Over the last year, the term “Artificial Intelligence” (AI) has become pivotal in shaping discussions across various industries, marking a significant milestone in technological advancement. This shift towards AI signifies the beginning of an era – reminiscent of the Industrial Revolution – setting the stage for unprecedented transformation across all sectors, including resistance welding. By harnessing the capabilities of AI, industries are poised to redefine efficiency, speed, and productivity. AI’s application can usher a transition from traditional, manual operations to sophisticated, machine-driven processes.

While the new industrial revolution may occur across other industries, ironically, the resistance welding industry is deeply rooted in established standards and historically known for gradual technological developments. Except for robotic welding processes, it would not be an understatement to say our industry is behind the times to adopt new technological advancements. In our opinion, AI will be a catalyst to change that paradigm.

This transformative technology provides valuable insights that can enhance production throughput, elevate quality standards, reduce operational costs, cut down on scrap, and much more. This approach ensures that real-world challenges faced in manufacturing processes are met with efficient and effective resolutions empowered by advanced technology. Some of these advancements are already available today.

It’s safe to say that AI is not merely a passing trend, nor does it reflect the possibilities only reserved for science fiction. Common applications for AI will elevate resistance welding by optimizing efficiency and refining precision to new levels. The integration of AI technology within resistance welding technology and processes will unlock the potential to dramatically enhance the way we set up and manage spot, seam, projection, and flash/butt welding operations.

At WeldComputer, we always strive to be at the leading edge of technology and innovation. For over 30 years, our team has studied resistance welding technologies, processes, and tools which have helped us successfully apply AI to the set up and adaptive control of the resistance welding process.  Yet there are far more applications beyond what have currently been implemented.

We thought it would be fun to share some visions for the future of resistance welding and create eight predictions for how the application of AI may transform our field as we know it:

 

Prediction #1

AI will lead to robotic welding automation becoming the norm.

In the next two decades, AI-driven robotic welding automation is set to revolutionize resistance welding applications. This technology will enhance robot trajectories, manage the handling and feeding of materials into machines, establish adaptive weld schedules to optimize welding performance, and oversee the removal and transfer of welded materials to subsequent production stages. Additionally, it will segregate parts that fail to meet all QC standards. Consequently, robotics will dominate resistance welding, replacing traditional human-led methods.

Flash/Butt Welding Example:  AI-guided welding systems automate flash and butt welding operations that enhance productivity and repeatability by integrating AI-driven path planning, adaptive control, and real-time feedback mechanisms. By coordinating robotic motion with welding parameters, AI-enabled systems optimize weld quality and throughput while minimizing cycle time and material waste.

Likelihood: More likely than not – While this seems like a bold prediction, the reality is closer than we may believe. But this shift is not solely due to AI. The resistance welding industry faces a talent shortage and welding skills gap that continues to widen as many experienced welding engineers head toward retirement without younger tradespeople ready to take their place. Robotics has become a way to ensure business continuity as these skilled engineers leave the workforce while reducing the need for trained engineers. The addition of AI will accelerate this change by making the robots smarter and more capable of increasing output as the humans may shift into more of a monitoring role.

 

Prediction #2

Resistance Welding Engineers will turn to Generative AI tools to learn and improve their welding process, reducing the need to be an expert to create perfect welds.

By 2050, generative AI will be incorporated into resistance welding technologies so a resistance welding engineer can prompt the AI to act as a virtual resistance welding expert — answering questions, providing explanations, and offering guidance to optimize their welding parameters.

Spot Welding Example:  A welding engineer puts instructions into a generative AI system to learn how to optimize welding parameters for spot welding aluminum sheets based on material properties and joint configuration. The AI iteratively refines parameter sets, considering factors such as weld strength and distortion, and provides visualizations and reports to aid interpretation.

