Abstract: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-va… Show more
“…The inferencing support (surrogate) explored here is the ability of a representation to support the prediction of a market value for a product based on the functional description. This work builds on previous work that explored market value prediction (Mathieson et al, 2011), the accuracy of prediction models (Sridhar et al, 2016 a ), and the precision of these prediction models (Mohinder et al, 2014, 2016). The research questions are the following: Can the value of a representation be used to compare two similar function representations? Can the value of a representation be associated with specific pruning rules? …”
Section: Research Motivation: Comparative Studies On Representation Imentioning
confidence: 92%
“…7) that are classified into four classes: size, interconnection, centrality, and decomposition (Mathieson & Summers, 2010). These complexity metrics have been successfully used to model the assembly time and market values using the product's function structure and assembly models (Mathieson et al, 2011; Namouz & Summers, 2013, 2014; Owensby & Summers, 2014). Fig.…”
“…Since changing the abstraction in a design representation can possibly lead to it being classified as a different design representation and the pruning rules themselves relate to both the grammar and the vocabulary allowed (Summers & Shah, 2004), it is important to verify the impact on the original set of function structures of different abstractions carried out in the following section. In order to provide a uniform base for comparison of different representation, the same set of 20 consumer products were used for this study and are similar to the sets used in previous work (Mathieson et al, 2011; Mohinder et al, 2014; Gill & Summers, 2016; Sridhar et al, 2016 b ).…”
Section: Abstraction By Pruning As a Distinguishing Characteristic Ofmentioning
confidence: 99%
“…From the previous discussions on pruning and comparison of error values obtained from the graph connectivity complexity method, which will be discussed further in Section 7, it is clear that there is a marked difference in how well each of the various pruned function structure sets behave as prediction models. This method has been used for market price prediction (Mathieson et al, 2011; Mohinder et al, 2014; Sridhar et al, 2016 b ), for assembly time estimation (Owensby et al, 2012; Namouz & Summers, 2013, 2014), for assembly quality defect prediction (Patel et al, 2016), and for using requirements to predict life cycle costs (Visotsky et al, 2017). As per the classification framework developed in Summers and Shah (2004), a design representation can be classified as being distinct on the basis of these five characteristics: vocabulary, structure (grammar), expression, purpose, and abstraction of the representation.…”
Section: Classification Of Pruned Function Structures As Independent mentioning
Benchmarking function modeling and representation approaches requires a direct comparison, including the inferencing support by the different approaches. To this end, this paper explores the value of a representation by comparing the ability of a representation to support reasoning based on varying amounts of information stored in the representational components of a function structure: vocabulary, grammar, and topology. This is done by classifying the previously developed functional pruning rules into vocabulary, grammatical, and topological classes and applying them to function structures available from an external design repository. The original and pruned function structures of electromechanical devices are then evaluated for how accurately market values can be predicted using the graph complexity connectivity method. The accuracy is found to be inversely related to the amount of information and level of detail. Applying the topological rule does not significantly impact the predictive power of the models, while applying the vocabulary rules and the grammar rules reduces the accuracy of the predictions. Finally, the least predictive model set is that which had all rules applied. In this manner, the value of a representation to predict or answer questions is quantified.
“…The inferencing support (surrogate) explored here is the ability of a representation to support the prediction of a market value for a product based on the functional description. This work builds on previous work that explored market value prediction (Mathieson et al, 2011), the accuracy of prediction models (Sridhar et al, 2016 a ), and the precision of these prediction models (Mohinder et al, 2014, 2016). The research questions are the following: Can the value of a representation be used to compare two similar function representations? Can the value of a representation be associated with specific pruning rules? …”
Section: Research Motivation: Comparative Studies On Representation Imentioning
confidence: 92%
“…7) that are classified into four classes: size, interconnection, centrality, and decomposition (Mathieson & Summers, 2010). These complexity metrics have been successfully used to model the assembly time and market values using the product's function structure and assembly models (Mathieson et al, 2011; Namouz & Summers, 2013, 2014; Owensby & Summers, 2014). Fig.…”
“…Since changing the abstraction in a design representation can possibly lead to it being classified as a different design representation and the pruning rules themselves relate to both the grammar and the vocabulary allowed (Summers & Shah, 2004), it is important to verify the impact on the original set of function structures of different abstractions carried out in the following section. In order to provide a uniform base for comparison of different representation, the same set of 20 consumer products were used for this study and are similar to the sets used in previous work (Mathieson et al, 2011; Mohinder et al, 2014; Gill & Summers, 2016; Sridhar et al, 2016 b ).…”
Section: Abstraction By Pruning As a Distinguishing Characteristic Ofmentioning
confidence: 99%
“…From the previous discussions on pruning and comparison of error values obtained from the graph connectivity complexity method, which will be discussed further in Section 7, it is clear that there is a marked difference in how well each of the various pruned function structure sets behave as prediction models. This method has been used for market price prediction (Mathieson et al, 2011; Mohinder et al, 2014; Sridhar et al, 2016 b ), for assembly time estimation (Owensby et al, 2012; Namouz & Summers, 2013, 2014), for assembly quality defect prediction (Patel et al, 2016), and for using requirements to predict life cycle costs (Visotsky et al, 2017). As per the classification framework developed in Summers and Shah (2004), a design representation can be classified as being distinct on the basis of these five characteristics: vocabulary, structure (grammar), expression, purpose, and abstraction of the representation.…”
Section: Classification Of Pruned Function Structures As Independent mentioning
Benchmarking function modeling and representation approaches requires a direct comparison, including the inferencing support by the different approaches. To this end, this paper explores the value of a representation by comparing the ability of a representation to support reasoning based on varying amounts of information stored in the representational components of a function structure: vocabulary, grammar, and topology. This is done by classifying the previously developed functional pruning rules into vocabulary, grammatical, and topological classes and applying them to function structures available from an external design repository. The original and pruned function structures of electromechanical devices are then evaluated for how accurately market values can be predicted using the graph complexity connectivity method. The accuracy is found to be inversely related to the amount of information and level of detail. Applying the topological rule does not significantly impact the predictive power of the models, while applying the vocabulary rules and the grammar rules reduces the accuracy of the predictions. Finally, the least predictive model set is that which had all rules applied. In this manner, the value of a representation to predict or answer questions is quantified.
“…The original connectivity method proposes that the properties of product connectivity graphs could be used to estimate assembly times for a given product [15]. The properties of a bi-partite graph, such that shown in Figure 4, are the basis for the analysis proposed here and in the authors' previous research [28][29][30]. The graph properties used here are based on the same as that proposed in the original assembly time estimation research based on connectivity [15].…”
Assembly time estimation is traditionally a time-intensive manual process that requires detailed geometric and process information, which is often subjective and qualitative in nature. As a result, assembly time estimation is rarely applied during early design iterations. In this paper, the authors explore the possibility of automating the assembly time estimation process while reducing the level of design detail required. In this approach, they train artificial neural networks (ANNs) to estimate the assembly times of vehicle subassemblies using either assembly connectivity or liaison graph properties, respectively, as input data. The effectiveness of estimation is evaluated based on the distribution of estimates provided by a population of ANNs trained on the same input data using varying initial conditions. Results indicate that this method can provide time estimates of an assembly process with ±15% error while relying exclusively on the geometric part information rather than process instructions.
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