Using regression models for predicting the product quality in a tubing extrusion process

Vicente García (), J. Salvador Sánchez, Luis Alberto Rodríguez-Picón, Luis Carlos Méndez-González and Humberto de Jesús Ochoa-Domínguez
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Vicente García: Universidad Autónoma de Ciudad Juárez
J. Salvador Sánchez: Universitat Jaume I
Luis Alberto Rodríguez-Picón: Universidad Autónoma de Ciudad Juárez
Luis Carlos Méndez-González: Universidad Autónoma de Ciudad Juárez
Humberto de Jesús Ochoa-Domínguez: Universidad Autónoma de Ciudad Juárez

Journal of Intelligent Manufacturing, 2019, vol. 30, issue 6, No 14, 2535-2544

Abstract: Abstract Quality in a manufacturing process implies that the performance characteristics of the product and the process itself are designed to meet specific objectives. Thus, accurate quality prediction plays a principal role in delivering high-quality products to further enhance competitiveness. In tubing extrusion, measuring of the inner and outer diameters is typically performed either manually or with ultrasonic or laser scanners. This paper shows how regression models can result useful to estimate both those physical quality indices in a tube extrusion process. A real-life data set obtained from a Mexican extrusion manufacturing company is used for the empirical analysis. Experimental results demonstrate that k nearest-neighbor and support vector regression methods (with a linear kernel and with a radial basis function) are especially suitable for predicting the inner and outer diameters of an extruded tube based on the evaluation of 15 extrusion and pulling process parameters.

Keywords: Regression models; Extrusion process; Product quality prediction; Support vector regression; K nearest-neighbor (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10845-018-1418-7

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