This paper focuses on the area of design and engineering, which deals with the development and validation of the virtual model design of a component up to manufacturability. In addition, this paper deals with the identification and implementation of optimization potential in product development and in the manufacturing process.
Recognizing optimization potential in product development and implementing in the manufacturing process
Computer-aided engineering (CAE) is proven to reduce waste at the conceptual design stage, reducing physical prototyping, delivering right-first-time customized solutions, and reducing component weight. Sustainability in engineering simulation predictions requires proper material utilization and optimization throughout a manufacturing process. CAE is both a source of simulation data and a glue to the ‘digital thread’ needed to deliver manufacturing cost savings, productivity improvements, and ultimately, quality products.
Industry 4.0 has been a topic for over a decade, present in public discussion and determined in the top management agenda for many companies. Central here is the transformation process from individual, human-controlled production steps to connected, digitalized production.
Manufacturers are looking for intelligent solutions to increase quality and productivity. Hexagon’s digital twin helps companies digitally transform manufacturing processes, seamlessly connecting data from throughout the process and converging the real and digital worlds to drive continuous improvement. Hexagon’s Manufacturing Intelligence division provides solutions that utilize data from design and engineering, production, and metrology to make manufacturing smarter.
This paper focuses on the area of design and engineering, which deals with the development and validation of the virtual model design of a component up to manufacturability. In addition, this paper deals with the identification and implementation of optimization potential in product development and in the manufacturing process. This leads to increased quality and productivity in the manufacturing process and to cost savings along the manufacturing chain.
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