Observational data-driven modeling and optimization of manufacturing processes

Abstract The dramatic increase of observational data across industries provides unparalleled opportunities for data-driven decision making and management, including the manufacturing industry. In the context of production, data-driven approaches can exploit observational data to model, control and improve process performance. When supplied by observational data with adequate coverage to inform the true process performance dynamics, they can overcome the cost associated with intrusive controlled designed experiments and can be applied for both process monitoring and improvement. We propose a novel integrated approach that uses observational data for identifying significant control variables while simultaneously facilitating process parameter design. We evaluate our method using data from synthetic experiments and also apply it to a real-world case setting from a tire manufacturing company.

[1]  André I. Khuri,et al.  Response surface methodology , 2010 .

[2]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[3]  Gerardo Beruvides,et al.  Multi-objective optimization based on an improved cross-entropy method. A case study of a micro-scale manufacturing process , 2016, Inf. Sci..

[4]  Pedro Larrañaga,et al.  Filter versus wrapper gene selection approaches in DNA microarray domains , 2004, Artif. Intell. Medicine.

[5]  Darrell Whitley,et al.  A genetic algorithm tutorial , 1994, Statistics and Computing.

[6]  Margaret J. Robertson,et al.  Design and Analysis of Experiments , 2006, Handbook of statistics.

[7]  Raúl M. del Toro,et al.  System Identification of the High Performance Drilling Process for Network-Based Control , 2007 .

[8]  Ratna Babu Chinnam,et al.  mr2PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification , 2011, Inf. Sci..

[9]  Rodolfo E. Haber,et al.  Hybrid Incremental Modeling Based on Least Squares and Fuzzy $K$-NN for Monitoring Tool Wear in Turning Processes , 2012, IEEE Transactions on Industrial Informatics.

[10]  Xiaoming Xu,et al.  A hybrid genetic algorithm for feature selection wrapper based on mutual information , 2007, Pattern Recognit. Lett..

[11]  Le Song,et al.  Supervised feature selection via dependence estimation , 2007, ICML '07.

[12]  Larry A. Rendell,et al.  A Practical Approach to Feature Selection , 1992, ML.

[13]  C. A. Murthy,et al.  Unsupervised Feature Selection Using Feature Similarity , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Huan Liu,et al.  Toward integrating feature selection algorithms for classification and clustering , 2005, IEEE Transactions on Knowledge and Data Engineering.

[15]  Jiju Antony,et al.  Simultaneous Optimisation of Multiple Quality Characteristics in Manufacturing Processes Using Taguchi's Quality Loss Function , 2001 .

[16]  Isabel González,et al.  Variable Selection for Multivariate Statistical Process Control , 2010 .

[17]  Huan Liu,et al.  Semi-supervised Feature Selection via Spectral Analysis , 2007, SDM.

[18]  Neil R. Ullman,et al.  Signal-to-noise ratios, performance criteria, and transformations , 1988 .

[19]  David Casasent,et al.  An improvement on floating search algorithms for feature subset selection , 2009, Pattern Recognit..

[20]  Heng Tao Shen,et al.  Dimensionality Reduction , 2009, Encyclopedia of Database Systems.

[21]  R. H. Myers,et al.  Response Surface Techniques for Dual Response Systems , 1973 .

[22]  Kai Yang,et al.  SAFS: A deep feature selection approach for precision medicine , 2016, 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[23]  Markus Hammer,et al.  How big data can improve manufacturing , 2022 .

[24]  G. Geoffrey Vining,et al.  Combining Taguchi and Response Surface Philosophies: A Dual Response Approach , 1990 .

[25]  Bernhard Schölkopf,et al.  Use of the Zero-Norm with Linear Models and Kernel Methods , 2003, J. Mach. Learn. Res..

[26]  Wei Jiang,et al.  High-Dimensional Process Monitoring and Fault Isolation via Variable Selection , 2009 .

[27]  Zenglin Xu,et al.  Discriminative Semi-Supervised Feature Selection Via Manifold Regularization , 2009, IEEE Transactions on Neural Networks.

[28]  Sanmay Das,et al.  Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection , 2001, ICML.

[29]  Hui-Huang Hsu,et al.  Hybrid feature selection by combining filters and wrappers , 2011, Expert Syst. Appl..

[30]  Tae-Hyung Kim,et al.  Feature selection for manufacturing process monitoring using cross-validation , 2013 .

[31]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[32]  Özge Uncu,et al.  A novel feature selection approach: Combining feature wrappers and filters , 2007, Inf. Sci..

[33]  G. Geoffrey Vining,et al.  Taguchi's parameter design: a panel discussion , 1992 .

[34]  Anil K. Jain,et al.  39 Dimensionality and sample size considerations in pattern recognition practice , 1982, Classification, Pattern Recognition and Reduction of Dimensionality.

[35]  R. Marler,et al.  The weighted sum method for multi-objective optimization: new insights , 2010 .

[36]  Marko Robnik-Sikonja,et al.  Theoretical and Empirical Analysis of ReliefF and RReliefF , 2003, Machine Learning.

[37]  Anil K. Jain,et al.  Dimensionality reduction using genetic algorithms , 2000, IEEE Trans. Evol. Comput..

[38]  Connie M. Borror,et al.  Response Surface Methodology: A Retrospective and Literature Survey , 2004 .

[39]  David G. Stork,et al.  Pattern Classification , 1973 .

[40]  Byung Rae Cho,et al.  Another view of dual response surface modeling and optimization in robust parameter design , 2009 .

[41]  Kwok-Leung Tsui,et al.  Signal-to-Noise Ratios for Robust Design† , 2008 .

[42]  Connie M. Borror,et al.  Robust Parameter Design: A Review , 2004 .

[43]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[44]  A. Shamsai,et al.  Multi-objective Optimization , 2017, Encyclopedia of Machine Learning and Data Mining.

[45]  田口 玄一,et al.  Introduction to quality engineering : designing quality into products and processes , 1986 .

[46]  Huan Liu,et al.  Feature Selection for Classification: A Review , 2014, Data Classification: Algorithms and Applications.

[47]  Carla E. Brodley,et al.  Feature Selection for Unsupervised Learning , 2004, J. Mach. Learn. Res..

[48]  Jean-Pierre Gauchi,et al.  Comparison of selection methods of explanatory variables in PLS regression with application to manufacturing process data , 2001 .

[49]  Huan Liu,et al.  Feature Selection for Classification , 1997, Intell. Data Anal..