# Rolling Machine Setters, Operators, and Tenders, Metal and Plastic

> Set up, operate, or tend machines to roll steel or plastic forming bends, beads, knurls, rolls, or plate, or to flatten, temper, or reduce gauge of material.

- **SOC code:** 51-4023.00
- **Canonical URL:** https://singulariki.com/roles/role-51-4023-00
- **Also known as:** Mill Operator, Rolling Mill Operator, Roughing Mill Operator, Tube Mill Operator, Breakdown Mill Operator, Calender Operator, Cold Mill Operator, Machine Operator
- **Frame:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- Monitor machine cycles and mill operation to detect jamming and to ensure that products conform to specifications.
- Adjust and correct machine set-ups to reduce thicknesses, reshape products, and eliminate product defects.
- Start operation of rolling and milling machines to flatten, temper, form, and reduce sheet metal sections and to produce steel strips.
- Examine, inspect, and measure raw materials and finished products to verify conformance to specifications.
- Read rolling orders, blueprints, and mill schedules to determine setup specifications, work sequences, product dimensions, and installation procedures.
- Thread or feed sheets or rods through rolling mechanisms, or start and control mechanisms that automatically feed steel into rollers.
- Manipulate controls and observe dial indicators to monitor, adjust, and regulate speeds of machine mechanisms.
- Set distance points between rolls, guides, meters, and stops, according to specifications.
- Calculate draft space and roll speed for each mill stand to plan rolling sequences and specified dimensions and tempers.
- Install equipment such as guides, guards, gears, cooling equipment, and rolls, using hand tools.
- Select rolls, dies, roll stands, and chucks from data charts to form specified contours and to fabricate products.
- Position, align, and secure arbors, spindles, coils, mandrels, dies, and slitting knives.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Operations Monitoring _(transferable_skill)_
- Operation and Control _(transferable_skill)_
- Problem Sensitivity _(ability)_
- Control Precision _(ability)_
- Reaction Time _(ability)_
- Near Vision _(ability)_
- Mechanical _(knowledge)_
- Rate Control _(ability)_
- Quality Control Analysis _(transferable_skill)_
- Manual Dexterity _(ability)_
- Multilimb Coordination _(ability)_
- Monitoring _(essential_skill)_

**Skills in demand:**
- English Language _(Common Skill)_
- Information Ordering _(Specialized Skill)_
- Finger Dexterity _(Common Skill)_
- Deductive Reasoning _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Inductive Reasoning _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Active Listening _(Common Skill)_
- Visualization _(Specialized Skill)_
- Time Management _(Common Skill)_
- Reading Comprehension _(Common Skill)_
- Equipment Maintenance _(Specialized Skill)_

**Tools & technology:**
- Email software
- Web browser software

## AI exposure & outlook

- **AI task-overlap index:** 22nd percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 15th percentile (Low) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 28th percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 32nd percentile (Low) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 68th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** -8.3% growth (Declining); 1.9k annual openings; 22.5k → 20.6k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $48,630; 22,350 employed.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Frey & Osborne (2013)** (frey-osborne-automation) — academic. https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment/
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

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_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-51-4023-00_