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Manufacturing Technology Insights | Tuesday, May 04, 2021
Engineers can now explore the design space more thoroughly and interactively, allowing them to improve next-generation products without incurring prohibitive computing costs or time.
FREMONT, CA: Hexagon AB, a global leader in sensor, software, and autonomous solutions, reported that it has acquired CADLM SAS, a pioneer in combining Artificial Intelligence (AI) and machine learning with Computer-Aided Engineering (CAE), to revolutionize the impact of simulation in product development processes and lifecycles.
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“The convergence of CAE with advances in data management, AI, machine-learning and an increasingly connected manufacturing lifecycle is transforming the industry’s ability to address increasingly complex design challenges with rapid innovation and increased productivity,” says Hexagon President and CEO Ola Rollen. “CADLMs AI knowledge and technology further strengthen our Smart Manufacturing solutions portfolio, putting data to work beyond the early design phase to improve product design innovation, manufacturing productivity, product quality and environmental sustainability through reductions in material waste.”
CADLM, based in France, has been developing computational design and optimization methods for industrial goods and processes since 1989 and has been developing AI and machine learning solutions since 2014. Its ODYSSEE software platform uses AI and machine learning to generate accurate, predictive models of a product using real-world sensor data and physics-based simulation data at low computing power levels. The combination allows for quicker, more effective simulations of complex, multi-physics phenomena like automotive crash and safety, allowing researchers to characterize and understand real-world product behavior completely.
Engineers can now explore the design space more thoroughly and interactively, allowing them to improve next-generation products without incurring prohibitive computing costs or time. Furthermore, manufacturers may use image recognition, predictive modeling, and fault prediction to solve issues including downtime, throughput, efficiency, and versatility in the manufacturing process, thanks to the widespread use of the digital twin beyond the early design phase.
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