The purpose of this paper is to investigate if early stage function models of design can be used to predict the market-value of a commercial product. In previous research, several metrics of complexity of graph-based product models have been proposed and suitably chosen combinations of these metrics have been shown to predict the time required in assembling commercial products. By extension, this research investigates if this approach, using new sets of combinations of complexity metrics, can predict market-value. To this end, the complexity values of function structures for eighteen products from the Design Repository are determined from their function structure graphs, while their market values are procured from different vendor quotes in the open market. The complexity and value information for fourteen samples are used to train a neural net program to define a predictive mapping scheme. This program is then used to predict the value of the final four products. The results of this approach demonstrate that complexity metrics can be used as inputs to neural networks to establish an accurate mapping from function structure design representations to market values to within the distribution of values for products of similar type.

The work in this paper uses neural networks to develop a relationship model between assembly times and complexity metrics applied to defined mate connections within SolidWorks assembly models. This model is then used to develop a Design for Assembly (DFA) automation tool that can predict a product’s assembly time using defined mate connections within SolidWorks assembly models. The development of this new method consists of: creating a SolidWorks (SW) Add-in to automatically extract the mate connections from SW assembly models, parsing the mate connections into graphs, implementing a new complexity training algorithm to predict assembly times based on mate graphs, and evaluating the effectiveness of the new method. The motivation, development, and evaluation of the new automated DFA method are presented in this paper. Ultimately, the method that is trained on both fully defined and partially defined assembly models is shown to provide assembly time prediction results that are typically within 25% of target time, but with one outlier at 95% error, suggesting that a more robust training set is needed.

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.

This paper presents a method for identifying components in CAD assemblies that have surfaces that have complementary, duplicate surfaces. The method evaluates faces on two parts within a given proximity by measuring the approximate surface that has parallel and opposing surface normal between the faces. This method can be used for applications such as identifying potential lazy parts, a previously developed method used by an automotive OEM, and generating connectivity graphs for use in manufacturing assembly time estimation. The method considers threshold distance between parts, orientation angle between faces, and targeted similarity overlap between geometries. This paper presents the algorithm, a justification, and example test cases and scenarios that demonstrate its utility.