Accurate estimation of prediction models for operator-induced defects in assembly manufacturing processes

Abstract

The presence of defects in industrial manufacturing may compromise the final quality and cost of a product. Among all possible defect causes, human errors have significant effects on the performances of assembly systems. Much research has been conducted in recent years focusing on the problem of defect generation in assembly processes, considering the close connection between assembly complexity and human errors. It was observed that the relationship between the average number of defects introduced during each assembly phase and the related assembly complexity follows a power-law relationship. Accordingly, many authors proposed a data logarithmic transformation in order to linearize the mathematical model. However, as has already been discussed in literature, when the model is retransformed in the original form a significant bias may occur, leading to completely wrong predictions. In this paper, the bias due to the logarithmic transformation of models for predicting defects in assembly is analyzed and discussed. Two alternative methods are proposed and compared to overcome this drawback: the use of a bias correction factor to the retransformed fitted values and a power-law nonlinear regression model. The latter has proved to be the best approach to predict defects with few non-repeated data and affected by high variability, such as in the case under study.

Keywords:

• quality control
• assembly process
• defect generation
• logarithmic transformation
• nonlinear regression model

Acknowledgments

The authors gratefully acknowledge Riccardo Gervasi for the fruitful collaboration in this project.

Additional information

Funding

This work has been partially supported by the “Italian Ministry of Education, University and Research,” Award “TESUN‐83486178370409 finanziamento dipartimenti di eccellenza CAP. 1694 TIT. 232 ART. 6.”

Notes on contributors

Maurizio Galetto

Maurizio Galetto received the Master of Science degree in Physics from University of Turin, Italy, in 1995 and the PhD Degree in Metrology: Measuring Science and Technique from Politecnico di Torino, Italy, in 2000. He is currently Head of Department and Full Professor at the Department of Management and Production Engineering (DIGEP) of the Politecnico di Torino, where he teaches Quality Engineering and Experimental Statistics and Mechanical Measurement. He is Associate Member of CIRP (The International Academy for Production Engineering) and Fellow of A.I.Te.M. (Associazione Italiana di Tecnologia Meccanica) and E.N.B.I.S. (European Network for Business and Industrial Statistics). He is Member of the Editorial Board of the scientific international journal Nanomanufacturing and Metrology and collaborates as referee for many international journals in the field of Industrial Engineering. He is author and coauthor of 4 books and more than 100 published papers in scientific journals and international conference proceedings. His current research interests are in the areas of Quality Engineering, Statistical Process Control, Industrial Metrology and Production Systems. At present, he collaborates in some important research projects for public and private organizations.

Elisa Verna

Elisa Verna received the Master of Science degree in Engineering and Management from Politecnico di Torino, Italy, in 2016. She is currently PhD student in Management, Production, and Design at the Department of Management and Production Engineering (DIGEP) of the Politecnico di Torino. Her current research interests are in the areas of Quality Engineering, Statistical Process Control and Innovative Production Systems.

Gianfranco Genta

Gianfranco Genta received the Master of Science degree in Mathematical Engineering from Politecnico di Torino, Italy, in 2005 and the PhD Degree in Metrology: Measuring Science and Technique from Politecnico di Torino in 2010. He is currently Fixed-Term Researcher at the Department of Management and Production Engineering (DIGEP) of the Politecnico di Torino, where he teaches Experimental Statistics and Mechanical Measurement. He is Research Affiliate of CIRP (The International Academy for Production Engineering) and Fellow of A.I.Te.M. (Associazione Italiana di Tecnologia Meccanica). He is author and coauthor of 3 books and more than 40 publications on national/international journals and conference proceedings. His current research focuses on Industrial Metrology, Quality Engineering and Experimental Data Analysis.