Intelligent Orthogonal Defect Classification (ODC) towards Manufacturing Nonconformance Tracking and Diagnostic Recovery

Tracking, diagnosis and recovery of the nonconformance in a manufacturing system is a time consuming and tedious process, which could result in substantial delays in the production process. Therefore it is imperative to develop an intelligent system that can track the nonconformance and provide timely diagnostic recovery. Since the current analysis techniques are either qualitative or quantitative, this paper describes a novel approach that bridges the gap between the qualitative and quantitative techniques based on a method called Orthogonal Defect Classification (ODC). The key concept in ODC is that the nonconformances are categorized into classes that collectively point to the part of the product development process that needs attention, much like characterizing a point in a Cartesian system of orthogonal axes by its (x, y, z) coordinates. The proposed system provides a basic capability to extract nonconformance signatures, infer the health of the product development process and recommend diagnostic recovery.