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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.
Digitalization is invading every aspect of our lives and modern technologies are at the helm of much disruptive change in all spheres of life. Hailed as the 4th industrial revolution every company has a mind to understand the implications of the Industry 4.0 suit of technologies and their multiple innovative applications for its operations. In this paper, we explore how the industry 4.0 transformation might affect Small and Medium sized
enterprises in Germany over a 15-year horizon. We focus on SMEs because they play a significant role in ensuring the prosperity of Germany as a global industrial and economic
powerhouse. We develop alternative pictures of the possible futures using the foresight technique of Scenario planning in which the factors that shape the business environment
SMEs and indeed all companies operate in are identified and used to build the most plausible alternative realities. The outcome is four distinct scenarios that reflect the possible growth trajectories regarding the impending transformation for SMEs.