2018
Estimating Process Duration and Safeguard Project Planning in a One-Of-A-Kind Production Environment by the Use of Simulation Technique
Wibke Kusturica, Christoph Laroque, Deike Gliem, Jana Stolipin, Sigrid Wenzel • Winter Simulation Conference 2018, Gothenburg (Sweden)
Customized engineering and the build-up of unique products are complex tasks, where project management contains lots of uncertainties. Simulation techniques could help to evaluate and achieve improved and more robust plans during project management, but are typically not applied in industry, especially at SMEs (small and medium-sized enterprises). This paper covers some ideas of the joint research project SimCast of the Universities of Kassel and the University of Applied Sciences Zwickau. It aims at the development of a method for duration estimation of a project task during project planning. Based on the researched state-of-the-art, requirements and a planning process are described as well as a draft of the current technical infrastructure of the intended modular prototype. First plug-ins are implemented and already show possible benefits for the project management process. The paper describes possible scenarios for the use of simulation techniques in this setting, based on gained experience.
Workpiece Positioning Based on Supervised Learning Methods for Simulation-Based Optimization of Virtual Tooling Processes
Jens Weber, Sebastian Risse, Christoph Laroque • Winter Simulation Conference 2018, Gothenburg (Sweden)
The setup process, Numerical Control (NC) program configuration and the linked configuration of point of origins, workpiece position, tool ranges require high computational effort that include multiple simulation runs during the work preparation process. This contribution describes an automatic setup optimization process, including validation of position parameters using a virtual tooling machine as simulation model. In the first step, the developed simulation-based optimization approach minimizes the production time while the collision information and NC program validation are provided by the simulation. In the next step, a cluster method is applied to avoid a high number of single simulation runs, but the validation effort is still high. In order to address this point the developed system offers a selection and data reconciliation procedure using supervised learning methods to determine feasible workpiece positions.
Applications of Big Data analytics and Related Technologies in Maintenance—Literature Based Research
Jens Baum, Christoph Laroque, Benjamin Oeser, Anders Skoogh, Mukund Subramaniyan • Machines 6(4)
Digitalisation is argued to increase the efficiency of maintenance activities in a production system. One consequence of digitalisation is data deluge; this allows data analytics methods and technologies to be used. However, the actual data analytical methods and technologies used may differ, thus leading to many scientific papers on this topic. The purpose of our contribution is to find and cluster scientific papers regarding the implemented approaches relevant for use in production maintenance. Our research is based on a broad, systematic literature review consisting of a two-step search approach combined with additional filtering and classification. Based on the search results, we evaluate and visualise the potential impact of data analytics on the subject of maintenance. The results of this study broadly summarise the research activities in production maintenance, whilst indicating that the impact of data analytics will grow further. Specific methodological approaches are clearly favored.