Machine Learning in Predictive Quality Assurance

Santhosh Bussa

doi:10.55544/sjmars.1.6.8

Authors

Santhosh Bussa Independent Researcher, USA.

DOI:

https://doi.org/10.55544/sjmars.1.6.8

Keywords:

Predictive Quality Assurance, Machine Learning, Quality Control, Supervised Learning, Unsupervised Learning, Predictive Maintenance, Automation Pipelines

Abstract

Predictive quality assurance (PQA) uses machine learning (ML) for enhancing the quality assurance process from traditional reactive systems to proactive predictive systems: predicting and preventing defects toward quality across the board. This paper is an exploration of the ML techniques of PQA with a focus on supervised, unsupervised, and reinforcement models, along with their interaction with real-time quality control systems. Techniques of data preprocessing, dealing with imbalanced datasets, and validation of the model in detail are discussed. Major applications in manufacturing, automotive, and electronic areas are described, together with ethical concerns and challenges. Future directions focus on self-governing quality assurance systems that are assisted by high AI algorithms.

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