Semantic Capability Model for the Simulation of Manufacturing Processes

TL;DR

This paper introduces an ontology-based information model that automates the planning of simulation sequences in manufacturing processes, improving efficiency and decision-making in complex production system analyses.

Contribution

It presents a novel, extendable ontology model for representing simulations, their knowledge generation capabilities, and quality criteria, enabling automatic simulation sequence generation.

Findings

The model enhances interoperability and reusability through industrial standards.

Application example demonstrates practical utility in manufacturing.

Supports comprehensive analysis by combining multiple simulations.

Abstract

Simulations offer opportunities in the examination of manufacturing processes. They represent various aspects of the production process and the associated production systems. However, often a single simulation does not suffice to provide a comprehensive understanding of specific process settings. Instead, a combination of different simulations is necessary when the outputs of one simulation serve as the input parameters for another, resulting in a sequence of simulations. Manual planning of simulation sequences is a demanding task that requires careful evaluation of factors like time, cost, and result quality to choose the best simulation scenario for a given inquiry. In this paper, an information model is introduced, which represents simulations, their capabilities to generate certain knowledge, and their respective quality criteria. The information model is designed to provide the foundation for automatically generating simulation sequences. The model is implemented as an extendable and adaptable ontology. It utilizes Ontology Design Patterns based on established industrial standards to enhance interoperability and reusability. To demonstrate the practicality of this information model, an application example is provided. This example serves to illustrate the model's capacity in a real-world context, thereby validating its utility and potential for future applications.