Javier Munguia, Alain Bernard and Merve Erdal
The purpose of this paper is to propose and evaluate a novel tool for the assessment and selection of rapid prototyping (RP)/manufacturing (RM) systems as alternative processes…
Abstract
Purpose
The purpose of this paper is to propose and evaluate a novel tool for the assessment and selection of rapid prototyping (RP)/manufacturing (RM) systems as alternative processes for low‐volume production in the machinery and equipment design sector. By analysing previous RP/RM selectors, this research addresses the necessary factors that a knowledge‐based engineering (KBE) system must include for the analysis, comparison and ranking of candidate technologies.
Design/methodology/approach
This research starts with the analysis of previous KBE solutions for RP/RM process selection, then a new KBE tool is proposed through the integration of artificial intelligence tools such as fuzzy logic, artificial neural networks (ANNs) and relational databases. Three case studies, provided by a Spanish machinery design centre, are used in order to measure the suitability of the proposed system for the assessment of real designs of special purpose mechanical parts.
Findings
The paper reports several improvements based on case studies which include a more suitable logic for process selection according to the designer's criteria and improvements in the overall parts cost estimation when compared to conventional parametric methods.
Practical implications
The newly proposed KBE system has proven useful especially in cases where non‐experts or students need to select a RP/RM process according to an initial product design specification. The cost estimation module based on ANNs provides a practical tool which may be used by academics but also practitioners who wish to automate product costing calculations.
Originality/value
Unlike previous solutions, the proposed system provides a straightforward means for RP/RM selection by an overall ranking of candidate processes, part cost estimation and materials selection. The main contribution is the modular design and logical planning, that overcomes the dilemma: material‐or‐process first.