Data-mining-based methodology for the design of product families

Agard, Bruno; Kusiak, Andrew

doi:10.1080/00207540410001691929

Cited by 143 publications

(52 citation statements)

References 14 publications

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“…Similar work that has been done in the joint field of product design and machine learning include: Agard and Kunsiak's work on data mining for the design of product families [23], where algorithms were used for customer segregation; Ferguson et al's work on creating a decision support system for providing information from later to earlier stages in the design process [24]. A good overview of other applications of computational intelligence in product design engineering can be found in Kusiak …”

Section: Stated and Revealed Preferencesmentioning

confidence: 97%

Approaches for Identifying Consumer Preferences for the Design of Technology Products: A Case Study of Residential Solar Panels

Chen

Honda

Yang

2013

Journal of Mechanical Design

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This thesis investigates ways to obtain consumer preferences for technology products to help designers identify the key attributes that contribute to a product's market success. A case study of residential solar PV panels is conducted in the context of the California, USA market within the 2007-2011 time span. First, interviews are conducted with solar panel installers to gain a better understanding of the solar industry. Second, a revealed preference method is implemented using actual market data and technical specifications to extract preferences. The approach is explored with three machine learning methods: Artificial Neural Networks, Random Forest decision trees, and Gradient Boosted regression. Finally, a stated preference self-explicated survey is conducted, and the results using the two methods compared. Three common critical attributes are identified from a pool of 34 technical attributes: power warranty, panel efficiency, and time on market. From the survey, additional non-technical attributes are identified: panel manufacturer's reputation, name recognition, and aesthetics. The work shows that a combination of revealed and stated preference methods may be valuable for identifying both technical and non-technical attributes to guide design priorities.

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Section: Stated and Revealed Preferencesmentioning

confidence: 97%

Approaches for Identifying Consumer Preferences for the Design of Technology Products: A Case Study of Residential Solar Panels

Chen

Honda

Yang

2013

Journal of Mechanical Design

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“…Clustering can be used to group customers or functions of similar behavior (Agard and Kusiak 2004;Jiao and Zhang 2005). Also, functional requirements in existing products can be clustered based on the similarity between them.…”

Section: Literature Review and Backgroundmentioning

confidence: 99%

“…An association rule describes an interesting relationship between attributes of different modules (Agard and Kusiak 2004;Jiao and Zhang 2005). Given a set of transactions, where each transaction is a set of attributes, an association rule is noted as A ⇒ B, where A and B are sets of attributes.…”

Section: Phase 3: Design Rule Generationmentioning

confidence: 99%

“…Association rule mining can uncover new design knowledge that is useful for defining a platform and common modules. Agard and Kusiak (2004) proposed a three-step method for the design of product families based on the analysis of customers' requirements using a data mining approach. In the first step, data mining algorithms are used for customer segmentation.…”

Section: Literature Review and Backgroundmentioning

confidence: 99%

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A methodology for knowledge discovery to support product family design

2008

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This paper introduces a methodology for knowledge discovery related to product family design that integrates an ontology with data mining techniques. In the proposed methodology, the ontology represents attributes for the components of products in functional hierarchies. Fuzzy clustering is employed for data mining to first partition product functions into subsets for identifying modules in a given product family and then identify the similarity level of components in a module. Module categorization is introduced to support association rule mining for knowledge discovery related to platform design. We apply the proposed methodology to first develop and then utilize design knowledge for a family of power tools. Based on the developed design knowledge, a new platform is suggested to improve commonality in the power tool family.

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“…Generated information can then be feedback to establish design change requirement and product quality improvement. Agard and Kusiak [143] applied data mining to customer response data for its utilization in the design of product families. They used clustering for customer segmentation, i.e.…”

Section: Association In Manufacturingmentioning

confidence: 99%

Data mining in manufacturing: a review based on the kind of knowledge

2008

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In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, such as product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection and so on. Data mining has emerged as an important tool for knowledge acquisition in manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with an special emphasis on the type of functions to be performed on data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been applied to the abstracts and keywords of 150 identified literatures to identify the research gaps and find the linkages between knowledge area, knowledge type and data mining tools and techniques applied.

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Data-mining-based methodology for the design of product families

Cited by 143 publications

References 14 publications

Approaches for Identifying Consumer Preferences for the Design of Technology Products: A Case Study of Residential Solar Panels

Approaches for Identifying Consumer Preferences for the Design of Technology Products: A Case Study of Residential Solar Panels

A methodology for knowledge discovery to support product family design

Data mining in manufacturing: a review based on the kind of knowledge

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