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Learning Factory Configuration Tool: An Approach for Preserving the Value of Educational Learning Factories

Frielinck, R. (2023) Learning Factory Configuration Tool: An Approach for Preserving the Value of Educational Learning Factories.

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Abstract:Learning Factories (LFs) are existing and proven systems of transferring knowledge in an active and practical way. Using a physical production process as a learning tool facilitates a constructive and contextualised learning process. Due to the fourth industrial revolution, many new and innovative manufacturing processimproving technologies enter the market. Existing Learning Factories depreciate over time due to the fact that they are frequently constructed with clearly defined specific objectives in mind. Currently this is a great disadvantage of such capital intensive and rigid buildings. Therefore a tool that preserves and maintains the value of LFs must be developed. To this end, a literature study has been conducted on Active Learning Methods (ALMs), Learning Factories and the relationship to its Learning Subjects (LS). Second, the implementation of changeability is identified as a method for preserving the value. Which is accomplished through a modular design that is interoperable and independent of equipment types. When planning a factory reconfiguration, it is difficult to maintain an overview of all the elements, including the Learning Subjects offered, the products to be manufactured, the available budget, and the size of the factory floor. Therefore, the findings of the literature review are incorporated into a Learning Factory Configuration Tool (LFCT). This LFCT provides the user with a list of equipment based on the parameters provided. These parameters consist of the Educational Product (EP) that must be manufactured, the corresponding LSs, the available budget, and the size of the factory floor. The user then selects the appropriate equipment models manually from the resulting equipment list. The suggested equipment list has been converted into a Learning Factory simulation. It helps to test the feasibility of different production lines according to the LFCT-suggested equipment list. To accommodate the use and expansion of the database, an equipment input form for has been created. Both the simulation and input form have demonstrated their functionality and the tool operates as expected. In the future, essential decisions should involve key stakeholders, the database should be moved to the cloud to increase its reliability, and a filtering algorithm is required for the massive output resulting from the expanded database. The ultimate goal would be to have a physical Learning Factory that can collect factory data through the strategic placement of sensors. The collected data should be connected to the LF simulation. Then, using an algorithm for machine learning, this data can be used to automatically adapt and improve the Learning Factory or to recommend changes.
Item Type:Essay (Master)
Faculty:ET: Engineering Technology
Subject:52 mechanical engineering, 81 education, teaching
Programme:Mechanical Engineering MSc (60439)
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