Bringing Historical Process Data to Life: Unlocking AI's Goldmine with Neural Networks for Smarter Manufacturing

This document is a working paper published for early dissemination and public access via Zenodo. It has not undergone formal peer review. The content reflects the author's original technical work, insights, and applied use of machine learning in real-world domains. This version is archived for academic and professional visibility and may be updated in the future based on community feedback or further development. The content was originally published as a technical article on https://prakashkota.com on March 16, 2025, and has been reformatted into this working paper for citation and archival purposes. Abstract This article explores the potential of leveraging historical process data in manufacturing through the application of machine learning, particularly neural networks. It demonstrates how vast archives of industrial data, often underutilized, can be transformed into predictive models to enhance process optimization, reduce costs, and improve decision-making. By training AI models on existing datasets, manufacturers can achieve real-time insights into product quality and process performance, leading to more efficient and intelligent operations.