Likelihood: Likely – Generative AI (GenAI), such as ChatGPT from OpenAI, has opened the door for massive transformation within resistance welding. The natural language processing capabilities and vast knowledge base of GenAI solutions can help welding engineers enhance their productivity, decision-making processes, and welding knowledge to improve weld quality, process efficiency, and innovation in welding. This can take many forms:  Knowledge acquisition and explanations for common welding-related questions, problem-solving and troubleshooting assistance, process optimization, documentation and reporting, and even as a training resource for novice welders. The integration of GenAI into modern technologies is happening across many industries today, and given the growing skills gap for welding engineers and the need for proper resistance welding training and education for new, inexperienced engineers entering the field, it’s only a matter of time before our industry takes the leap.

 

Prediction #3

AI will automatically optimize welding parameters for every weld on any material.

In the coming decade, several resistance welding control technologies will incorporate AI algorithms to dynamically fine-tune parameters like electrode force, current, and duration. This advancement will lead to stronger, more uniform welds across a range of materials and joint designs.

Projection Welding Example:  AI algorithms can optimize parameters such as electrode force, welding current, and weld time based on material thickness, type, and joint design. By analyzing real-time sensor data and historical welding performance, AI-driven systems can dynamically adjust parameters to achieve optimal weld quality and efficiency.

Likelihood: Already available, but opportunities to improve – Current adaptive controls can adjust the machine’s ability to yield consistent, high-quality welds in real-time using welding data to account for many variables; however, future innovations could be smart enough to recognize the material being welded and adjust the entire weld schedule accordingly.

 

Prediction #4

AI will improve quality control processes and standards.

By 2030, AI-powered quality control will instantly inspect welding work, detecting imperfections with unmatched accuracy to ensure welds meet the highest-quality standards.

Seam Welding Example: AI-driven quality control systems can monitor seam welds for defects such as cracks, porosity, or irregularities. By integrating advanced monitoring technologies and machine learning algorithms, these systems provide real-time feedback to operators, facilitating prompt intervention and quality assurance.

Likelihood: Already available, but opportunities to improve – Today’s resistance welding monitors provide rich data to assess quality in real-time for each weld. Future advanced systems can augment this data using precision AI-enabled systems to predict the occurrence of defects such as nugget size, expulsion, or incomplete fusion. By analyzing data captured during the welding process, AI algorithms can be better at detecting anomalies and triggering immediate corrective actions based on the available information collected about each weld. The combined visual image analysis and monitoring data will ensure that only the highest-quality welds leave the factory.

 

Prediction #5

AI will impact how we anticipate Preventative Maintenance.

By 2035, AI-powered predictive maintenance will become standard in resistance welding, forecasting maintenance needs, and cutting downtime by proactively spotting and fixing potential issues before they disrupt production.

Seam Welding Example:  Similarly, in seam welding operations, AI can anticipate maintenance needs by monitoring factors like electrode wear, cooling system efficiency, and seam tracking accuracy. This predictive maintenance approach minimizes downtime and ensures continuous operation by addressing potential problems before they impact production.

Likelihood: Very High – Machines that use monitoring technology can already help identify preventative maintenance issues before they lead to machine downtime. AI will only make these systems smarter, helping the operator know exactly what and when to perform maintenance. Some of this is started to be handled with managed services designed to proactively monitor and optimize their resistance welding machine’s mechanical and electrical performance, but AI would likely reduce the reliance on humans to do this.

 

Prediction #6

AI will enable weld schedules to be configured using virtual welding simulations.

By 2030, AI-driven modeling systems will make virtual welding simulations become more popular tools in resistance welding, enabling engineers to optimize processes, validate designs, and train operators in a risk-free virtual environment, accelerating innovation and reducing time-to-market.

Spot Welding Example:  Virtual welding simulations powered by AI allow engineers to optimize electrode placement, welding sequence, and parameter settings for spot welding applications. By simulating welding processes in a virtual environment, AI-driven simulations facilitate design validation, process optimization, and training without the need for physical prototypes.

