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Time Machine

Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders

Scrub through 132years of this role's history, from when it first emerged, through every wave of technology that reshaped it, to the cited projections for where it's heading next.

2026drag to travel through time
1925195019752000now
2026
Known today as Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders (BLS SOC 51-9041)
Latest actual · 2024
57K
OEWS is a point-in-time survey snapshot, not a continuous time series; BLS advises against using it for year-over-year trend comparison.
Latest actual · 2024
$45,130
Source: BLS-OEWS
Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.
Tools of the era

The tools that defined the work

Select an era to see how it reshaped the work.

  • Owens Automatic Rotary Bottle Machine and early screw extruders (hydraulic/pneumatic actuation)

    Michael J. Owens's automatic bottle machine, commercially deployed from 1904, gathered molten glass by vacuum suction, formed a parison, then blew it into molds at 240 bottles per minute. It replaced armies of skilled glassblowers and their boy assistants and created a new worker: the forming-machine operator who set vacuum levels, monitored gob temperature, measured wall thickness, and adjusted mold cooling. Simultaneously, the first industrial screw extruders were applied to rubber compounds in the 1820s-1890s and by the early 20th century were standard equipment in tire and hose factories. Operators set barrel temperatures, die pressure, and take-off speeds. Both machine families operated on hydraulic or steam pressure with mechanical actuation; precision was achieved through operator judgment rather than instrumentation.

    Effect on the work

    The Owens machine reduced glass-bottling labor costs by up to 80% at adopting factories. Skilled glassblowers (who could earn $10-15 per day in 1903) were largely displaced; machine operators earned substantially less but the new positions were numerically significant as glass-factory headcounts reorganized around the machines.

    Work toolChanging equipment
  • Thermoplastic screw extruders and twin-screw designs (Troester 1935, Colombo twin-screw 1930s, post-WWII polyethylene and PVC expansion)

    In 1935, German engineer Paul Troester achieved the first successful thermoplastic extrusion in Hamburg, extruding PVC film through a single-screw machine. Roberto Colombo in Italy independently pioneered the twin-screw extruder in the 1930s, offering superior mixing for compounded polymers. World War II accelerated plastic extrusion for military applications (hydraulic tubing for aircraft, wire insulation), and the post-war polymer boom brought polyethylene (1940s), polypropylene (1950s), and PVC pipe and window profiles to mass production. US plastic extruder operators became a growth occupation through the 1950s and 1960s as packaging film, pipe, and profiles proliferated. Food extrusion also scaled: cooking extruders commercialized corn snacks in the 1940s and the global extruded snack market grew rapidly through the 1960s. Operators learned to set die profiles, barrel-zone temperatures, and screw speeds, and to perform die-change changeovers under tight production schedules.

    Effect on the work

    Each new plastic application created net operator positions: US plastics employment expanded continuously from the late 1940s through the 1970s, with forming-machine operators among the fastest-growing production operative categories.

    Work toolChanging equipment
  • PLC-assisted machine control and in-line quality gauges (first programmable logic controllers, 1968)

    The first programmable logic controller (PLC), the Modicon 084, was introduced in 1968 for General Motors' Detroit-area assembly plants. PLC adoption in forming-machine environments through the 1970s and 1980s allowed temperature zones, screw speeds, and die pressures to be set digitally and held automatically rather than by manual valve adjustment. In-line gauging (laser micrometers measuring pipe or film thickness continuously) replaced the periodic manual caliper check. For the operator, this shifted the role from constant manual adjustment to setpoint programming and trend monitoring, raising the cognitive demand while reducing the physical. Changeover times fell as die dimensions and temperature profiles were stored and recalled as recipes. By the mid-1980s, most large-volume extruding lines in plastics and food were PLC-equipped; smaller forming shops (glass, ceramics, soap) lagged.

    Work toolChanging equipment
  • Statistical Process Control (SPC) and touchscreen HMI operator interfaces

    The 1990s brought Statistical Process Control to the forming-machine floor: real-time control charts on line-side monitors showed operators whether dimensional variation was trending toward specification limits before defects occurred. Touchscreen Human-Machine Interfaces (HMIs) replaced ladder-of-switches panels, displaying all zone temperatures, pressures, and drive speeds on a single screen and logging production data automatically. The HMI shift raised the reading and basic computer-interaction requirement for entry-level operators noticeably. It also enabled remote monitoring: a shift supervisor could check a line's status from a laptop rather than walking the floor. By 2010, most new forming lines in the United States used standardized HMI platforms (Siemens, Allen-Bradley), and operators were expected to navigate menus, enter recipe parameters, and acknowledge alarms without a supervisor present.

    Effect on the work

    SPC and HMI adoption did not eliminate forming-machine operators but raised the floor on required capability. Older operators who had mastered manual valve-and-gauge methods retired or retrained; the replacement generation came in with better reading and computer skills but fewer hands-on troubleshooting instincts for mechanical failure modes.

