Textile knitting and weaving machine setters, operators, and tenders Textile Knitting and Weaving Machine Setters, Operators, and Tenders
Kod pekerjaan: 51-6063（SOC） Bukan pekerjaan migrasi mahir Keseluruhan 5.2/10
Set up, operate, or tend textile machines to perform knitting, weaving, or drawing-in processes. Exclude sewing machine operators.
Penilaian · Keseluruhan 5.2/10i
In the AI era: what happens to Textile knitting and weaving machine setters, operators, and tenders
Textile knitting and weaving machine operators face high automation risk, as machine vision and industrial IoT can take over core tasks like quality inspection and machine monitoring, while generative AI can replace process parameter adjustments, leading to a significant reduction in job demand.
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Replaces manual operation and monitoring of machine operators in weaving processes, including yarn handling, fabric finishing, and troubleshooting.
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Largely replaced manual operations and subsequent steps of knitting machine operators, using program control to automatically complete complex pattern weaving and forming.
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Partially replaced the machine adjuster role, reducing manual trial and error through intelligent nesting and auto-optimization, improving weaving efficiency.
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replaced operator duties in quality inspection, automatically identifying and marking defects like broken yarns and stains, reducing manual dependency.
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- Automatically detect fabric defects via machine vision, replacing manual visual inspection
- Use AI predictive maintenance systems to automatically monitor machine status and adjust parameters
- Automatically optimize knitting/weaving process parameters (e.g., tension, speed) based on historical data
- Automated quality report generation, replacing manual recording and statistical analysis
- Remote operations center centrally monitors multiple machines, reducing on-site staff
- AI-assisted fault diagnosis system helps operators quickly locate complex mechanical issues
- Augmented reality (AR) glasses provide repair guidance, improving troubleshooting efficiency
- AI-driven production scheduling optimization to help operators manage multiple machines more efficiently
- Digital twin simulates different process scenarios to assist operator decision-making
- Automated material handling systems (AGVs) collaborate with operators, reducing physical labor
- Hands-on troubleshooting skills for on-site mechanical breakdowns
- Interpersonal coordination in communicating special order requirements with production supervisors and designers
- Tactile and experiential judgment for non-standard fabrics (e.g., special materials)
- Mechanical innovation to retrofit old machines for non-standard processes
- Safety and compliance responsibilities, especially when handling chemicals or high-temperature processes
- Industrial robot programming and debugging (e.g., PLC, SCADA)
- Data analysis and basic machine learning (inspection model maintenance)
- CAD/CAM and digital twin software operation.
- Automation system integration and sensor maintenance
- Ability to read and report on English technical documents
- Cross-departmental coordination and project management basics
Entry-level positions have significantly decreased because automatic looms and smart sensors no longer require many attendants, and companies prefer to hire technicians who can maintain automated systems; apprenticeship opportunities are shrinking.
It is recommended to transition to textile automation technician or smart factory operations engineer. First step: learn PLC programming and industrial networks, master common automation equipment debugging. Second step: take data analysis courses and participate in production data optimization projects. Third step: obtain Industry 4.0 certification (e.g., CERT) and combine AI tools (e.g., predictive maintenance platforms) to improve overall equipment effectiveness, eventually qualifying as production line system administrator.
Gaji
| Pengalaman | Tahunan (USD) | |
|---|---|---|
| Permulaan (0-3 tahun) | $25,000 ~ $30,000 | Entry-level pay is low, often near minimum wage |
| Pertengahan (3-5 tahun) | $30,000 ~ $35,000 | Slight increase after experience accumulation |
| Senior (5+ years) | $35,000 ~ $40,000 | Senior operator or foreman salary cap around 40,000 USD |
Laluan Pendidikan
| Peringkat | Tempoh | Kos (USD) |
|---|---|---|
| High school diploma or equivalent | 0 years | $0~$0 |
| Short-term on-the-job training | 1-3 months. | $0~$500 |
Kelayakan
| Kelayakan | Pengeluar | |
|---|---|---|
| No mandatory certification required | None | Pilihan |
| High school diploma or GED | School | Pilihan |
Migrasi
Bukan pekerjaan migrasi mahir. Visa pathways depend on matching the specific duties to the right petition category; refer to the latest USCIS rules and the relevant category.
Siapa yang sesuai
- Willing to accept low pay and does not require high education for entry-level workers
- Career changers interested in manufacturing
- Physical laborers requiring no English skills
- People seeking high income and growth opportunities
- Those hoping to obtain skilled migration in the US
Prospek kerjaya
Can advance to textile machinery technician or production supervisor through experience, but promotion opportunities are limited. Some workers move into textile machinery maintenance or quality control roles.
U.S. textile manufacturing continues to shrink, automation replacing labor, employment opportunities expected to decline. Most jobs are in low-wage areas, heavily affected by overseas competition.
Bidang pertumbuhan：
DecliningAutomationOffshoringLow demand
FAQ
Sumber data
Salary ranges are estimates aggregated from public listings on Indeed, Glassdoor, ERI SalaryExpert and the U.S. Bureau of Labor Statistics (BLS OEWS); employment and demand outlook cite the BLS Occupational Outlook and O*NET; visa and migration details follow the latest USCIS work-visa (H-1B / O-1 / L-1) and employment-based green-card (EB-2 / EB-3, incl. DOL PERM labor certification) rules. Figures are indicative only — always refer to the latest official sources.