ADVANCED MATERIALS & PROCESSES | SEPTEMBER 2025 20 to respond dynamically to shifts in demand. For example, if a defense contractor needs to pivot from low-rate aerospace production to field-repair components, they can do so with the same equipment and workforce. Similarly, commercial manufacturers can respond to supply chain needs, rampups, or product design changes without needing to fabricate new dies or retool their lines. CURATING AND PRESERVING MANUFACTURING KNOWLEDGE In conventional manufacturing, much of the process knowledge is stored in the experience of metalworkers, welders, machinists, toolmakers, and engineers, many of whom are nearing retirement. When they leave the workforce, they often take decades of knowhow and industry-specific insight with them. This phenomenon poses a serious challenge to knowledge continuity and process control. The RoboCraftsman addresses this by capturing not just geometry but complete process intelligence. Each formed part is linked to a digital record that includes toolpaths, robot sensor data, scan data, and quality assessments. These records function as “digital tooling,” which preserves the recipe for how each part was made and why. This data-rich manufacturing history is searchable, transferable, and repeatable. It allows cloning a successful process at a different location or with a different material by drawing from prior runs. With continued use, this digital knowledge base becomes a form of institutional memory that persists and grows, even as personnel change. Combined with machine learning, these digital traces improve forming algorithms and reduce the need for trial-and-error tuning. And for materials science teams, this trove of data opens new possibilities for linking micro-structure evolution to in-process variables, unlocking better models and new alloy-processing strategies. LOOKING AHEAD: TOWARD AUTONOMOUS, DISTRIBUTED MANUFACTURING The RoboCraftsman system represents a shift in how metal parts can be formed, finished, and validated. It combines the principles of software- defined manufacturing with the practical realities of physical production by bringing together robotics, real-time sensing, AI, and process control in a portable, scalable package. Machina Labs is still early in this journey. With every deployment, the company is pushing the boundaries of what’s possible in sheet metal manufacturing (Fig. 5). This includes forming novel geometries such as conformal shapes, engineered surface textures, and lightweighted structures with non-uniform wall thicknesses or integrated ribs. These complex designs, once considered impractical or cost- prohibitive with traditional tooling, are now achievable through software- defined toolpaths and adaptive forming strategies. The team is also advancing beyond individual parts into full assemblies. As more operations are integrated into the RoboCraftsman, they are actively exploring how to incorporate processes like robotic welding, friction stir welding, and additive manufacturing (AM) directly within the same cell. This convergence of processes will enable more complex, multi-material assemblies to be fabricated in a single, reconfigurable setup. Another critical area of focus is expanding material capabilities. The company is exploring how RoboForming can be applied to difficult or advanced alloys, i.e., those with low ductility, anisotropic behavior, high strain hardening, or hard tempers like T6 aluminum alloys. Forming these materials at room temperature requires precise control over strain paths, force feedback, and heat treatment, all of which are active areas of R&D. The goal is to make high- performance materials as accessible and adaptable as common metals in digital manufacturing workflows. As industries push for greater agility, resilience, and localization in manufacturing, Machina Labs believes the RoboCraftsman ecosystem offers a pathway toward truly autonomous, Fig. 5 — Examples of different types of parts being formed at Machina Labs. (a) Lettering and signage. (b) Truck outfitted with RoboFormed panels. (c) Advanced structures for aerospace applications. (d) Artificial wood texturing. (e) Forms made from scanned faces. (f) RoboForming using sheets that were friction stir welded together.
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