    Work toolChanging equipment
  • AI-assisted quality vision systems and predictive-maintenance platforms (machine learning process monitoring)

    Machine-vision cameras paired with deep-learning classifiers can now inspect forming-machine output at line speeds that human eyes cannot match: surface defects on extruded pipe, dimensional deviations in glass containers, and density variations in compacted tablets are flagged and rejected automatically. Predictive-maintenance platforms connect vibration sensors, temperature histories, and motor current readings to cloud dashboards that alert operators to likely bearing failures or heater-element degradation days before a breakdown. For the forming-machine operator, this is augmentation rather than replacement: the operator is relieved of the least-engaging inspection tasks and given more actionable information about the machine's health, but the physical presence, changeover judgment, and startup sequencing knowledge remain human. As of 2026, these systems are well-established in high-volume plastics and food lines and are beginning to reach mid-size glass and ceramics operations.

    Bedside monitoringVitals at a glance
Projection cone · present → 2034

What credible sources project

Scrub the slider past now to anchor each scenario on the scrubber. The spread is the range of futures credible sources project for this role.

Employment outlook
Projected change in the number of people doing this work.
BLS National Employment Matrix 2024-34
2034
+2%
BLS Employment Projections 2024-34 industry-occupation matrix for SOC 51-9041. Baseline employment: 57.3 thousand (2024); projected: 58.4 thousand (2034), a change of +1.1 thousand over ten years. The BLS classifies this as modest positive growth. Key drivers: continued domestic demand for specialty glass, ceramics, and food-extruded products; partial reshoring of plastics manufacturing for supply-chain resilience; and stable demand for soap and personal-care product forming. The projection does not assume a major new automation wave that displaces operator headcount, though AI-vision inspection may reduce labor per unit of output at the margin.
BLS Occupational Outlook Handbook: Production Occupations overview (2024-34)
2034
-6%
BLS projects overall production occupations to decline approximately 6 percent from 2024 to 2034, driven by continued automation and offshoring in high-volume manufacturing. SOC 51-9041 outperforms the production-occupations average in the current projection cycle (BLS projects +2% for 51-9041 vs -6% for the broader production group), likely because the cross-material diversity of the occupation means it captures growth in specialty food and personal-care extruding that offsets continued decline in commodity plastics processing. Reported here as the broader sector context for the occupation-level forecast.
AI task exposure
Share of the role’s tasks that researchers estimate AI can do. This is a measure of task exposure, not a forecast of jobs lost.
Frey and Osborne (2013): "The Future of Employment"
2033
75%
of tasks
Gaussian-process classifier on O*NET task features, estimating probability of computerization for each occupation. Forming and extruding machine occupations score high on the Frey-Osborne risk scale because the work is procedural (set parameters, monitor, adjust) and takes place in a structured physical environment, two conditions their model identifies as automation-susceptible. The -75% figure represents the implied displacement scenario if computerization probability is fully realized, which Frey and Osborne do not claim as a forecast. Actual employment decline since 2003 has been roughly 20-25% from the estimated earlier peak, substantially less severe than the F&O scenario. The more robust remaining barriers are: physical access to the machine (loading, cleaning, changeover), the judgment calls during startup and after a fault, and the cross-material variety that makes a single robot difficult to deploy across product changeovers in lower-volume operations.
Today, in this role

What's shifting in the work right now

The historical view above shows how this role has moved. This is the present-day detail: which AI tools are picking up which tasks, where the edge still is, and the natural directions this work can grow.

What's changing in your day

Three parts of your work where AI is already doing real lifting, and what stays yours.

AI is sitting alongside you hereMonitor machine operations and observe lights and gauges to detect malfunctions.

Monitor machine operations and observe lights and gauges to detect malfunctions.[2]

Where your edge is

AI is sitting alongside you hereTurn controls to adjust machine functions, such as regulating air pressure, creating vacuums, and adjusting coolant flow.

Turn controls to adjust machine functions, such as regulating air pressure, creating vacuums, and adjusting coolant flow.[2]

Where your edge is

AI is sitting alongside you hereClean dies, arbors, compression chambers, and molds, using swabs, sponges, or air hoses.

Clean dies, arbors, compression chambers, and molds, using swabs, sponges, or air hoses.[2]

Where your edge is

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The data behind this timeline

On record since1904
Latest tracked employment57,310 (US, 2024)
Latest median pay$45,130 (2024)
Outlook+2% by 2034 (BLS National Employment Matrix 2024-34)
View all 26 cited data points
YearUS employmentMedian annual paySource
192045,000n/aESTIMATE
195095,000n/aESTIMATE
1980120,000n/aESTIMATE
200075,000$27,736ESTIMATE, BLS-CPS
200373,990$27,140BLS-OEWS
200473,970$27,460BLS-OEWS
200580,420$27,790BLS-OEWS
200681,000$27,710BLS-OEWS
200788,600$28,000BLS-OEWS
200885,130$28,960BLS-OEWS
200972,770$29,860BLS-OEWS
201065,100$31,210BLS-OEWS
201166,330$31,190BLS-OEWS
201268,080$31,310BLS-OEWS
201369,740$31,760BLS-OEWS
201467,490$32,100BLS-OEWS
201571,430$32,160BLS-OEWS
201671,260$32,510BLS-OEWS
201776,120$33,680BLS-OEWS
201872,870$35,120BLS-OEWS
201971,850$35,480BLS-OEWS
202063,730$36,560BLS-OEWS
202156,570$37,660BLS-OEWS
202258,740$39,480BLS-OEWS
202357,080$42,670BLS-OEWS
202457,310$45,130BLS-OEWS
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