PSAM 16 Conference Paper Overview
Welcome to the PSAM 16 Conference paper and speaker overview page.
Lead Author: Marcin Hinz Co-author(s): Doha Meslem, dmeslem.dm@gmail.com
Stefan Bracke, bracke@uni-wuppertal.de
| Application of Gaussian Mixed Model for the clustering of fine grinded surfaces |
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| The optical perception of high precision, fine grinded surfaces plays a major role, especially in various consumer goods. The very complex manufacturing process of many of these products consists of a variety of parameters such as feed rate, cutting speed, grinding disc, cutting fluid, contact force or process time. The change of a parameter setting has a direct effect on the surface topography. Therefore, a standardized and optimized configuration of process parameters enables a desired quality of a product. By varying the process parameters of the high precision fine grinding process, a variety of cutlery samples with different surface topographies are manufactured. Surface topographies of grinded surfaces are measured by the use of classical methods (roughness measuring device, gloss measuring device, spectrophotometer). To improve the conventional methods, a new image processing analysis approach is needed to get a faster and more cost-effective analysis of produced surfaces. For the recognition of the product’s quality, a systematic analysis based on unsupervised learning techniques is needed. In this study, a multivariate analysis of interpreted data of knife surfaces is performed. For this purpose, three different knife types were measured in the lab and analyzed with the help of computer vision. The data is multivariate with over 40 extracted features, structured, and not full (some variables – mainly the production process data – are partially missing). Here, we discuss the theoretical and practical application of an unsupervised machine learning method on the mentioned data. Moreover, this research paper focuses on Gaussian Mixed Model, a probabilistic-based unsupervised machine learning method. This algorithm deals with soft-clusters, forming clusters and giving each datapoint certain probabilities. To create the most accurate output from an unsupervised machine learning method however, the data are clustered and subsequently compared to the already company-set criteria and to tangible knife properties such as roughness. This parameter study includes pre-processing and preparing the data using standardizing and normalizing techniques and adopting the algorithm’s parameters to monitor how this affects the clusters itself. To perform a comprehensive analysis of the algorithm’s parameters and ensure minimal discrepancy between the clustered data and classes proposed by the manufacturer, Gaussian Mixed Model parameters are tuned. This research paper therefore focuses on the study of different cluster-setups and the choice of the most accurate output based on the achieved results. The research has a generic character and can be applied to other sets of extracted data of fine grinded surfaces. |
Paper M.110 Preview
Author and Presentation Info
Lead Author Name: Stefan Bracke (m.hinz@uni-wuppertal.de)
Bio: 1991 – 1995 Studies of Mechanical Engineering; University of Bochum 1997 – 1999 Doctor´s Thesis: „Quality strategies regarding to the reuse of components of technical products within the product remanufacturing (product recycling)” 1996 – 1999 University of Bochum, Chair of Manufacturing System Planning, Prof. Dr.-Ing. H. Schnauber, Section: Quality Planning and Control 1996 – 2000 Consultancy INNOSYS GmbH & Co. KG; Quality Management, Bochum, Germany 2000 – 2006 Dr. Ing. h.c. F. Porsche AG, Department of Quality Management, Stuttgart, Germany 2007 – 2010 Cologne University of Applied Sciences, Cologne, Germany Faculty of Vehicle Systems and Production, Institute of Production, Professor; Department of Quality Management and Production Metrology Since 10.2010 University of Wuppertal, Germany, Faculty of Safety Engineering Professorship: Chair of Reliability Engineering and Risk Analytics Since 04.2016 Guest Professorship at Meiji University, Kawasaki, Tokio, Japan
Country: Germany
Company: University of Wuppertal
Job Title: Professor
Bio: 1991 – 1995 Studies of Mechanical Engineering; University of Bochum 1997 – 1999 Doctor´s Thesis: „Quality strategies regarding to the reuse of components of technical products within the product remanufacturing (product recycling)” 1996 – 1999 University of Bochum, Chair of Manufacturing System Planning, Prof. Dr.-Ing. H. Schnauber, Section: Quality Planning and Control 1996 – 2000 Consultancy INNOSYS GmbH & Co. KG; Quality Management, Bochum, Germany 2000 – 2006 Dr. Ing. h.c. F. Porsche AG, Department of Quality Management, Stuttgart, Germany 2007 – 2010 Cologne University of Applied Sciences, Cologne, Germany Faculty of Vehicle Systems and Production, Institute of Production, Professor; Department of Quality Management and Production Metrology Since 10.2010 University of Wuppertal, Germany, Faculty of Safety Engineering Professorship: Chair of Reliability Engineering and Risk Analytics Since 04.2016 Guest Professorship at Meiji University, Kawasaki, Tokio, Japan
Country: Germany
Company: University of Wuppertal
Job Title: Professor