Addressing the Complexity of AI Integration in Manufacturing: A Morphological Analysis

Leonid Koval, Muhammad Uzair Akmal, Saara Asif, Selvine G. Mathias, Simon Knollmeyer, Daniel Grossmann

Abstract: This paper introduces a novel methodological approach to transform a traditional model-centric machine learning pipeline into a morphological box. Utilizing a taxonomy development method, we iteratively refine a morphological box to address the complexity inherent in selecting and adjusting components within machine learning pipelines. Our method leverages a generic active learning process tailored for quality control in manufacturing, serving as a practical example. We demonstrate that decomposing the machine learning pipeline into distinct morphological box dimensions with meta char-acteristics significantly enhances decision-making clarity by reducing option complexity. This transformation is further supported by defining universal attributes-Cost, Time, Avail-ability, and Complexity-that cater to users with varying machine learning expertise. Future work will focus on empirical validation and the development of software tools to facilitate the practical application of morphological boxes in diverse machine learning pipeline contexts.