An intelligent decision support system for on-demand fixture retrieval, adaptation and manufacture

Abstract

Purpose The purpose of this paper is to propose a decision support system (DSS) that stabilizes the flow of fixtures in manufacturing systems. The proposed DSS assists decision-makers to reuse or adapt the available fixtures or to manufacture new fixtures depending upon the similarity between the past and new cases. It considers the cost effectiveness of the proposed decision when an adaptation decision is passed. Design/methodology/approach The research problem is addressed by integrating case-based reasoning, rule-based reasoning and fuzzy set theory. Cases are represented using an object-oriented (OO) approach to characterize them by their feature vectors. The fuzzy analytic hierarchy process (FAHP) and the inverse of weighted Euclidean distance measure are applied for case retrieval. A machining operation is illustrated as a computational example to demonstrate the applicability of the proposed DSS. Findings The problems of fixture assignment and control have not been well-addressed in the past, although fixture management is one of the complex problems in manufacturing. The proposed DSS is a promising approach to address such kinds of problems using the three components of an artificial intelligence and FAHP. Research limitations/implications Although the DSS is tested in a laboratory environment using a numerical example, it has not been validated in real industrial systems. Practical implications The DSS is proposed in terms of simple rules and equations. This implies that it is not complex for software development and implementation. The illustrated numerical example indicates that the proposed DSS can be implemented in the real-world. Originality/value Demand-driven fixture retrieval and manufacture to assign the right fixtures to planned part-orders using an intelligent DSS is the main contribution. It provides special consideration for the adaptation of the available fixtures in a system.

Integrating fuzzy case-based reasoning and discrete-event simulation to develop a decision support system for part-fixture assignment and fixture flow control

“…A decision-based part-fixture assignment and control to utilize the available fixtures were not well addressed in the past. This research area needs more explorations (Kasie et al, 2017a). In Rahimifard and Newman (1997), a simulation-based multi-flow scheduling system was presented for the simultaneous planning and scheduling of workpieces, fixtures and cutting tools in flexible machining cells.…”

“…Kasie et al (2016b) proposed an intelligent DSS to stabilize the flow of fixtures using CBR, RBR, fuzzy set theory and simple additive weighting methods. In Kasie et al (2017a), an intelligent DSS was presented to perform a decisionbased part/fixture assignment and control by combining CBR, RBR, fuzzy set theory and the AHP. In these two studies, numerical examples were illustrated; however, the systems were tested in neither simulated environments nor industrial systems to validate their soundness.…”

“…An appropriate part-fixture assignment and control strategy is required to reduce operational costs, increase productivity and enhance delivery time. Part-fixture assignment and fixture flow control problems were not adequately researched in the past (Kasie et al, 2017a). The reasons are:…”

“…The limitations of the current decision support system (DSS) in this regard were reviewed in Kasie et al (2017aKasie et al ( , 2017b. This study aims to develop a novel DSS that undertakes a decision-based part-fixture assignment and controls the flow of fixtures using fuzzy case-based reasoning (FCBR) and discrete-event simulation (DES).…”

Intelligent design technology of automobile inspection tool based on 3D MBD model intelligent retrieval

“…Therefore, how to obtain the results of similar 3D digital process equipment quickly in the past has been one of the key technologies to improve product quality and shorten the development cycle. 6 For the automobile main model inspection tool, although a powerful CAD system is applied, a large number of existing tooling design results lack effective characterization methods and reuse methods and are difficult to effectively use. Resulting in a lot of repetitive tooling design work, which seriously affects the design overall efficiency of the process.…”