70
/100

SOC 51-4031

Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic

High RiskFrey/Osborne: 78.0%

Risk Score

โš ๏ธ

70/100

High Risk

US Employment

๐Ÿ‘ฅ

174,430

Total workers

Median Wage

๐Ÿ’ฐ

$46K

$35K โ€“ $63K

Projected Growth

๐Ÿ“ˆ

-12.1%

2023-2033 (BLS)

GenAI Exposure

๐Ÿค–

44/100

Moderate exposure

How we calculate these numbers โ†’

๐Ÿ’ก Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic face a risk score of 70/100 โ€” 26 points above the national average of 44. With only 44/100 GenAI exposure, most core tasks remain resistant to current AI capabilities. See our methodology โ†’

๐Ÿ’ก Workers in this field earn $46K. The 3 recommended career transitions all maintain competitive wages while reducing automation exposure. Explore transition paths โ†’

๐Ÿ” AI Impact Analysis

With a risk score of 70/100, Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic faces significant automation pressure. Key threats include automated cnc programming and machine operation and predictive maintenance reducing manual inspection roles. The 174,430 Americans in this role should actively develop skills in troubleshooting complex equipment malfunctions and quality judgment requiring tactile and visual inspection to remain competitive. Workers who proactively adapt will find new opportunities even as traditional tasks are automated.

Will AI Replace Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic?

Read our full analysis with verdict, risk factors, safe tasks, and career transition paths โ†’

โš ๏ธ Top Risk Factors

1

Automated CNC programming and machine operation

2

Predictive maintenance reducing manual inspection roles

3

Smart factory scheduling and production optimization

4

Cobots handling repetitive material handling tasks

๐Ÿ›ก๏ธ Tasks Safe from Automation

โœ“

Troubleshooting complex equipment malfunctions

โœ“

Quality judgment requiring tactile and visual inspection

โœ“

Handling non-standard materials and configurations

๐Ÿ“Š Task Automation Breakdown

Based on O*NET task analysis and GenAI exposure scoring. Shows the estimated proportion of this occupation's core tasks that are automatable by current AI, augmented by AI tools, or require essential human skills.

๐Ÿ“‹ O*NET Task Profile

โ€ข

Examine completed workpieces for defects, such as chipped edges or marred surfaces and sort defective pieces according to types of flaws.

โ€ข

Measure completed workpieces to verify conformance to specifications, using micrometers, gauges, calipers, templates, or rulers.

โ€ข

Set stops on machine beds, change dies, and adjust components, such as rams or power presses, when making multiple or successive passes.

โ€ข

Start machines, monitor their operations, and record operational data.

โ€ข

Set up, operate, or tend machines to saw, cut, shear, slit, punch, crimp, notch, bend, or straighten metal or plastic material.

๐Ÿ’ป Technology Skills

โ€ข

Computer aided design CAD software

โ€ข

Inventory management software

โ€ข

Industrial control software

โ€ข

Spreadsheet software

โ€ข

Office suite software

๐ŸŽ“ Key Knowledge Areas

โ€ข

Mechanical

โ€ข

Production and Processing

โ€ข

Mathematics

โ€ข

English Language

โ€ข

Design

๐Ÿ“Š vs National Average

Median Wage$46K
$-720

National avg: $46K

Risk Score70/100
+26

National avg: 44/100

GenAI Exposure44/100
+6

National avg: 38/100

Projected Growth-12.1%
-15.8%

National avg: 3.7%

๐Ÿ”„ Career Transition Paths

OccupationRiskWageOverlap
Engineers20$106K65%
First-Line Supervisors of Transportation and Material Moving Workers, Except Aircraft Cargo Handling Supervisors25$62K55%
Fabric and Apparel Patternmakers33$68K74%