Milling and Planing Machine Setters, Operators, and Tenders, Metal and Plastic
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Set up, operate, or tend milling or planing machines to mill, plane, shape, groove, or profile metal or plastic work pieces.
The occupation "Milling and Planing Machine Setters, Operators, and Tenders, Metal and Plastic" has an automation risk of 47.8%, slightly below its base risk of 48.3%. This moderate risk indicates that while many core tasks can be automated, certain responsibilities still require human input or oversight. The occupation involves configuring and running milling and planing machines, which can benefit from advancements in automation technology, particularly in repetitive and precision-driven functions. Automation risk in this field is heavily influenced by the rapid progress of machine learning, computer vision, and robotics, all of which can now perform many manual and measurement-related tasks with high reliability. However, complete automation is hindered by the need for skills that are difficult for artificial intelligence to replicate. Three of the most automatable tasks in this occupation are: removing finished workpieces from machines and inspecting them with measuring instruments; verifying the alignment of workpieces using gauges or calipers; and operating machine controls to set specifications, align parts, or start the equipment. These duties are largely rule-based, repetitive, and rely on precision, making them ideal targets for automation. Advanced robotics and machine sensors can now handle these operations with minimal human intervention, resulting in increased efficiency, reduced labor costs, and enhanced workplace safety. As these automated systems become more affordable and widespread, the share of such tasks performed by humans is expected to continue declining. Consequently, the job's overall automation risk remains close to the base estimate. On the other hand, the most resistant tasks are those requiring manual dexterity, nuanced judgment, or creative problem-solving. These include making custom templates or cutting tools, regulating coolant or lubricant flow by manual adjustment, and recording production output, which may involve subjective or situational decisions not easily codified for machines. The bottleneck skills for automation in this role are associated with originality, ranked very low at 2.3% and 2.0%, underscoring that machine operators occasionally need to innovate or adapt tools and processes for new or specialized jobs. These creative and adaptive demands are currently challenging for AI and robotics to manage, preserving a portion of human involvement and consequently keeping automation risk from reaching even higher percentages.