# Adhesive Bonding Machine Operators and Tenders

> Operate or tend bonding machines that use adhesives to join items for further processing or to form a completed product. Processes include joining veneer sheets into plywood; gluing paper; or joining rubber and rubberized fabric parts, plastic, simulated leather, or other materials.

- **SOC code:** 51-9191.00
- **Canonical URL:** https://singulariki.com/roles/role-51-9191-00
- **Also known as:** Coater Operator, Glue Line Operator, Machine Operator, Utility Worker, Glue Reel Operator, Gluer Machine Operator, Gluing Pressman, Perfect Bind 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):
- Align and position materials being joined to ensure accurate application of adhesive or heat sealing.
- Adjust machine components according to specifications such as widths, lengths, and thickness of materials and amounts of glue, cement, or adhesive required.
- Monitor machine operations to detect malfunctions and report or resolve problems.
- Start machines, and turn valves or move controls to feed, admit, apply, or transfer materials and adhesives, and to adjust temperature, pressure, and time settings.
- Fill machines with glue, cement, or adhesives.
- Perform test production runs and make adjustments as necessary to ensure that completed products meet standards and specifications.
- Examine and measure completed materials or products to verify conformance to specifications, using measuring devices such as tape measures, gauges, or calipers.
- Read work orders and communicate with coworkers to determine machine and equipment settings and adjustments and supply and product specifications.
- Remove and stack completed materials or products, and restock materials to be joined.
- Maintain production records such as quantities, dimensions, and thicknesses of materials processed.
- Remove jammed materials from machines and readjust components as necessary to resume normal operations.
- Observe gauges, meters, and control panels to obtain information about equipment temperatures and pressures, or the speed of feeders or conveyors.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Production and Processing _(knowledge)_
- Operation and Control _(transferable_skill)_
- Operations Monitoring _(transferable_skill)_
- Mechanical _(knowledge)_
- Arm-Hand Steadiness _(ability)_
- Manual Dexterity _(ability)_
- Static Strength _(ability)_
- Control Precision _(ability)_
- Trunk Strength _(ability)_
- Near Vision _(ability)_
- English Language _(knowledge)_
- Problem Sensitivity _(ability)_

**Skills in demand:**
- English Language _(Common Skill)_
- Finger Dexterity _(Common Skill)_
- Visualization _(Specialized Skill)_
- Information Ordering _(Specialized Skill)_
- Deductive Reasoning _(Common Skill)_
- Active Listening _(Common Skill)_
- Time Management _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Reading Comprehension _(Common Skill)_
- Microsoft Word _(Common Skill)_
- Microsoft PowerPoint _(Common Skill)_
- Microsoft Excel _(Common Skill)_

**Tools & technology:**
- Microsoft Excel _(hot technology)_
- Microsoft Office software _(hot technology)_
- Microsoft PowerPoint _(hot technology)_
- Microsoft Word _(hot technology)_
- SAP software _(hot technology)_

## AI exposure & outlook

- **AI task-overlap index:** 15th percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 8th percentile (Low) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 9th percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 36th percentile (Moderate) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 89th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 1.0% growth (About average); 1.3k annual openings; 12.2k → 12.3k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $45,210; 12,170 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-51-9191-00_