May 2026 Volume 8

EQUIPMENT & TECHNOLOGY

Geometry changes. Surface conditions become more difficult. Detecting issues such as non-fill, folds, or inclusions may require more than a conventional 2D image. That is where systems like MAS QCPlus®, referenced by Adaptec, begin to create a different kind of value. Trizzano is clear that such systems are not meant for standard billet loading. “QCPlus® would not be used for standard bin picking of billets,” he says, because the quality needs at that stage are limited mainly to verifying diameter and length. Where a system like QCPlus® adds value is after the forging process, when it can support automated inspection of critical dimensions, automated defect detection in a 3D environment, and even intelligent process control for grinding or polishing operations followed by reinspection to confirm the result. Jacob Cipriano, CTO & Business Development Manager of Manufacturing Automation Systems (MAS) notes that QCPlus® is used today primarily for “in line and near line dimensional inspection, defect detection, and data driven process feedback within forging environments,” with the system most commonly applied to cold or post-process measurement to verify dimensional conformity and monitor process stability without relying on manual gauges or subjective inspection. That progression lines up with broader work underway in manufacturing metrology. NIST has been leading and supporting efforts to develop standards and performance metrics for 3D imaging systems used in manufacturing automation, including work related to bin-picking systems, sensor performance standardization, and guidance for selecting 3D imaging systems. That kind of work matters because advanced inspection only becomes practical at scale when plants can compare, validate, and trust the sensing systems they are using 4,5 . The most sustainable path is usually to start with better handling and basic sensing, then add more advanced inspection where scrap, rework, or customer quality demands justify the investment. Cipriano frames the business case in practical terms: “Automated 3D inspection helps compress the feedback loop between forging and dimensional verification, allowing operations to catch drift earlier, reduce scrap and rework, and return presses to stable production faster after changeovers.” Where AI Fits — and Where It Doesn’t AI is part of the conversation now, but forging companies are right to be skeptical of inflated claims. The forge shop is visually noisy and operationally messy. Lighting changes. Surface appearance varies. Scale interferes. Parts are not always presented consistently. Traditional rule-based vision can struggle in those conditions, which is why AI-based inspection and classification have attracted attention. But Trizzano offers a grounded way to think about where the technology stands today. “There are so many recent developments in sensor and AI technology that the limitations are not the ability to do the job, but rather the amount of time and money required to make it happen,” he says. That may be the clearest summary of the current moment in forging automation. The question is no longer whether AI can help. In many cases, it can. The real question is whether the total system—sensors, software, integration, training data, engineering labor, and ongoing support—makes operational and economic sense for a given plant.

According to Cipriano, from the MAS perspective, “the longer term opportunity is less about AI as a standalone feature and more about closed-loop manufacturing: using inspection data as an active input to downstream robotic actions and, eventually, upstream process adjustments.” Trizzano explains that Adaptec’s current focus remains on “economically feasible solutions centered primarily on vision for robot guidance, while continuing to work with partners on more advanced AI-based inspection and process-control applications.” That caution is consistent with broader industry analysis. McKinsey has identified AI-based visual inspection as a promising manufacturing use case and has reported that, in suitable applications, image-recognition-based inspection can substantially improve defect detection compared with human inspection. But those gains depend on having the right problem definition, sensing foundation, and implementation discipline 6 . In other words, the industry is moving forward—but carefully. The Human Side of the Learning Curve The technical challenge in forging automation is real. But the human challenge can be just as important. A successful automation project changes how a plant works. Operators need to understand how the cell behaves and how to recover from faults. Maintenance teams need new skills. Engineers need to think more explicitly about variation that used to be absorbed through operator experience. And management needs to think about staffing not just in terms of headcount, but in terms of capability. Forging industry suppliers identify a reality many forgers know too well: labor instability is pushing automation from a long-term idea into a near-term operating necessity. That pressure is not unique to forging. Deloitte and The Manufacturing Institute found that attracting and retaining talent is a primary business challenge for manufacturers, reinforcing why more plants are treating automation as part of a broader labor and operating strategy rather than a stand-alone capital project 2,3 . At the same time, FIA member suppliers have identified an important point that goes beyond labor. “Properly implemented automation and controls can also improve quality by making a process less subjective and more repeatable. In many shops, the ability to fill a forging properly still relies heavily on operator judgment developed over years of experience. Better controls and automation can reduce some of that burden by making more of the process objective and consistent,” said BiLLy Paris, Director of Sales, Aftermarket & Rebuilds at Ajax/CECO/Erie Press. That does not remove the need for skilled people. It changes where those skills are applied. Hybrid Automation Requires Discipline One of the most important cautions mentioned by suppliers pertaining to material handling is when there is a hybrid environment, where manual operations and automated functions exist close together. That arrangement can be workable, but only with the right safeguards. “If hand operations are taking place near automated equipment, the control logic has to ensure that human activity always overrides automatic movement. If tongs are in the die space, nearby automation must pause. If a person is performing a task, the machine cannot assume the workspace is clear, said Paris.

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