Abstract
As a consequence of the increasing digitalization of production, the availability of data from individual manufacturing processes and components is rising continuously. Digitalization affects various levels of production, such as the design of manufacturing processes, technology planning, quality monitoring, or logistics control. The contribution of this paper is to present the foundations for digitalized manufacturing process sequences and to develop data-based models and methods for the design of economically and ecologically optimized process sequences. For this purpose, a procedure for cross-process life cycle assessment was developed and carried out in order to evaluate the various ecological effects resulting from manufacturing and, in particular, different process designs. This ecological evaluation was combined with the evaluation of economic efficiency in order to provide an integrated evaluation as the basis for the design of manufacturing process sequences. In addition, the effects of the individual manufacturing process designs or process parameters on component quality were analyzed and an approach was derived for determining cross-process dependencies. The basis for these developments is a process sequence for the manufacture of pinion shafts used in electric car gearboxes. This digitalized process sequence consists of the processes soft cutting, gear hobbing, case-hardening, cylindrical grinding, and gear grinding, and over 100 components were manufactured with various process designs. This enabled an extensive amount of process and quality data to be collected throughout the process sequence. The application of the developed methods to the aforementioned process sequence underlines the high potential for the optimization of the target parameters quality, economy and ecology through cross-process analyses of digitalized manufacturing process sequences. Finally, an outlook on further developments regarding the digitalization of manufacturing process sequences is given.
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More From: CIRP Journal of Manufacturing Science and Technology
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