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Manufacturing & Production · Updated Aug 2026

Patternmakers, Metal and Plastic

Lay out, machine, fit, and assemble castings and parts to metal or plastic foundry patterns, core boxes, or match plates.

JVS 2.0.0-phase4b
AI Exposure
61/100
High

How much of this occupation’s work can be materially affected by current AI systems.

Replacement Risk
50/100
Moderate

How likely exposure is to translate into reduced human demand.

Includes provisional estimates for AI adoption pressure and labour-market resilience. How this is measured

Evidence quality
Confidence80/100
Task coverage80%

Confidence reflects task coverage, mapping and capability-evidence quality, and how much of the score rests on provisional inputs.

Task-level evidence

What is driving the score?

Occupation scores are built from the task mix—not a single prediction about a job title.

JVS 2.0.0-phase4b
TaskImportanceAI impactExposure
Verify conformance of patterns or template dimensions to specifications, using measuring instruments such as calipers, scales, and micrometers.High
74
Set up and operate machine tools, such as milling machines, lathes, drill presses, and grinders, to machine castings or patterns.High
65
Read and interpret blueprints or drawings of parts to be cast or patterns to be made, compute dimensions, and plan operational sequences.Medium
73
Create computer models of patterns or parts, using modeling software.High
72
Mark identification numbers or symbols onto patterns or templates.Medium
75
Construct platforms, fixtures, and jigs for holding and placing patterns.Medium
75
Lay out and draw or scribe patterns onto material, using compasses, protractors, rulers, scribes, or other instruments.Medium
74
Select pattern materials such as wood, resin, and fiberglass.Medium
75
Design and create templates, patterns, or coreboxes according to work orders, sample parts, or mockups.Medium
38
Apply plastic-impregnated fabrics or coats of sealing wax or lacquer to patterns used to produce plastic.Medium
74
Assemble pattern sections, using hand tools, bolts, screws, rivets, glue, or welding equipment.Medium
21
Clean and finish patterns or templates, using emery cloths, files, scrapers, and power grinders.Medium
19
Most exposed

Where AI can do more

Routine, digitized, and highly repeatable tasks face the greatest pressure.

  1. Mark identification numbers or symbols onto patterns or templates.75
  2. Construct platforms, fixtures, and jigs for holding and placing patterns.75
  3. Select pattern materials such as wood, resin, and fiberglass.75
  4. Verify conformance of patterns or template dimensions to specifications, using measuring instruments such as calipers, scales, and micrometers.74
Hardest to automate

Where people still matter

These tasks score lowest on automation feasibility—physical presence, judgement, accountability and real-world variability all resist end-to-end automation.

  1. Assemble pattern sections, using hand tools, bolts, screws, rivets, glue, or welding equipment.01
  2. Clean and finish patterns or templates, using emery cloths, files, scrapers, and power grinders.02
  3. Design and create templates, patterns, or coreboxes according to work orders, sample parts, or mockups.03
  4. Verify conformance of patterns or template dimensions to specifications, using measuring instruments such as calipers, scales, and micrometers.04
  5. Set up and operate machine tools, such as milling machines, lathes, drill presses, and grinders, to machine castings or patterns.05
Where else this work leads

Related occupations

Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.

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Beyond AI capability

Adoption and labour-market outlook

Structural factors are kept separate from raw capability so you can see what actually resists automation. Adoption pressure and labour-market resilience are still provisional models—25% of this occupation’s replacement-risk weight rests on them.

Human dependency63
Physical dependency56
Adoption pressure42
Labour-market resilience62
Methodology & sources

O*NET 30.3 occupational data interpreted through the JobsVsAI capability, automation and structural-constraint models.

Confidence80/100
CalculatedAug 21, 2026
Read methodology →