Measurement data holds immense potential, but it only delivers real value when properly harnessed and flows seamlessly across plants, suppliers, and engineering/manufacturing systems. PolyWorks 2026 is creating a networked quality-management ecosystem built on centralized data, collaboration, and interoperability.
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With the launch of PolyWorks 2026, InnovMetric is driving the transition toward a connected ‘ultimate inspection lab’ where dimensional inspection data flows seamlessly across plants, suppliers, and engineering/manufacturing systems.
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Mitsubishi Electric Corporation has announced the development of a new edge-based digital twin technology capable of correcting machining errors in real time on CNC machine tools, marking a significant advance in precision manufacturing and smart factory automation.
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If the digital transformation is being hampered, it is usually due to technical reasons: heterogeneous legacy systems, fragmented data landscapes, and limited interoperability make it difficult to adopt new technologies.
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Zero-defect manufacturing has long stood as one of the most ambitious goals in industrial quality control. For decades, however, it remained more of a guiding philosophy than a practical reality, limited by sampling inspection, human oversight, and delayed feedback from production.
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Siemens recently announced Digital Twin Composer, a new software solution that builds Industrial Metaverse environments at scale, empowering organizations to apply industrial AI, simulation and real-time physical data to make decisions virtually, at speed and at scale.
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Aegis Software has announced that it has completed the acquisition of Simio, a provider of Digital Twin Simulation software and Advanced Planning and Scheduling (APS) to an attractive and resilient set of end-markets.
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Digital Twin Consortium (DTC) has announced that Advanced Micro Devices, Inc. (AMD) has joined as a member. AMD will bring its cutting-edge AI processing capabilities and innovative edge computing solutions to accelerate AI-powered digital twin systems development and deployment across industries.
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Aiming to overcome barriers that prevent digital twins from delivering on their promise to improve manufacturing, the University of Michigan and Arizona State University are inviting industrial partners to participate in a new Center for Digital Twins in Manufacturing.
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Traditional factory planning software often relies on model-first approaches that can be slow to implement and don’t necessarily reflect real-world conditions.
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