Artificial intelligence offers a transformative solution to the resistance welding skills gap
By Jake Godgart, vice president of marketing at WeldComputer Corp., Troy, N.Y.
Reprinted with permission: The AWS Welding Journal
Manufacturing today demands constant improvement. Better quality, higher throughput, and lower costs are all at the top of mind. If you’re a manufacturer in the resistance welding industry — often in the automotive, aerospace, or defense sectors — you’re feeling these pressures intensify, especially with economic factors like tariffs and inflation squeezing margins.
While resistance welding is valued for speed and efficiency, a critical challenge threatens your ability to compete: the growing skills gap. Finding and retaining personnel with deep resistance welding expertise is getting harder. Experienced engineers are retiring, and attracting new talent is difficult (Ref. 1). This bottleneck impacts your ability to optimize processes, troubleshoot effectively, and maintain the consistent, high-yield production your customers expect.
Getting specialized help quickly often isn’t possible or affordable, leading to costly delays. That’s why applying artificial intelligence (AI) to enhance resistance welding operations offers a promising solution. In many manufacturing sectors, AI combined with advanced data collection and analysis is already proving its value, moving beyond theory to deliver tangible results.
This presents a significant opportunity for resistance welding, where adoption has been lighter thus far. Integrating these established AI capabilities offers a clear pathway to handling critical workforce challenges and bridging the skills gap.
Crossing the Deepening Skills Chasm
Industry reports consistently highlight a severe shortage of skilled welding professionals, which creates real operational hurdles. Projections indicate a need for approximately 320,500 new welding professionals by 2029, meaning roughly 80,000 jobs need filling each year (Ref. 1).
This is exacerbated by the retirement of engineers possessing decades of invaluable, experiential tribal knowledge that is difficult to transfer systematically. AWS’s latest estimates show that over 20% of the workforce is nearing retirement, yet fewer than 10% of welders are under the age of 25 (Ref. 1). We are entering a resistance welding experience vacuum.
This labor gap means many manufacturers lack sufficient in-house expertise. While expert help is crucial for setup, troubleshooting (like electrode or machine issues), and validating materials, needing it only intermittently makes full-time hires costly, and delays in getting external help lead to inefficiencies and potential quality problems.
How Remote Expertise Helps Today
The intermittent need for deep expertise often makes full-time specialists cost-prohibitive, leading to reactive problem-solving when delays are most costly. Recognizing this, leading resistance welding vendors currently offer remote weld engineering managed services to provide guidance for manufacturers lacking in-house resources (Ref. 2). Experts can securely log into connected systems online, examine detailed weld data immediately after production, and provide crucial guidance.
They analyze process signatures to diagnose issues related to process drift, consistency, part fitup, material changes, electrode wear, machine variations, and more. Based on these findings, they can identify specific problems with the machine that necessitate maintenance, provide recommendations on process improvements, and even implement schedule adjustments remotely via the control interface. This remote support significantly cuts down the time and cost of accessing expertise. However, it still depends on human availability and scalability. Each engineer can only oversee so many machines.
Integrating AI Will Be the Next Frontier
The logical progression involves substituting AI for remote human analysis. The necessary infrastructure — primarily systems capable of capturing high-resolution data and allowing secure electronic data transfer — is largely available. This connectivity enables operational data to be uploaded into commercially available AI services.
Trained with specialized resistance welding data sets (closed teaching data sets), these AI systems analyze the nuances of the welding operation’s state with speed and depth. Based on this analysis, the AI can generate insights, predictions, actionable instructions, or notifications that can be relayed to operational systems or personnel.
This automates the oversight function, moving beyond periodic analysis toward continuous, proactive, intelligent process management. Imagine this AI functioning like an expert engineer, constantly watching every single weld. It identifies patterns, predicts potential issues, recommends optimized parameters, or flags necessary interventions related to electrodes, the machine, or materials. This brings expert-level oversight to potentially every machine on your floor.
How AI Will Transform Resistance Welding Operations
Think of AI as a powerful analytical partner driven by process data and poised to enhance resistance welding performance by achieving new levels of operational intelligence, efficiency, and quality assurance. This isn’t merely an incremental update for the field; AI’s ability to automate complex analysis and data-driven decision-making offers a fundamental improvement in process understanding and optimization, directly addressing the core challenge of limited expert availability.
AI offers tangible benefits by providing deeper understanding and enabling smarter decisions. Given the advancements of AI in other industries over the past two-and-a-half years, we anticipate many of these innovations will be delivered in the not-too-distant future:
Automated oversight and proactive warning: Downtime costs can be significant for small and medium-sized manufacturers, reaching upwards of $150,000 an hour. For larger automotive plants, that average hourly loss is $2.3 million (Ref. 3). While only a portion of all downtime can be directly attributed to resistance welding, these numbers portray the grim reality of the negative impact of downtime on the manufacturing sector. Studies show that implementing AI-driven predictive maintenance drives up to a 25% reduction in maintenance costs and reduces downtime by 35%, facilitating scheduled interventions rather than reactive emergency repairs (Ref. 4). For example, currently available systems already have the infrastructure in place to use AI to continuously monitor weld data streams, automatically analyzing trends and subtle deviations indicative of incipient issues like electrode wear or process drift before they cause defects. AI provides those context-rich notifications for even the most minor deviations in your process, helping avoid costly downtime.
