Abstract
Operator assistance systems can help to reduce disturbance-related machine downtime in food production and packaging processes, especially when combined with machine learning algorithms. These assistance systems analyze the available sensor signals of the process control over time to help operators identify the causes of disturbances. Training such systems requires sufficient test data, which often are hardly available. Thus, this paper presents a study to investigate how test data for teaching machine learning algorithms can be generated by numerical simulation. The potential of using virtual commissioning (VC) software for simulating disturbances of discrete processes is examined, considering the example of a friction and collision-afflicted sub-process from an intermitting wrapping machine for confectionary. In this study the software industrialPhysics (iP) is analyzed regarding accuracy of static and dynamic friction and restitution. The values are verified by setting up virtual substitute tests and comparing the results with analytically determined values. Subsequently, prerecorded disturbances are classified, and seven selected elements are simulated in VC software, recording visual effects and switching the characteristics of sensors. The verification shows that VC software is generally adequate for the assigned task. Restrictions occur regarding the computing power required of the built-in physics engine and the resulting reduction of the machine to be simulated.
Highlights
Food production and packaging processes are strongly affected by volatile properties of the processed biogenic materials and the complex behavior of flexible packaging materials
The model was built within the virtual commissioning (VC) software
Results of the Model Building Process through the machine sub-system, constructive adjustments were made in the relevant CAD-data, Atthe first,physics the model was built within the VC software
Summary
Food production and packaging processes are strongly affected by volatile properties of the processed biogenic materials and the complex behavior of flexible packaging materials. According to Allais et al the process-relevant sources of knowledge can be represented by using three different kinds of models: models for human expertise, models for data or physical models [3]. The integration of these models into specific assistance systems for food processing have already been pursued in several, mostly scientific approaches; e.g., for processing of dairy (as proposed by Perrot et al [4]) or baking products (as presented by Edoura-Gaena et al and Kansou et al [5,6]). Different root causes of disturbances can lead to distinctive, temporal switching characteristics of sensors, Machines 2020, 8, 19; doi:10.3390/machines8020019 www.mdpi.com/journal/machines
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