Extruding and Drawing Machine Setters, Operators, and Tenders, Metal and Plastic

Set up, operate, or tend machines to extrude or draw thermoplastic or metal materials into tubes, rods, hoses, wire, bars, or structural shapes.

The occupation "Extruding and Drawing Machine Setters, Operators, and Tenders, Metal and Plastic" faces a moderate automation risk, calculated at 58.7%, which is closely aligned with its base risk of 59.4%. This suggests that more than half the tasks performed in this role are susceptible to automation, primarily due to the repetitive and procedure-driven nature of much of the work. Advancements in robotics, AI-driven quality control, and machine learning have enabled machines to execute many standardized tasks with precision, reducing the need for constant human intervention. However, the risk is not absolute, as certain tasks within this occupation demand skills that are currently less replicable by technology. Among the most automatable tasks are those involving measurement, setup, and operation adjustments, which lend themselves easily to sensor-based automation and programmable controls. For instance, "Measure and examine extruded products to locate defects and to check for conformance to specifications, adjusting controls as necessary to alter products" can increasingly be handled by automatic inspection systems with defect recognition algorithms. Similarly, determining setup procedures and selecting machine dies according to specifications can be automated through integration with computerized scheduling and inventory systems. Finally, starting machines and setting controls to regulate process parameters—such as vacuum, air pressure, and synchronization—can all be automated using advanced industrial control software, minimizing the need for manual intervention. Conversely, the tasks most resistant to automation typically require a blend of manual dexterity, on-the-spot decision-making, and high adaptability. Operating shearing mechanisms to cut rods, for example, still often requires real-time manual oversight due to the variability inherent in raw materials. Selecting nozzles, spacers, and wire guides demands tactile feedback and an understanding of subtle differences that machines may not yet reliably perceive. Additionally, replacing worn dies when products deviate from specifications involves not just routine action, but also problem-solving and equipment care. The bottleneck skill of originality, rated at only 2.3% and 2.0%, respectively, further underscores that while creativity and novel problem-solving are needed occasionally, they do not dominate the required skillset—hence the significant, but not overwhelming, risk of automation.