Evaluation and Comparison of Two Design for Assembly Methods: Subjectivity of Information Inputs

Abstract

This paper presents a comparison study on two design for assembly (DFA) tools, Boothroyd and Dewhurst’s Design for Manufacturing and Assembly software and the Mathieson-Summers connective-complexity algorithm, focusing on the amount of information required from the designer to complete the analysis and the subjectivity of this information. The Boothroyd Dewhurst software requires the user to answer a set of questions about each part and how it is assembled to estimate an assembly time, assembly cost, and to suggest design improvements. The connective-complexity method predicts assembly times based on the physical connectivity between parts within an assembly. The methods are applied to three consumer products and evaluated and compared through five criteria: approximate time to conduct the analysis, predicted assembly time, amount of required input information, amount of subjective information, and number of redesign features provided to the user. The results show that the DFMA software requires the user to go through eight types of information answering a total of forty nine questions per part. Sixteen of these questions are based on subjective information making the analysis nearly a third subjective. The connectivity method requires only two types of information and a total of five questions per part to complete the analysis, none of it being subjective. The predicted assembly times from the connective-complexity DFA method ranged from 13.11% to 49.71% lower than the times predicted by the DFMA software. The results from this comparison can be used to bench mark DFA methods so that their weaknesses can be identified and improved.

Comparison of Graph Generation Methods for Structural Complexity Based Assembly Time Estimation

“…One limitation of the B&D method is the time required to analyze a product even with the extensive training (which is a service that can be purchased). The time required to analyze product using the B&D method motivated the need for an automated assembly time estimation method [13]. Regardless of the limitations, this method appears to be the most prevalent in the literature and in industrial application.…”

“…The ANN is used as a data mining tool to find the relationship between the complexity vector and the known assembly times. The use of the ANN was shown to improve the predictive ability of the method, however the manual bi-partite graph generation was still time consuming and inherently subjective due to manual creation [13,17]. To further improve the CCM, an automated graph generation method is needed.…”

Manufacturing Assembly Time Estimation Using Structural Complexity Metric Trained Artificial Neural Networks

“…The handling and insertion times selected from each set of tables are summed for each of the parts to determine the estimated assembly time for the product. Though beneficial, this time consuming method includes subjective inputs [8,21]. Additionally, the information required for this analysis is often only present after the design is complete or near completion.…”

“…To begin the process of evaluating and refining the connectivity method, the connectivity graphs and associated MTM-based time estimates for a sample of systems are required. As discussed earlier, there are currently no automated means of generating connectivity information although current research in this field is ongoing [21,22,27].…”

A UNITY Sketch Based Modelling Environment for Virtual Assembly and Machining to Evaluate DFMA Metrics

“…The first stage in software solutions for DFA analysis was the computerized version of Boothroyd and Dewhurst handbook [4]. This approach while removing the user from manual calculations still requires a lengthy analysis process consisting of forty-nine questions per part [5].…”