Insights

AI assisted CAM reduces NC programming time by 40–75% for complex parts by automating feature recognition, strategy selection, and parameter suggestion—while keeping humans in control of risk and trade offs. In this article, I explore how this occurs along with the role of AI to automate routine tasks while empowering programmers to focus on critical trade-offs and decision-making.

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While AI can process vast amounts of data, optimize plansand surface recommendations at a speed no human can match, it still lacks the contextual judgment needed to navigate ambiguity, trade-offs and real-world business consequences. This article argues that in complex industrial environments like supply chains and manufacturing, the best outcomes come from combining AI’s analytical power with human oversight, accountability and cross-functional decision-making.

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This article explores how augmented reality delivers measurable improvements in both quality and productivity by transforming how operators interact with manufacturing data.

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In my previous two articles, I delved into the first four pillars of a closed-loop, self-sustainable green factory of the future – Automate, Optimize, Innovate and Integrate. In this article, I examine the final pillar of the green factory of the future.

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