Observational Data-Driven Modeling and Optimization of Manufacturing Processes

TL;DR

This paper presents a novel data-driven approach for modeling and optimizing manufacturing processes using observational data, enabling process control and improvement without costly experiments, demonstrated through simulations and real-world case studies.

Contribution

It introduces an integrated method for process parameter design and control variable identification using observational data, advancing manufacturing process optimization.

Findings

Effective process modeling from observational data

Successful application to tire manufacturing case

Potential to reduce experimental costs

Abstract

The dramatic increase of observational data across industries provides unparalleled opportunities for data-driven decision making and management, including the manufacturing industry. In the context of production, data-driven approaches can exploit observational data to model, control and improve the process performance. When supplied by observational data with adequate coverage to inform the true process performance dynamics, they can overcome the cost associated with intrusive controlled designed experiments and can be applied for both monitoring and improving process quality. We propose a novel integrated approach that uses observational data for process parameter design while simultaneously identifying the significant control variables. We evaluate our method using simulated experiments and also apply it to a real-world case setting from a tire manufacturing company.