A RELIEFF attribute weighting and X - means clustering methodology for top - down product family optimization 1

Q1 Should RELIEFF be set as ReliefF as in other sources? See throughout article. Q2 Please supply date of meeting, place of publication, name of publisher and page numbers. Q3 Please supply date and place of symposium, place of publication and name of publisher. Q4 Please clarify publication details. Q5 Please supply place of publication. Q6 Please supply place of publication. Q7 Please supply date of conference, place of publication, name of publisher and page numbers. Q8 Please supply date and place of conference. Q9 Please supply date and place of conference, place of publication and name of publisher. Q10 Please supply date and place of conference, place of publication, name of publisher and page numbers. Q11 Please supply date and place of conference, place of publication, name of publisher and page numbers. Q12 Please supply place of publication and name of publisher. Q13 Please supply date and place of conference, place of publication, name of publisher and page numbers. Q14 Please supply date of conference, place of publication and name of publisher. Q15 Please supply date of conference, place of publication, name of publisher and page numbers. Q16 Please supply date of conference, place of publication, name of publisher and page numbers. Q17 Please supply page numbers. Q18 Please supply place of publication. Q19 Please provide Table 4 citation. (Received) This article proposes a top-down product family design methodology that enables product design engineers to identify the optimal number of product architectures directly from the customer preference data set by employing data mining attribute weighting and clustering techniques. The methodology also presents an efficient component sharing strategy to aid in product family commonality decisions. Two key data mining models are presented in this work to help guide the product design process: (1) RELIEFF attribute weighting technique that identifies and ranks product attributes, and (2) the X-means clustering approach that autonomously identifies the optimal number of candidate products. Product family commonality decisions are guided by once again employing the X-means clustering technique, this time to identify the components across product families that are most similar. A family of prototype aerodynamic air particle separators is used to evaluate the efficiency and validity of the proposed product family design methodology. Nomenclature AF Air flow area. f k Local product design objective function(s), a function of local design variables: f k (x k). R Eng Engineering design response (feasible/infeasible). T Cj Vector of …