ORNL Closes the LFAM Loop

Large-format additive manufacturing depends on a narrow thermal window: When a new layer is deposited, the layer below must be hot enough to bond but cool enough to hold its shape, a balance that traditionally required constant human supervision. Researchers at the U.S. Department of Energy's Oak Ridge National Laboratory built on an existing model to improve fault detection, adding a controller that monitors layer temperature for a variety of machines and materials through low-cost thermal cameras mounted at the machine's nozzle. Computer vision identifies deviations and adjusts print speed automatically. In a full-scale test, the system detected a 30% temperature shortfall and corrected it without human intervention.

The Bottom Line: This research suggests that a combination of computer vision and inexpensive thermal cameras can enable manufacturers to produce large, custom parts with fewer defects, potentially reducing waste, lowering costs and strengthening domestic additive manufacturing.

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This Week in Manufacturing Technology

CISA Expands PLC Warning to Siemens, Schneider Devices 🔗 — CISA

CISA and federal partners updated a joint advisory warning that Iranian-affiliated actors are actively exploiting internet-connected PLCs across U.S. critical infrastructure, expanding the scope from Rockwell Automation to include Schneider Electric and Siemens devices.

Connection: OT engineers and controls engineers whose facilities use any internet-connected PLC from these manufacturers now face an explicitly expanded threat surface, and the advisory provides updated indicators of compromise and guidance on validating PLC project files for unauthorized changes.

ML Model Predicts Milling Stability Where Physics Falls Short 🔗 — Modern Machine Shop

A k-nearest neighbors machine learning algorithm can model milling stability with process damping effects, a phenomenon difficult to quantify with physics-based models alone.

Connection: Process damping increases the stability boundary at low spindle speeds, but its energy dissipation mechanism resists accurate physics-based modeling. Machining engineers who currently rely on conservative depth-of-cut limits at low spindle speeds may be able to use a physics-informed machine learning model to predict a more accurate stability boundary.

Siemens, HD Hyundai Bring AI to U.S. Shipbuilding 🔗 — Siemens News

Siemens and HD Hyundai signed a nine-figure deal to develop a repeatable AI-powered digital shipyard model using the Siemens Xcelerator portfolio, targeting modernization of U.S. commercial and defense shipbuilding operations.

Connection: U.S. shipyards have historically operated without integrated digital threads connecting design, engineering, production planning and lifecycle management, leading to costly rework and slow production readiness. This collaboration aims to create a scalable model that connects those functions and can adapt to shipyards at different stages of digital maturity.

The Connected Team

AI Is the Present, Not the Future

“

The path to AI adoption doesn’t have to be overwhelming. Focus on small, measurable experiments, leverage off-the-shelf tools and make mistakes. Position your shop for success in an AI-driven world or, in fact, any technology disruptor that occurs.

— Mina Girges, IT Leader

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