# Woodworking Machine Setters, Operators, and Tenders, Except Sawing

> Set up, operate, or tend woodworking machines, such as drill presses, lathes, shapers, routers, sanders, planers, and wood nailing machines. May operate computer numerically controlled (CNC) equipment.

- **SOC code:** 51-7042.00
- **Canonical URL:** https://singulariki.com/roles/role-51-7042-00
- **Also known as:** Cabinet Maker, Machine Operator, Sander, Sander Operator, Boring Machine Operator, Knot Saw Operator, Lathe Operator, Molder 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):
- Set up, program, operate, or tend computerized or manual woodworking machines, such as drill presses, lathes, shapers, routers, sanders, planers, or wood-nailing machines.
- Examine finished workpieces for smoothness, shape, angle, depth-of-cut, or conformity to specifications and verify dimensions, visually and using hands, rules, calipers, templates, or gauges.
- Start machines, adjust controls, and make trial cuts to ensure that machinery is operating properly.
- Monitor operation of machines and make adjustments to correct problems and ensure conformance to specifications.
- Examine raw woodstock for defects and to ensure conformity to size and other specification standards.
- Adjust machine tables or cutting devices and set controls on machines to produce specified cuts or operations.
- Install and adjust blades, cutterheads, boring-bits, or sanding-belts, using hand tools and rules.
- Change alignment and adjustment of sanding, cutting, or boring machine guides to prevent defects in finished products, using hand tools.
- Determine product specifications and materials, work methods, and machine setup requirements, according to blueprints, oral or written instructions, drawings, or work orders.
- Feed stock through feed mechanisms or conveyors into planing, shaping, boring, mortising, or sanding machines to produce desired components.
- Select knives, saws, blades, cutter heads, cams, bits, or belts, according to workpiece, machine functions, or product specifications.
- Push or hold workpieces against, under, or through cutting, boring, or shaping mechanisms.

## Skills, tools, capabilities

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

**Skills in demand:**
- Visualization _(Specialized Skill)_
- Finger Dexterity _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Microsoft Word _(Common Skill)_
- Microsoft Windows _(Common Skill)_
- Microsoft PowerPoint _(Common Skill)_
- Microsoft Outlook _(Common Skill)_
- Microsoft Excel _(Common Skill)_
- Information Ordering _(Specialized Skill)_
- Equipment Maintenance _(Specialized Skill)_
- Deductive Reasoning _(Common Skill)_
- Critical Thinking _(Common Skill)_

**Tools & technology:**
- Adobe Acrobat _(hot technology)_
- Adobe Creative Cloud software _(hot technology)_
- Adobe Illustrator _(hot technology)_
- Adobe InDesign _(hot technology)_
- Adobe Photoshop _(hot technology)_
- Apple macOS _(hot technology)_
- Autodesk AutoCAD _(hot technology)_
- Dassault Systemes SolidWorks _(hot technology)_
- Microsoft Excel _(hot technology)_
- Microsoft Office software _(hot technology)_
- Microsoft Outlook _(hot technology)_
- Microsoft PowerPoint _(hot technology)_

## AI exposure & outlook

- **AI task-overlap index:** 21st percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 13th percentile (Low) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 15th percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 42nd percentile (Moderate) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 94th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** -1.8% growth (Declining); 6.4k annual openings; 63.1k → 61.9k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $40,440; 63,350 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-51-7042-00_