Product life cycle oriented optimization of the manufacturing phase and the use phase of precision tools

Key Info

Basic Information

Duration:
01.01.2020 to 31.03.2022
Organizational Unit:
Chair of Manufacturing Technology, Technology Management in Manufacturing
Funding:
German Research Foundation DFG
Status:
Closed

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+49 241 80 24995

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  Production Scenario

Precision tool manufacturers are facing a global shortage of critical raw materials as well as increasing competition from companies in the East Asian region. These factors are leading to increasing cost pressure on German tool manufacturers, who produce 15% of the world's precision tools. Furthermore, companies are required by the European Union to document and evaluate measures pursued to improve the ecological balance of the products manufactured in sustainability reports. To secure the competitive position, it is necessary to reduce the costs and environmental impact caused by precision tools. A focus on the manufacturing phase is not sufficient in this respect, since a large part of the costs and environmental impact caused by a product is incurred in its use phase.

Therefore, when evaluating the manufacturing processes used to produce tools, it is necessary to evaluate the entire life cycle of the products being manufactured. To this end, a methodology is being developed that enables process sequences to be designed according to economic and ecological criteria, taking into account the influences of the manufacturing phase on the use phase of the precision tools manufactured. The methodology enables technology planners to improve the life cycle assessment of tools by adapting process sequences for the manufacture of redesigned precision tools without threaten economic efficiency. A new ecological-economic assessment model to be developed enables process sequences to be evaluated in terms of their cost and environmental impact. Thus, for the first time, the methodology allows the derivation of generally applicable design rules for ecologically and economically optimized process sequences.