As manufacturing accelerates toward Model-Based Enterprise (MBE), metrology software is evolving from a standalone inspection tool into a critical enabler of the digital thread. By connecting engineering intent, manufacturing execution, and quality validation, software platforms are helping organizations move beyond disconnected workflows toward fully traceable, model-driven manufacturing.
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Tolerances are one of the most powerful and misunderstood parts of product design. A tolerance tells the manufacturer how much variation is acceptable. That simple instruction affects machining time, inspection effort, scrap risk, supplier choice, assembly quality, and production cost.
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Manufacturers continue to accelerate their digital transformation efforts, investing heavily in automated inspection technologies, connected production systems, and real-time quality monitoring.
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Metal additive manufacturing has a quality problem, but not the one people think. The industry doesn’t lack inspection methods. It lacks a credible, scalable inspection strategy. Too many teams are still trying to ‘buy certainty’ the same way they did in traditional manufacturing, inspect more, test more, document more. In metal AM, that instinct is understandable, and often self-defeating.
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In this article, Stephanie Williams, senior product specialist at composites testing instrumentation manufacturer Instron, explains how drop towers are key to Compression After Impact (CAI) testing.
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A manufacturer can invest in the most advanced metrology equipment, quality management systems, and inspection automation technology. But if no one understands the measurement workflows, calibration governance requirements, data ownership structures, or how quality functions connect to production, supply chain, and customer requirements, the investment underperforms.
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When evaluating an AI visual inspection system, most engineers focus on detection accuracy, camera resolution, and throughput. The deployment architecture question, whether inference runs at the edge or in the cloud, often gets treated as a secondary decision. It should not. Where your AI model runs determines whether your inspection system can function at all under real production conditions.
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Global supply chains are fracturing. The disruptions caused by the COVID-19 pandemic and the conflict in Ukraine and Iran have exposed the severe vulnerabilities of relying heavily on distant suppliers in China and the Far East. For manufacturers across Europe, the Middle East, and Africa (EMEA), the era of prioritising rock-bottom costs over supply security is definitively over. Today, the strategic mandate is resilience.
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For generations, industrial metrology has been rooted in fixed environments. Coordinate measuring machines occupied climate-controlled labs, laser trackers were deployed by specialists, and precision inspection often required moving parts away from production and into dedicated quality rooms. In modern manufacturing, however, that model is rapidly evolving.
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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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