Abstract
The performance indicator, Overall Equipment Effectiveness (OEE), is one of the most important ones for production control, as it merges information of equipment usage, process yield, and product quality. The determination of the OEE is oftentimes not transparent in companies, due to the heterogeneous data sources and manual interference. Furthermore, there is a difference in present guidelines to calculate the OEE. Due to a big amount of sensor data in Cyber Physical Production Systems, Machine Learning methods can be used in order to detect several elements of the OEE by a trained model. Changeover time is one crucial aspect influencing the OEE, as it adds no value to the product. Furthermore, changeover processes are fulfilled manually and vary from worker to worker. They always have their own procedure to conduct a changeover of a machine for a new product or production lot. Hence, the changeover time as well as the process itself vary. Thus, a new Machine Learning based concept for identification and characterization of machine set-up actions is presented. Here, the issue to be dealt with is the necessity of human and machine interaction to fulfill the entire machine set-up process. Because of this, the paper shows the use case in a real production scenario of a small to medium size company (SME), the derived data set, promising Machine Learning algorithms, as well as the results of the implemented Machine Learning model to classify machine set-up actions.
Highlights
With a turnover of 103 billion euros, the metal industry is one of the largest German industrial sectors, which is characterized by volatile market conditions and a high level of competition [1,2]
Due to a big amount of sensor data in Cyber Physical Production Systems, Machine Learning methods can be used in order to detect several elements of the Overall Equipment Effectiveness (OEE) by a trained model
Changeover time is one crucial aspect influencing the OEE, as it adds no value to the product
Summary
With a turnover of 103 billion euros, the metal industry is one of the largest German industrial sectors, which is characterized by volatile market conditions and a high level of competition [1,2]. It is essential to ensure the profitability of a production company by means of efficient production planning and control and the resulting high level of responsiveness and flexibility. It is necessary to optimally align production planning with market and customer requirements and maintain plant efficiency at a high and stable level [5]. The acquisition of real-time data provides an adequate response to the requirements and great potential for production planning and control in order to optimize the scheduling and coordination of work orders, as well as to react immediately to disturbance variables or unforeseen deviations from the plan [4,6]. Transparency and improvement of human decision-making processes is necessary and can be provided by data driven methods [7]
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