Project Details
Projekt
DeCap - Design for Capabilities - Data Science and Artificial Intelligence in Capability-Oriented Product Engineering for Sustainable Production
Subject Area
Engineering Design, Machine Elements, Product Development
Term
since 2024
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 543074930
Material circularity shall be carried out with as little processing effort as possible and thus at the highest possible level of product structure. Closing material loops on such a high level requires Design for Assembly, Disassembly and Reassembly (DfADR). According to VDI/VDE 2206, a product engineer early needs a mental model of the future production system. Capabilities of employees in Assembly, Disassembly and Reassembly (ADR) are particularly relevant. The work steps of dis- and reassembly can never be planned completely in advance, but in many cases must be decided by the individual employee directly in the situation. The product engineer therefore has to ask oneself: Is it possible to achieve high circularity rates through ADR with current human capabilities? Or does the new circular product require dedicated learning processes to develop the required skills among employees? The approach of Design for Capabilities (DeCap) extends DfADR by hybrid decision support and combines approaches from Simultaneous Engineering with Data Science and Artificial Intelligence (DS/AI). Thus, the current human capability profile is assessed and future capabilities are anticipated on the basis of extreme data from ADR processes. Required human capabilities can be interpreted as company-specific restrictions for sustainable product concepts and limit the available solution space. Capabilities of employees are typically traced back to basic skills, i.e. to a level that can be measured by simple indicators (e.g., Methods-Time Measurement (MTM) or work on human-robot collaboration). However, situational decision capabilities cannot be assured in advance. Decision-making requires the interpretation of a large amount of specific process data with the risk of interpretation errors and situational bias. Data is large in terms of the amount of data sets, includes errors from sensors and communication, refers to heterogeneous products and processes, is limited due to access restrictions - it is extreme. In this project, a metadata model will be developed that enables an ontology-based matching between product features on the one hand and capabilities of employees on the other hand. Thus, sustainability of product concepts, their production and remanufacturing will be assessed at an early stage of product creation. Resulting product concepts will profit from this feasibility analysis: Do I have the right employees on board or where do I need to train them so that remanufacturing will have an economic benefit? For this, human capabilities shall be extracted from extreme data through data analytics and semantic enrichment.
DFG Programme
Priority Programmes
Subproject of
SPP 2443:
Hybrid decision support in product creation
