The ENBIS‐16 quality and reliability engineering international special issue

Abstract

Grazia Vicario (Politecnico di Torino) chaired the Programme Committee, and Eleanor Stillman (University of Sheffield) chaired the Organizing Committee. The abstracts of all oral and poster presentations, in most instances accompanied by full papers and presentation slides, are available on the ENBIS website (www.enbis.org).The 2017 special issue of Quality and Reliability Engineering International is a result of the call for papers announced during the ENBIS-16 conference. All the submitted papers were peer-reviewed by 2 anonymous referees before being accepted for publication in this special issue. This special issue covers advances in statistical methods from an industrial and business perspective, as is the purpose of ENBIS. Most of the papers focus on manufacturing, starting from product design, moving to process optimization and control, and eventually arriving to end-of-life performance, as briefly summarized in the following.Product design and development is considered in the first 2 papers. In particular, Halabi, Kenett, and Sacerdote, 1 propose dynamic Bayesian networks as a tool to integrate information from multiple sources to calculate risks at different product development stages. Hejazi and Esfahani 2 consider challenge of multistagemultiresponse problems in gear manufacturing with special attention to the design stage, where stresses affecting the mechanical component have to be taken into account. It is specifically shown that robust estimation of surfaces perform better than ordinary least squares regression.Acceptance testing is faced by Santos-Fernández, Govindaraju, and Jones, 3 where consumer risks of imperfect testing is considered in different scenarios of a presence-absence sampling plans used in the food industry to determine whether a batch of food is contaminated.Factorial designs with missing values are examined by Xampeny, Grima, and Tort-Martorell, 4 where a solution when interactions are negligible is proposed to deduce missing values.Motivated by a real semiconductor problem, Zappa and Borgoni 5 propose a method to extract a subgrid from an initial monitored network to effectively maintain the spatial representativeness.Multistage reliability and multistage-multiresponse problems are faced in 2 papers. In the first paper, Moslemi and Esfahani 6 consider the manufacturing challenge of multistage processes in gear production. Multistage processes require special considerations for experimenters, and here, a robust estimation of surfaces is found to perform better than ordinary least squares regression in these cases. In the second paper, Hejazi 7 presents an approach to optimize multiple quality metrics of a multistage system that considers the stochastic behaviour of input, intermediate, and output variables via stochastic programming.Problems affecting traditional control charts design when the data sampling sequence is relevant (ie, data are not random) are examined and commented by Shper and Adler. 8 They review the possible drawbacks and suggest a new test for data randomness....