A monitoring and diagnostic approach for stochastic textured surfaces

TL;DR

This paper introduces a supervised learning-based method for monitoring and diagnosing defects in stochastic textured surfaces, such as textiles and metal surfaces, using spatial statistics derived from residual errors.

Contribution

It presents a novel approach combining supervised learning with spatial moving statistics for defect detection in stochastic textured surfaces.

Findings

Successfully detects local defects in textile images.

Effectively characterizes complex spatial dependencies.

Applicable to various stochastic surface data types.

Abstract

We develop a supervised-learning-based approach for monitoring and diagnosing texture-related defects in manufactured products characterized by stochastic textured surfaces that satisfy the locality and stationarity properties of Markov random fields. Examples of stochastic textured surface data include images of woven textiles; image or surface metrology data for machined, cast, or formed metal parts; microscopy images of material microstructure samples; etc. To characterize the complex spatial statistical dependencies of in-control samples of the stochastic textured surface, we use rather generic supervised learning methods, which provide an implicit characterization of the joint distribution of the surface texture. We propose two spatial moving statistics, which are computed from residual errors of the fitted supervised learning model, for monitoring and diagnosing local aberrations in the general spatial statistical behavior of newly manufactured stochastic textured surface samples in a statistical process control context. We illustrate the approach using images of textile fabric samples and simulated 2-D stochastic processes, for which the algorithm successfully detects local defects of various natures. Supplemental discussions, results, data and computer codes are available online.