Set up, operate, or tend machines to crush, grind, or polish materials, such as coal, glass, grain, stone, food, or rubber.
U.S. Workers
28,550
Median Salary
$46,890
10-Year Growth
-2.5%
Annual Openings
2,700
Typical entry: High school diploma or equivalent
22 of 22 tasks have some AI capability
Exposure Trend
This score reflects estimated AI technical capability for tasks in this occupation. It does not predict employment changes, and it does not account for company-specific constraints, regulation, or adoption barriers.
Observe operation of equipment to ensure continuity of flow, safety, and efficient operation, and to detect malfunctions.
AI: Fully automatable - AI-powered sensors and computer vision can continuously observe equipment, maintain flow, and detect many classes of malfunctions in real time.
Move controls to start, stop, or adjust machinery and equipment that crushes, grinds, polishes, or blends materials.
AI: Fully automatable - Starting, stopping, and adjusting machinery are routine control actions already executed by PLCs and AI-based control systems.
Test samples of materials or products to ensure compliance with specifications, using test equipment.
AI: Fully automatable - Automated testing rigs and laboratory instruments can run standard material and product tests and AI can interpret results to verify specifications.
Weigh or measure materials, ingredients, or products at specified intervals to ensure conformance to requirements.
AI: Fully automatable - Weighing and measuring can be fully automated with sensors, scales, and AI-driven monitoring systems that enforce schedules and conformance.
Read work orders to determine production specifications and information.
AI: Fully automatable - Parsing digital or scanned work orders and extracting production specifications is a routine OCR/NLP task that AI can fully automate to drive downstream processes.
Tend accessory equipment, such as pumps and conveyors, to move materials or ingredients through production processes.
AI: Fully automatable - Monitoring and controlling pumps and conveyors is widely automated via PLCs, sensors, and AI supervisory systems, enabling full automation of tending tasks.
Record data from operations, testing, and production on specified forms.
AI: Fully automatable - Recording operational and test data can be fully automated by integrating sensors, IIoT and MES systems with AI to populate forms and logs.
Notify supervisors of needed repairs.
AI: Fully automatable - Automated monitoring and diagnostic systems can detect issues and reliably notify supervisors via alerts, workflows, and ticketing integrations.
Transfer materials, supplies, and products between work areas, using moving equipment and hand tools.
AI: Fully automatable - Material transfer is widely automated in industry using conveyors, AGVs and robotic handlers under AI/PLC control, enabling full automation in most settings.
Inspect chains, belts, or scrolls for signs of wear.
AI: Fully automatable - Modern computer-vision systems and sensor-equipped robots can reliably detect visible wear on chains, belts, and scrolls and flag or log defects.
Mark bins as to types of mixtures stored.
AI: Fully automatable - Labeling and marking bins is a routine, low-complexity task that is easily and reliably automated with printers, conveyors, and simple control logic.
Turn valves to regulate the moisture contents of materials.
AI: Fully automatable - Moisture regulation via valve actuation tied to sensors is a standard industrial control problem and can be fully automated with actuators and control systems.
Add or mix chemicals and ingredients for processing, using hand tools or other devices.
AI: Fully automatable - Batch dosing and mixing of chemicals and ingredients is widely automated using pumps, feeders, mixers, and PLC/AI control in industrial settings.
Examine materials, ingredients, or products, visually or with hands, to ensure conformance to established standards.
AI: Partial - Visual inspection is often automatable with machine vision, but tactile examination and subtle quality judgments still frequently require humans.
Clean, adjust, and maintain equipment, using hand tools.
AI: Partial - Requires manual dexterity, varied tool use, and situational judgment that AI-controlled robots in 2025 can only handle in limited, structured settings.
Dislodge and clear jammed materials or other items from machinery and equipment, using hand tools.
AI: Partial - Dislodging jams involves unpredictable, constrained physical interactions and diverse failure modes that current robotics and AI handle only partially and often require human intervention.
Collect samples of materials or products for laboratory testing.
AI: Partial - Automated samplers exist for many controlled processes, but collecting representative physical samples across varied contexts often requires human judgment and manual access.
Set mill gauges to specified fineness of grind.
AI: Partial - Closed-loop controls can set fineness when gauges are motorized or digital, but many mills still require manual mechanical gauge adjustments that are not fully automatable.
Load materials into machinery and equipment, using hand tools.
AI: Partial - Loading materials often requires complex perception and dexterous manipulation across varied shapes and contexts, so robots in 2025 can automate many but not all loading scenarios.
Clean work areas.
AI: Partial - Basic floor and routine cleaning are automated, but thorough cleaning around machinery and tool-based cleaning remain difficult for current autonomous systems.
Reject defective products and readjust equipment to eliminate problems.
AI: Partial - AI systems can perform automated visual rejection and do some parameter tuning, but complex troubleshooting and manual mechanical readjustments still require human intervention.
Break mixtures to size, using picks.
AI: Partial - Breaking mixtures to size with picks is a manual dexterity task and while mechanical breakers exist, general-purpose automation for pick-like tasks is limited.