64
/100

SOC 51-6091

Extruding and Forming Machine Setters, Operators, and Tenders, Synthetic and Glass Fibers

High RiskFrey/Osborne: 88.0%

Risk Score

โš ๏ธ

64/100

High Risk

US Employment

๐Ÿ‘ฅ

14,900

Total workers

Median Wage

๐Ÿ’ฐ

$45K

$36K โ€“ $64K

Projected Growth

๐Ÿ“ˆ

-1.1%

2023-2033 (BLS)

GenAI Exposure

๐Ÿค–

53/100

Moderate exposure

How we calculate these numbers โ†’

๐Ÿ’ก Extruding and Forming Machine Setters, Operators, and Tenders, Synthetic and Glass Fibers face a risk score of 64/100 โ€” 20 points above the national average of 44. With 53/100 GenAI exposure, this occupation faces significant pressure from AI tools despite weak projected growth. See our methodology โ†’

๐Ÿ’ก Workers in this field earn $45K. 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 64/100, Extruding and Forming Machine Setters, Operators, and Tenders, Synthetic and Glass Fibers faces moderate automation pressure. While tasks like ai quality inspection via computer vision systems are increasingly handled by AI, the role retains significant human elements. The 14,900 workers in this occupation should focus on strengthening skills in quality judgment requiring tactile and visual inspection and setup and calibration of custom production runs to stay ahead. The role will likely evolve rather than disappear.

Will AI Replace Extruding and Forming Machine Setters, Operators, and Tenders, Synthetic and Glass Fibers?

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

โš ๏ธ Top Risk Factors

1

AI quality inspection via computer vision systems

2

Predictive maintenance reducing manual inspection roles

3

Cobots handling repetitive material handling tasks

4

Smart factory scheduling and production optimization

๐Ÿ›ก๏ธ Tasks Safe from Automation

โœ“

Quality judgment requiring tactile and visual inspection

โœ“

Setup and calibration of custom production runs

โœ“

Coordinating workflow across diverse production teams

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

Set up, operate, or tend machines that extrude and form filaments from synthetic materials such as rayon, fiberglass, or liquid polymers.

โ€ข

Press buttons to stop machines when processes are complete or when malfunctions are detected.

โ€ข

Notify other workers of defects, and direct them to adjust extruding and forming machines.

โ€ข

Observe machine operations, control boards, and gauges to detect malfunctions such as clogged bushings and defective binder applicators.

โ€ข

Load materials into extruding and forming machines, using hand tools, and adjust feed mechanisms to set feed rates.

๐Ÿ’ป Technology Skills

โ€ข

Data base management system software

โ€ข

Industrial control software

โ€ข

Spreadsheet software

โ€ข

Office suite software

โ€ข

Electronic mail software

๐ŸŽ“ Key Knowledge Areas

โ€ข

Production and Processing

โ€ข

English Language

โ€ข

Mathematics

โ€ข

Education and Training

โ€ข

Mechanical

๐Ÿ“Š vs National Average

Median Wage$45K
$-1K

National avg: $46K

Risk Score64/100
+20

National avg: 44/100

GenAI Exposure53/100
+15

National avg: 38/100

Projected Growth-1.1%
-4.8%

National avg: 3.7%

๐Ÿ”„ Career Transition Paths

OccupationRiskWageOverlap
Engineers20$106K53%
First-Line Supervisors of Transportation and Material Moving Workers, Except Aircraft Cargo Handling Supervisors25$62K63%
Fabric and Apparel Patternmakers33$68K77%