Towards Leveraging End-of-Life Tools as an Asset: Value Co-Creation based on Deep Learning in the Machining Industry

TL;DR

This paper presents a deep learning system for automatic classification of worn cutting tools to enhance value co-creation in the machining industry, emphasizing sustainability and asset reuse.

Contribution

It introduces a novel deep learning approach for automatic worn tool classification, enabling better asset management and value creation in manufacturing.

Findings

Achieved a Matthews Correlation Coefficient of 0.878 for flank wear classification.

Demonstrated feasibility of deep learning for worn tool recognition using VGG-16 and Gradient Boosting.

Proposed a research agenda for holistic tool characterization and assessing business impact.

Abstract

Sustainability is the key concept in the management of products that reached their end-of-life. We propose that end-of-life products have -- besides their value as recyclable assets -- additional value for producer and consumer. We argue this is especially true for the machining industry, where we illustrate an automatic characterization of worn cutting tools to foster value co-creation between tool manufacturer and tool user (customer) in the future. In the work at hand, we present a deep-learning-based computer vision system for the automatic classification of worn tools regarding flank wear and chipping. The resulting Matthews Correlation Coefficient of 0.878 and 0.644 confirms the feasibility of our system based on the VGG-16 network and Gradient Boosting. Based on these first results we derive a research agenda which addresses the need for a more holistic tool characterization by semantic segmentation and assesses the perceived business impact and usability by different user groups.