Abstract: Understanding and tailoring the visual elements of a developing product to evoke desired perceived qualities and a positive response from the consumer is a key challenge in industrial design. To date, computational approaches ...

Abstract: In topology optimization using deep learning, the load and boundary conditions represented as vectors or sparse matrices often miss the opportunity to encode a rich view of the design problem, leading to less than ideal ...

Abstract: Scalar fields, such as stress or temperature fields, are often calculated in shape optimization and design problems in engineering. For complex problems where shapes have varying topology and cannot be parametrized, ...

Abstract: Metals-additive manufacturing (MAM) is enabling unprecedented design freedom and the ability to produce significantly lighter weight parts with the same performance, offering the possibility of significant environmental ...

Abstract: Using deep learning to analyze mechanical stress distributions is gaining interest with the demand for fast stress analysis. Deep learning approaches have achieved excellent outcomes when utilized to speed up stress ...

Abstract: This research presents a method of optimizing the consolidation of parts in an assembly using metal additive manufacturing (MAM). The method generates candidates for consolidation, filters them for feasibility and structural ...

Abstract: A multitude of studies in economics, psychology, political and social sciences have demonstrated the wisdom of crowds (WoC) phenomenon, where the collective estimate of a group can be more accurate than estimates of ...

Abstract: The ability to track human operators' hand usage when working in production plants and factories is critically important for developing realistic digital factory simulators as well as manufacturing process control. We ...