XAIP: An eXplainable AI-Based Pipeline for Identifying Key Factors of Surface Defects in Strip Steel

Abstract

The surface defect has a direct impact on the performance and quality of the final product, so it is crucial to identify the key factors causing the defect. To achieve accurate key factor identification, an eXplainable AI-based Pipeline (XAIP) is proposed herein. The proposed XAIP combines data mode clustering and local model construction to meet practical application requirements. Meanwhile, after analyzing the characteristics of practical data, a strategy for defect rate calculation is designed to provide more fine-grained defect-related information to supervise model training. The final identification results are presented by a two-stage explainable method, which is designed to reveal the information of data modes and key variables and can give engineers more specific defect-related information. The experiment results based on two practical manufacturing datasets show the effectiveness of the proposed method.