In the manufacturing industry, automated optical inspection aims to improve the detection and classification of anomalies by utilizing artificial intelligence and computer vision techniques to enhance quality control processes and minimize production defects. However, this automated system faces significant challenges, particularly regarding the detection of anomalies due to predominance of normal instances over defected ones. Addressing this imbalance is crucial for effective real-time anomaly detection particularly in images captured by Airbag Sensors among other automotive parts. Earlier contributions in domain-specific fields commonly relied on traditional computer vision methods, while recent systems are increasingly using deep learning techniques. Utilizing various data augmentation techniques ensures a more balanced representation of anomalies in the dataset, thereby enhancing the accuracy of the detection process. Moreover, it also enhances the robustness and generalization of the anomaly detection model by exposing it to a more diverse range of instances during training. Such work has not been carried out to augment Airbag Sensor images for analysis through a deep learner. Accordingly, this paper introduces a framework that employs data augmentation techniques for Convolutional Neural Networks (CNNs). The proposed system, based on data augmentation and CNN, significantly improves the performance for anomaly detection in Airbag Sensor images with a classification accuracy on the unaugmented dataset being 53 % which improves to 90% with augmentation.

Reusing and managing existing knowledge from available documents is crucial for success in the factory planning domain. By leveraging Artificial Intelligence (AI) and Question Answering (QA) systems, users can query a document corpus through a chat-based application and receive precise answers. The recent advancements in Large Language Models (LLMs) and their linguistic capabilities present new opportunities for such applications. Utilizing the methodology of Retrieval Augmented Generation (RAG), document sections are provided to the LLM based on user queries. However, existing RAG implementations that use vector databases as document repositories face limitations when answering questions that extend beyond the text content of the documents. To address this issue, this paper proposes a concept to enhance RAG systems by integrating a Knowledge Graph (KG) constructed from the document structures.

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.

The presence of class imbalance, denoting a dis-proportionate distribution of class instances in a dataset, has emerged as a significant challenge in the era of Deep Learning (DL) where models crave abundance in data. This issue is pervasive in various real-world applications, where certain classes exhibit limited data representation. This problem is frequently encountered when dealing with image data, which exhibits an imbalanced distribution, with one class significantly outnumbering the others. Failing to address class imbalance introduces bias in machine learning and deep learning models, favoring the majority classes and leading to subpar performance for the minority classes. This research specifically delves into the recurrent problem in the context of image data, that is “class imbalance”. The research comprehensively explores the existing challenges based on data pre-processing, algorithmic techniques, hybrid methodologies, and state-of-the-art solutions.

The amount of necessary information and knowledge to fulfill the tasks within the factory planning domain is rapidly growing. Currently, data and documents containing relevant information are stored as single artifacts in various established systems which makes information gathering time-consuming. Therefore it would be helpful to connect the existing artifacts so that it is possible to get all information of different origins at a glance. As Semantic Web technologies are recently evolving from academic research to industry applications, Knowledge Graphs (KG) as one of their specific implementations seem to be a suitable option to manage and connect these information artifacts in a sustainable matter. This can effectively support knowledge management in factory planning and provides machine-readable access to domain information that can be used by currently emerging Artificial Intelligence (AI) applications. This paper focuses on the conceptual challenges related to the creation of a knowledge graph within the factory planning domain and proposes a concept for a platform to collect and access existing information and experiences from domain experts.