Knowledge-based program generation approach for robotic manufacturing systems

In recent decades, robotic manufacturing systems have been considered as effective solutions for providing more productive manufacturing processes, but with less cost and risk. However, the programming for robotic manufacturing systems is a time-consuming task, and hinders the implementation of robotic manufacturing systems in today's industry. This paper proposes a knowledge-based program-generation approach for robotic manufacturing systems. The proposed approach provides effective support for the standardization of the rules and knowledge related to manufacturing programs that have proven successful in previous manufacturing cases; this can not only increase the programming efficiency, but can also improve the manufacturing stability and production quality. First, an ontological knowledge model is developed to provide an explicit semantic description of the relevant concepts for the robotic manufacturing system, basic instruction units for the program, and product models of the workpieces. Second, a rule-based reasoning mechanism is established to infer the implicit relationships between the basic instruction units of the manufacturing program. Finally, based on the semantic descriptions and reasoning mechanism of the proposed knowledge model, the basic instruction units of the manufacturing program are instantiated based on data extracted from the product models and integrated according to the relationships inferred by the reasoning mechanism, thereby generating the robotic manufacturing program.

Automated summary

This study proposes a knowledge-based program-generation approach for robotic manufacturing systems to improve programming efficiency and enhance manufacturing stability and production quality by standardizing manufacturing program rules and knowledge.