A study of micromanufacturing process fingerprints in micro-injection moulding for machine learning and Industry 4.0 applications

Mert Gülçür,Ben Whiteside

doi:10.1007/s00170-021-07252-7

Mert Gülçür, Ben Whiteside

Open Access

https://doi.org/10.1007/s00170-021-07252-7

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Abstract

This paper discusses micromanufacturing process quality proxies called “process fingerprints” in micro-injection moulding for establishing in-line quality assurance and machine learning models for Industry 4.0 applications. Process fingerprints that we present in this study are purely physical proxies of the product quality and need tangible rationale regarding their selection criteria such as sensitivity, cost-effectiveness, and robustness. Proposed methods and selection reasons for process fingerprints are also justified by analysing the temporally collected data with respect to the microreplication efficiency. Extracted process fingerprints were also used in a multiple linear regression scenario where they bring actionable insights for creating traceable and cost-effective supervised machine learning models in challenging micro-injection moulding environments. Multiple linear regression model demonstrated %84 accuracy in predicting the quality of the process, which is significant as far as the extreme process conditions and product features are concerned.

Highlights

Data-rich manufacturing, Internet of Things (IoT), and cloud manufacturing are becoming more and more prominent every day and are addressing challenges such as product quality improvement, cost-effectiveness, and sustainability for many different sectors in industry and research
The relationships between process fingerprints (PF) and Microreplication efficiency (Mμ) have been quantified using the R-squared (R2) statistical parameter calculated from linear correlations, which is called as coefficient of determination (COD)
A ranking system have been made to see which PF is linked to the process/product quality better, and discussions are provided for the explanation of physical effects

Summary

Introduction

Data-rich manufacturing, Internet of Things (IoT), and cloud manufacturing are becoming more and more prominent every day and are addressing challenges such as product quality improvement, cost-effectiveness, and sustainability for many different sectors in industry and research. As opposed to the reports available, we provide and discuss the required rationale for selection of such PFs and their physical interpretations This approach can by-pass a number of filtering or data sorting steps, since the selected quality proxies are well-thought and closely linked to the products in the first place. We believe that this systematic extraction of information patterns using process fingerprinting is essential for deep learning and smart manufacturing applications.

Process fingerprints in micro-injection moulding

Process data collection in μIM

Part design and process details

Results and discussion

Conclusions