Optimizing AI-Driven Production in Industry 4.0: A Morphological Box and Taxonomy Approach

This paper presents a novel framework for integrating artificial intelligence (AI) into Industry 4.0 environments by systematically transforming a data-centric machine learning (ML) pipeline into a morphological box through an iterative taxonomy development process. Building on established methodologies, the framework quantifies each pipeline component using attributes such as cost, time, availability, and complexity. A case study involving a small-to-medium enterprise (SME) regrinding company demonstrates the practical application of the framework in automating binary image classification of machine tools. The results illustrate that the interdisciplinary approach not only streamlines the decisionmaking process but also aligns AI deployment strategies with overarching production goals, offering a robust foundation for future research and industrial implementation.