| Abstract |
Manufacturing processes are highly automated and standardized. However, the newest information technology, such as machine learning, is often not utilized throughout the entire product lifecycle. The active integration of smart and innovative solutions in the manufacturing process, which is a key task in Industry 4.0, results in benefits ranging from increased visibility into operations, to substantial cost savings, to faster production times. Using sensors for data collection and cloud computing for storing and organizing makes data usable in the analysis and implementation of suitable machine learning models. This enables manufacturers to improve production, optimize operations, and gain a better un- derstanding of the product and manufacturing phases. For example, the product test phase and decisions within it could be completely automated by machine learning algorithms to facilitate large production volumes. Every hydraulic product analyzed in this thesis goes through a specified test to ensure high quality results. A machine learning model that can decide a priori whether a piece can or cannot pass a test can significantly shorten testing time. A reduced production time subsequently results in lower production costs.
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