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

> Set up, operate, or tend plating machines to coat metal or plastic products with chromium, zinc, copper, cadmium, nickel, or other metal to protect or decorate surfaces. Typically, the product being coated is immersed in molten metal or an electrolytic solution.

- **SOC code:** 51-4193.00
- **Canonical URL:** https://singulariki.com/roles/role-51-4193-00
- **Also known as:** Anodizer, Chrome Plater, Electro Plater, Plater, Anodizing Line Operator, Coater Associate, Coater Operator, Galvanizer
- **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):
- Immerse workpieces in coating solutions or liquid metal or plastic for specified times.
- Adjust dials to regulate flow of current and voltage supplied to terminals to control plating processes.
- Inspect coated or plated areas for defects, such as air bubbles or uneven coverage.
- Set up, operate, or tend plating or coating machines to coat metal or plastic products with chromium, zinc, copper, cadmium, nickel, or other metal to protect or decorate surfaces.
- Maintain production records.
- Remove objects from solutions at periodic intervals and observe objects to verify conformance to specifications.
- Observe gauges to ensure that machines are operating properly, making adjustments or stopping machines when problems occur.
- Remove excess materials or impurities from objects, using air hoses or grinding machines.
- Determine sizes and compositions of objects to be plated, and amounts of electrical current and time required.
- Test machinery to ensure that it is operating properly.
- Measure or weigh materials, using rulers, calculators, and scales.
- Measure, mark, and mask areas to be excluded from plating.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Production and Processing _(knowledge)_
- Mathematics _(knowledge)_
- Chemistry _(knowledge)_
- English Language _(knowledge)_
- Control Precision _(ability)_
- Near Vision _(ability)_
- Mechanical _(knowledge)_
- Engineering and Technology _(knowledge)_
- Operations Monitoring _(transferable_skill)_
- Oral Comprehension _(ability)_
- Problem Sensitivity _(ability)_
- Arm-Hand Steadiness _(ability)_

**Skills in demand:**
- Mathematics _(Common Skill)_
- Chemistry _(Specialized Skill)_
- English Language _(Common Skill)_
- Reading Comprehension _(Common Skill)_
- Active Listening _(Common Skill)_
- Time Management _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Microsoft Word _(Common Skill)_
- Microsoft Outlook _(Common Skill)_
- Microsoft Excel _(Common Skill)_
- Information Ordering _(Specialized Skill)_
- Inductive Reasoning _(Common Skill)_

**Tools & technology:**
- Autodesk AutoCAD _(hot technology)_
- Microsoft Excel _(hot technology)_
- Microsoft Outlook _(hot technology)_
- Microsoft Word _(hot technology)_
- Hazardous materials management HMS software
- Oracle Advanced Procurement

## AI exposure & outlook

- **AI task-overlap index:** 18th percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 21st percentile (Low) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 19th percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 23rd percentile (Low) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 82nd percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** -13.6% growth (Declining); 2.5k annual openings; 31.7k → 27.4k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $41,600; 31,510 employed.

## How people actually use AI here

Anthropic Economic Index — measured AI conversations mapped to this occupation's tasks:

- **Automation vs augmentation:** 70% automation, — augmentation (usage-weighted).
- **Autonomy median:** 2.0 (higher = AI acts more independently).
- **Dominant collaboration mode:** directive.

**Tasks most handed to AI here:**
- Measure or weigh materials, using rulers, calculators, and scales. _(0.3% of measured AI use; directive)_

**Example prompts (honest phrasings of the tasks above — starting points, not endorsed instructions):**
- Help me measure or weigh materials, using rulers, calculators, and scales.

## 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/
- **Anthropic Economic Index** (v4 (2026-01-15) + v2 (2025-03-27)) — Anthropic. https://www.anthropic.com/economic-index
- **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-51-4193-00_