Inference on errors in industrial parts: Kriging and variogram versus geometrical product specifications standard

• Published in: Applied stochastic models in business and industry Vol. 37; no. 5; pp. 839 - 858
• Main Authors: Maculotti, Giacomo, Pistone, Giovanni, Vicario, Grazia
• Format: Journal Article
• Language: English
• Published: Bognor Regis Wiley Subscription Services, Inc 01.09.2021
• Subjects: Biological properties; geometrical product specification; Inference; inspection strategies; Kriging; Manufacturing; Measurement techniques; Optical properties; Process parameters; Product specifications; surface topography characterization; Systematic errors; Tribology; variogram
• ISSN: 1524-1904; 1526-4025
• DOI: 10.1002/asmb.2603

This article focuses on the inference on the errors in manufactured parts controlled by using measurements devices. The characterization of the part surface topographies is core in several applications. A broad set of properties (tribological, optical, biological, mechanical, etc.) depends on the micro‐ and macrogeometry of the parts. Moreover, parts usually show typical deterministic geometric deviation pattern, referred to as manufacturing signatures, due to the specific manufacturing processes and process setup parameters adopted for their production. In several situations, the measurements may also be affected by systematic errors due to the measurement process, that might be caused, for example, by a poor part alignment during the measurement process. Measurement techniques and characterization methods have been standardized in the International Standard ISO 25178, defining parameters characterizing the surface topography and supplying methods and formula adapt to deal with this issue computationally. In the present article, we consider a type of spatial dependence between measured values at different points that suggest the use of the variogram to identify patterns in the parts. We offer a comparison, based on a real set of measures, between the latter approach and the conventional as a test of the efficient performance of our findings.