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The rise of digital twins in the manufacturing industry is accompanied by new possibilities, like process automation and condition monitoring, real time simulations and quality and maintenance prediction are just a few advantages which can be realized. This paper takes a novel approach by extracting the fundamental knowledge of a data set from a production process and mapping it to an expert fuzzy rule set. Afterwards, new fundamental augmented data is generated by exploring the feature space of the previously generated fuzzy rule set. At the same time, a high number of artificial neural network (ANN)models with different hyperparameter configurations are created.
The best models are chosen, in line with the idea of survival of the fittest, and improved with the additional training data sets, generated by the fuzzy rule simulation. It is shown that ANN models can be improved by adding fundamental knowledge represented by the discovered fuzzy rules. Those models can represent digitized machines as digital twins. The architecture and effectiveness of the digital twin is evaluated within an industry 4.0 use case.
Cylindrical grinding is an important process in the manufacturing industry. During this process, the problem of grinding burn may appear, which can cause the workpiece to be worthless. In this work, a machine learning neural network approach is used to predict grinding burn based on the process parameters to prevent damage. A small dataset of 21 samples was gathered at a specific machine, grinding always the same element type with different process parameters. Each workpiece got a label from 0 to 3 after the process, indicating the severity of grinding burn. To get a robust neural network model, the dataset has been scaled by augmentation controlled by grinding experts, to generate more samples for training a neural network model. As a result, the model is able to predict the severity of grinding burn in a multiclass classification and it turned out that even with little data, the model performed well.
Smart Condition Monitoring for Industry 4.0 Manufacturing Processes: An Ontology-Based Approach
(2019)
Within the 21st century data are the new raw material, is what Ms. Angela Merkel said at the CEBIT conference in 2016. Digitization, what means data-analysis in real-time as well as fast and steady changes in the technological environment, is a key factor nowadays. New processes, new daily tasks and new know-how are needed to survive in a steadily changing world. In succession, companies and private households need to adapt. Otherwise, these will be selected according to Darwin’s theory of evolution. “Survival of the smartest” – as the mantra of today. While some companies are already familiar with the changes introduced by industry 4.0, others are still challenged with catching up industry 3.0. In a smart world it is important to know in which digital maturity status the company is staying and where the company sees itself in the future. But especially for smaller companies the obstacles of realizing industry 4.0 or digitization are defined by high investment costs, a lack of human resources and high requirements in data protection. Therefore, the following thesis is handling this topic specifically within procurement and gives answers to the following questions: What is procurement 4.0? Do the massive changes mean the procurement-endgame? What are the new challenges that procurement has to face and what are the new competencies a purchaser has to adopt? On the basis of a survey the procurement department of the Sto SE & Co. KGaA is classifying the own digital Status quo in the digital capability maturity model. On the basis of the current project “Implementing a Supplier-Relationship-Management and Collaboration system (SRM)” the thesis is answering to the research question: “What approach is suitable to introduce an SRM-and Collaboration tool and how to implement this tool specifically in procurement?”