Intelligent schedule setup: Developing optimal weld schedules often involves complex tables and experimentation. AI can streamline schedule development by interpreting inputs like material specifications, thickness, and joint requirements alongside catalog data and best practices. It can analyze requirements and recommend optimized starting parameters based on historical data, simulations, and best practices, reducing setup time and reliance on scarce expertise.
Adapt dynamically to process changes: Extending beyond current adaptive controls, AI can learn optimal responses to process variations. Detecting subtle changes (e.g., in new material batches), AI can characterize the change, make controlled adjustments automatically to maintain consistency, and clearly notify operators about what happened and why. For issues requiring manual intervention, AI provides specific diagnostic guidance.
Troubleshoot faster with enhanced diagnostics: When problems occur, AI acts as a powerful diagnostic assistant. By analyzing fault signatures against its database of known patterns, it can suggest probable root causes for issues like inconsistent strength or expulsions, helping you differentiate between machine, material, or electrode problems and guiding corrective actions more efficiently.
Essential Infrastructure and Key Considerations for AI Adoption
Realizing this AI-driven future requires appropriate technological foundations: systems capable of capturing high-granularity; millisecond-level data; and robust, secure connectivity for data transfer to cloud platforms. Investing in these systems early aligns with market growth projections for resistance welding machines (an estimated $2 billion by 2033) and with the increasing adoption of AI-integrated control systems aimed at future-proofing resistance welding operations (Ref. 5).
While AI integration prompts discussions about employment impact and data security, it should primarily be viewed as a tool to augment your workforce. AI automates complex analysis, freeing your experts for hands-on work, innovation, and strategic oversight. McKinsey research suggests there’s an opportunity to see 10–30% increases in throughput, 15–30% improvements in labor productivity, and 85% more-accurate forecasting by successfully implementing solutions that deliver the promises of a digitally enabled factory (Ref. 6).
It would be remiss not to acknowledge the robust cybersecurity measures needed to defend this operational technology and enhanced transparency crucial for building trust with operators. As other industries race to adopt AI technologies for more-sophisticated use cases outside the realm of resistance welding, we anticipate similar guardrails will apply to our industry as well and will likely develop progressively from AI recommendations to controlled autonomous adjustments. The vision is for more-intelligent, resilient operations with enhanced stability, reduced waste, faster setups, and amplified human expertise.
Embracing the Intelligent Future
The resistance welding industry’s skills gap poses a significant challenge to meeting market demands for quality and throughput, with a projected need for hundreds of thousands of welding professionals. AI, leveraging data captured through modern controls and connectivity, offers the most effective path forward to directly address the expertise bottleneck by automating analysis, providing early warnings, assisting setup, and enabling sophisticated process adaptation.
As the experienced workforce shrinks and cost pressures rise, AI-driven capabilities become essential for improving quality, boosting throughput, and reducing costs with fewer personnel. Given the advancements over the last several years, it wouldn’t be surprising to see successful implementations of AI used by engineers to achieve ten times what they previously could have without AI augmenting their production process.
The foundational technologies are available today. Embracing this potential requires investing in data-capable controls and connectivity. Your next hire — the AI robot — is quickly becoming a necessary partner for staying competitive. WJ
References
1. AWS. Welding Workforce Data. Retrieved May 30, 2025, from weldingworkforcedata.com.
2. WeldComputer Corp. Managed Machine Performance. Retrieved April 24, 2025, from weldcomputer.com/services/
managed-machine-performance.
3. Siemens AG. 2024. Senseye Predictive Maintenance — The True Cost of Downtime 2024. Retrieved May 30, 2025, from assets.new.siemens.com/siemens/assets/api/uuid:1b43afb5-2d07-47f7-9eb7-893fe7d0bc59/TCOD-2024_original.pdf.
4. Dupont, M. 2024. Predictive maintenance revolutionized by AI models. LabelVisor. Retrieved April 24, 2025, from labelvisor.com/predictive-maintenance-revolutionized-by-ai-models.
5. Global Growth Insights. 2024. Resistance Welding Machine Market Size, Share, Growth, and Industry Analysis, By Types (Automatic Resistance Welding Machine, Semi - Automatic Resistance Welding Machine, Manual Resistance Welding Machine), By Applications (Aircraft Construction, Domestic Appliances Industry, Automobile Industry, Others), Regional Insights and Forecast to 2033.
6. Gregolinska, E., Khanam, R., Lefort, F., and Parthasarathy, P. 2022. Capturing the true value of Industry 4.0. McKinsey & Co. Retrieved May 30, 2025, from mckinsey.com/capabilities/operations/our-insights/capturing-the-true-value-of-industry-four-point-zero.
Fig 1 — The Seedorff precision seam welding unit equipped with WeldComputer® technology uses cloud-transmitted weld data to enable automated process trend tracking, human intervention alerts, and root cause analysis.