Utilizing past Engineering Change (EC) knowledge to predict the impact of a proposed EC effect requires an approach for computing similarity between ECs. This paper presents an approach for computing the similarity between ECs each defined by a set of disparate attributes. Since the available information is probabilistic, measures of information are used for defining measures to compute similarity between two attribute values or ECs. The semantics associated with attribute values are used to compute similarity between them. The similarities between attribute values are aggregated to compute the similarity between ECs in the context of overall goal. An example EC knowledge-base is used for evaluating our approach against a statistical approach and two state-of-the-art approaches, namely, metric space and probability-based. The evaluation is done from two perspectives: precision in retrieving similar ECs and success in predicting the impact. The results show that there is a statistically significant improvement in precision and success rate using our approach as compared to those using other approaches. In addition, based on the results, it can be inferred with 90% confidence that for a large number of changes (N >; 100) the success in predicting impact using our approach shall be greater than that obtained using the two state-of-the-art approaches.

There is a need to promote drastically increased levels of interoperability of product data across a broad spectrum of stakeholders, while ensuring that the semantics of product knowledge are preserved, and when necessary, translated. In order to achieve this, multiple methods have been proposed to determine semantic maps across concepts from different representations. Previous research has focused on developing different individual matching methods, i.e., ones that compute mapping based on a single matching measure. These efforts assume that some weighted combination can be used to obtain the overall maps. We analyze the problem of combination of multiple individual methods to determine requirements specific to product development and propose a solution approach called FEedback Matching Framework with Implicit Training (FEMFIT). FEMFIT provides the ability to combine the different matching approaches using ranking Support Vector Machine (ranking SVM). The method accounts for nonlinear relations between the individual matchers. It overcomes the need to explicitly train the algorithm before it is used, and further reduces the decision-making load on the domain expert by implicitly capturing the expert's decisions without requiring him to input real numbers on similarity. We apply FEMFIT to a subset of product constraints across a commercial system and the ISO standard. We observe that FEMIT demonstrates better accuracy (average correctness of the results) and stability (deviation from the average) in comparison with other existing combination methods commonly assumed to be valid in this domain.

An increasing trend toward product development in a collaborative environment has resulted in the use of various software tools to enhance the product design. This requires a meaningful representation and exchange of product data semantics across different application domains. This paper proposes an ontology-based framework to enable such semantic interoperability. A standards-based approach is used to develop a Product Semantic Representation Language (PSRL). Formal description logic (DAML+OIL) is used to encode the PSRL. Mathematical logic and corresponding reasoning is used to determine semantic equivalences between an application ontology and the PSRL. The semantic equivalence matrix enables resolution of ambiguities created due to differences in syntaxes and meanings associated with terminologies in different application domains. Successful semantic interoperability will form the basis of seamless communication and thereby enable better integration of product development systems. Note to Practitioners-Semantic interoperability of product information refers to automating the exchange of meaning associated with the data, among information resources throughout the product development. This research is motivated by the problems in enabling such semantic interoperability. First, product information is formalized into an explicit, extensible, and comprehensive product semantics representation language (PSRL). The PSRL is open and based on standard W3C constructs. Next, in order to enable semantic translation, the paper describes a procedure to semi-automatically determine mappings between exactly equivalent concepts across representations of the interacting applications. The paper demonstrates that this approach to translation is feasible, but it has not yet been implemented commercially. Current limitations and the directions for further research are discussed. Future research addresses the determination of semantic similarities (not exact equivalences) between the interacting information resources.