3615; 1872-6194
• DOI: 10.1016/j.compind.2010.01.004

Industrial manufacturing processes representation is a key challenge for leveraging interoperability among business partners. The Semantic representation of information enables the creation of intelligent systems, which can interpret and understand potentially automated tasks, harnessing added-value decision-making processes. Particularly, the Semantic Web can provide a cutting-edge formal representation and knowledge-driven set of technologies to enable automation of industrial manufacturing processes. This paper presents an ontology and a proof-of-concept implementation to describe the automation of decision-making processes which model human behavior, representing the interaction with the overall environment. The model is based on different situations a problem might yield and the correspondent behavioural responses which should be generated. Using the concept of “Situation” as the conceptual corner-stone and building block of descriptions, we discuss how semantics provides a natural knowledge representation strategy, which eases the resource-intensive process of acquiring knowledge. The validation milestones of the system come from a real-world company where the system has been in production mode for a remarkably successful time, a mechanical parts factory.