An Ontology-Enabled Case-Based Reasoning Decision Support System for Manufacturing Process Selection

Abstract

In nowadays industry 4.0 and changeable manufacturing context, designers and manufacturing engineers struggle to determine appropriate quick, accurate (with flawless quality), and cost-effective processes to design highly customized products to meet customer requirements. To determine manufacturing processes, the matching between product features, material characteristics, and process capabilities needs to be optimized. Finding such an optimized matching is usually referred to as manufacturing process selection (MPS), which is not an easy task because of the infinite combinations of product features, numerous material characteristics, and various manufacturing processes. Although problems associated with MPS have received considerable attention, semantic web technologies are still underexplored and their potential is still uncovered. Almost no previous study has considered combining case-based reasoning (CBR) with ontologies, a famous and powerful semantic web enabler, to achieve MPS. In this study, we developed a decision support system (DSS) for MPS based on ontology-enabled CBR. By applying automatic reasoning and similarity retrieving on an industrial case study, we show that ontologies enable process selection by determining competitive matching between product features, material characteristics, and process capabilities and by endorsing effective case retrieval.

Finding Misstatement Accounts in Financial Statements through Ontology Reasoning

“…Ontology-based decision support systems have also been used to manufacturing companies. To the best of our knowledge, these studies are mainly devoted to solving the problems of industrial chain resilience [23,24], production system reconfiguration [25,26] and production process selection in manufacturing [27,28,24]. Firstly, to address the uncertainly challenge in manufacturing and supply chain, a rule-based ontology model [23] was constructed to enhance supply chain resilience.…”

AgriEnt: A Knowledge-Based Web Platform for Managing Insect Pests of Field Crops

“…For instance, in [19], the authors presented an ontology-based decision support tool that helps users to select a domestic solar hot water system that meets the user's needs such as installation costs, components and their interrelationships, number of occupants, house location, and daily hot water requirements. In [20], an ontology-enabled decision support system for manufacturing process selection is described. This system helps manufacturing engineers to determine appropriate processes to design products with a competitive matching between features, material characteristics, and process capabilities.…”

An Approach to Building Decision Support Systems Based on an Ontology Service

“…The central part of DSS is an inference system [9][10][11][12][13][14][15][16][17]. An inference system allows inferring results based on information about an analyzed object within a context.…”

“…A FIS can be implemented based on various methods: machine learning [14,16], fuzzy controller or simulator [9][10][11][12][13], knowledge base [6][7][8][15][16][17].…”

“…However, such rules are usually not contextualized, which can produce logical errors and inconsistencies. Knowledge bases require the involvement of an expert and time for development, but they allow describing the analyzed object considering context features [6][7][8][15][16][17]. Moreover, knowledge bases allow controlling the logical consistency of facts.…”