Predicting Defect Rates of Printed Circuit Board Assemblies: Towards Zero Defect Manufacturing and Zero-Maintenance Strategies

Abstract

Printed Circuit Boards (PCB) manufacturing is a critical part of volatile supply chains for a wide variety of products and high value assets. PCBs are expected to exhibit zero defects and be subject to zero-maintenance. However low the defect rates, defects are highly disruptive and costly. Such defects can be introduced by a multitude of reasons, including faulty parts or sub-standard manufacturing processes. While sophisticated and dedicated quality inspection systems are typically in place in production environments, they still leave room for erroneous quality control outcomes. Besides in-line or post-production quality inspection, manufacturers can exploit experience gained from historical records of past inspections to predict future defect rates. This paper presents the development of a predictive quality modelling approach, which capitalises on such historical data and domain knowledge, to predict defect rates in new production orders. Employing appropriate encoding of knowledge through data pre-processing and applying regression type of machine learning, the proposed approach is validated on a real case study from an electronics manufacturing company. The developed approach can positively contribute towards optimising consequent maintenance and warranty services and become part of a zero-defect production strategy.