Likelihood: Will be application dependent – Simulations can be applied to many different areas of engineering, but variability between metallurgy, electrodes, machines, and many other factors can influence the welding process. The simulation may be able to help determine a good starting place for the weld schedule, but process measurements will still be needed to calibrate the simulation to match actual real-world conditions.

 

Prediction #7

Data for each weld will become just as important to welding manufacturer’s customers as the physical part delivery.

Prediction: By 2030, data for each weld will become just as important to seam welding customers as the actual delivery of the finished welded parts.

Seam Welding Example:  Seam welding manufacturers will harness advanced AI and monitoring technologies to gather, analyze, and present this data in easily digestible formats. Their customers will use this information to validate weld quality, ensure compliance with industry standards, and ensure proper delivery of their products. As a result, data-driven transparency and accountability in welding processes will become indispensable for maintaining competitive advantage and building trust between manufacturers and their customers.

Likelihood: Already available, but opportunities to improve – As industries increasingly prioritize quality assurance, traceability, and process optimization,  manufacturer’s customers will demand comprehensive data packages accompanying their welded components. These data packages will include detailed insights into each weld, such as welding parameters, seam tracking accuracy, weld integrity assessments, and real-time monitoring data.  Armed with the data for every weld, these customers will be able to make informed decisions about supplier/manufacturer relationships, ensure the reliability and durability of the welded components within their products, and better understand where a deficiency originated from in their supply chain if there is an issue.

 

 

Prediction #8

AI and the vast amount of welding data will create new needs to store and analyze the results.

By 2045, manufacturers will leverage AI-driven knowledge management systems that enable their welding operations leaders and decision-makers to assess trend data and derive actionable insights, leading to enhanced efficiency and competitiveness for their operation.

Spot Welding Example:  AI-driven knowledge management systems aggregate and analyze welding data from multiple sources, including historical records, process parameters, and weld inspection results. By leveraging machine learning algorithms, these systems identify trends, patterns, and best practices to inform process optimization, material selection, and quality assurance strategies. By extracting actionable insights and correlations, these systems provide decision-makers with valuable guidance for process optimization, defect prevention, and continuous improvement initiatives.

Likelihood: High, but won’t be widespread – The biggest hurdle for this is not AI or data warehousing. It’s that using this data would be a massive culture shift for the resistance welding industry; while other industries have leveraged big data and AI to improve their processes, our industry is a laggard. That said, operations that already perform monitoring to understand the data behind each weld produced will welcome the addition of AI since it will leverage the same data they have today. But the reality is that most manufacturers are not placing a premium value on this data, and therefore, this prediction may not reach reality across the industry for a long time.

 

Conclusion

While it’s fun to pontificate on the impact AI will have on our industry, these are all just predictions. Some will be correct, some may never materialize, and other applications will emerge that are not included in this list; the market will decide. Regardless, AI’s introduction to resistance welding is no mere stride; it’s a giant leap into an era where precision, efficiency, and quality converge.

The inclusion of AI in the resistance welding industry presents a bastion of reliability—where production is lifted from the gritty grind to the peaks of optimal welding efficiency and output. Manufacturers who leverage AI in their resistance welding processes will witness a metamorphosis in productivity and excellence. Authority in execution and confidence in delivery are the power of advanced AI solutions at work. It isn’t the future; it’s the present—ready to deploy and ready to transform.

The future of resistance welding, powered by AI, is bright with possibilities. From improving product quality to increasing operational efficiency and opening new avenues for innovation, the potential is vast. At WeldComputer, we are eager to lead the charge, but more importantly, we are excited to collaborate with our industry peers. 

We invite all resistance welding engineers, manufacturers, and fellow vendors to join us on the journey to command the power of AI for the benefit of our collective industry. For those who join us on the journey to evolve resistance welding into the AI-world, we look forward to working alongside you. Together, we can redefine the standards of excellence in resistance welding and build a future that is smarter, more efficient, and endlessly innovative.