How Will AI Impact Manufacturing?

February 12, 2025

Custom metal manufacturing relies on stringent quality control systems to create consistent products free of defects. Traditionally, this has taken the form of visual inspections, hardness and tensile testing, and the use of tools like laser cutting and stamping machines — but these methods are far from perfect.

Integrating more artificial intelligence (AI) and machine learning (ML) into manufacturing processes is helping metal manufacturers overcome common operational challenges, improve overall efficiency and produce higher-quality output.

Understanding AI in Metal Manufacturing

The manufacturing industry as a whole is experiencing Industry 4.0, also known as the Fourth Industrial Revolution, which focuses on the relationship between smart technology, sensors and advanced analytics tools.

In metal manufacturing, enhancements like the following have already provided advantages:

  • Machine learning: ML models use AI-powered algorithms and historical data to enhance machinery performance and more accurately predict when equipment will require servicing. Certain models can empower decision-making, like optimizing CNC cutting paths and making real-time adjustments as needed. ML ultimately leads to less material waste, lower costs and faster turnaround times.
  • Robotics: Metal manufacturing robotics can handle manual processes that are too dangerous or complex for humans to complete. This makes them a powerful production assistant that keeps employees safer and allows them to focus on tasks that require more skilled attention. Automated robotics — such as robotic welding systems — perform repetitive, strenuous tasks faster and often with greater precision, helping minimize downtime and improve production speeds.
  • Computer vision: Computer vision (CV) uses AI to visualize metal characteristics and various data parameters to detect defects, errors and anomalies.

Opportunities Presented by AI in Metal Manufacturing

One of the key aspects of AI in manufacturing is that it becomes more adaptive and accurate as it analyzes more data and manufacturing scenarios. It’s also a fast-advancing field that’s constantly producing and improving new technologies to make custom metal manufacturing faster, safer and more precise. Some areas worth exploring include:

Quality Improvement

Accessing real-time analytics from sensor input lets you make more informed decisions. By using a combination of historical data and predictive ML, you’ll have critical information at your fingertips when you need it. This could take the form of:

  • Predictive maintenance: Downtime costs small- and medium-sized manufacturers up to $150,000 per hour — and the effects go beyond immediate financial strain. Delayed production and delivery times can lead to a loss of business and high-paying contracts. AI can help through predictive maintenance, which analyzes past and live data to anticipate equipment failure and receive alerts when components are reaching the end of their life span. You can even use AI to calculate the best time to schedule planned preventive maintenance to cause the least amount of disruption.
  • Machining accuracy: AI tools can identify flaws in your custom metal components so you can deliver a higher quality product without incurring costly waste. One example is ultrasonic testing, which uses sound waves to penetrate deep into the metal’s interior to detect inconsistent densities or cracks that would otherwise be invisible to the naked eye.
  • Defect prediction: Armed with enough high-quality data, ML models can analyze historical information and live sensor input and compare those findings to your specific parameters to predict when a defect is likely to occur along your production line.

Supply Chain Optimization

AI has and will continue to transform the manufacturing supply chain — companies that have successfully implemented it have improved logistics costs by 15% and service levels by 65% compared to their competitors.

AI algorithms can help you calculate the best time to schedule each phase of production, including specific toolpaths and machine availability, to deliver faster lead times with minimal cost. For example, an AI-powered CNC machine can automatically establish and prompt operating procedures, design adjustments and tool changes without human intervention.

Because AI can analyze enormous amounts of data, you can also use it to improve your forecasting based on current market trends and information about previous sales and customer behavior.

Customization and Flexibility

If you manufacture custom pieces, you know how crucial precision cutting and flexible systems are to fulfilling different specifications. You’re likely also familiar with the challenge of keeping the right type and amount of materials on hand to fulfill orders on time without overstocking. AI-enhanced production lines can eliminate these obstacles through adaptive manufacturing tools and processes and data-driven inventory management.

Challenges and Considerations of Artificial Intelligence in Production

While the benefits of AI in manufacturing are vast, there are also a few factors and barriers to consider.

Workforce Implications

Fewer people are applying for manufacturing positions, and approximately 1.9 million jobs could remain unfilled in the industry by 2033. Despite this, many people are unwilling to integrate AI into their processes due to distrust or a lack of understanding. Being transparent is essential — managing engineers and project managers should clearly demonstrate how each tool will make employees’ jobs safer, easier and more comfortable.

Most AI tools are useless without human intelligence to interpret and act on things like preventive maintenance alerts and defect detection. Implementing AI even opens opportunities for employees to learn new skills or take on new roles.

Cost of Integration

While optimizing production with AI can ultimately lower operational expenses and shorten the total time-to-market, the cost of integrating tools and smart sensors is a large barrier to entry for many small- to midsize metal manufacturers.

One strategy to combat this is incremental optimization, which focuses on introducing AI slowly, starting with the areas that will have the highest impact. For example, optimizing a CNC machine for more accurate and precise cutting may have more immediate benefits than AI-powered inventory management software.

Data Quality Assurance

AI can only make accurate and informed decisions and predictions if it has a large enough volume of consistent, error-free, high-quality data. Manufacturers seeking to implement ML and AI-driven tools must have processes in place for data collection and storage, including regular audits.

Future Trends in AI and Metal Manufacturing

The future of metal manufacturing promises faster, more efficient production. As our understanding of AI grows and ML models become smarter, expect to see more of these emerging trends:

  • Increased IoT integration: The Internet of Things (IoT) — which refers to the overall network of smart technology, sensors, AI tools and connected devices — will likely expand to offer more cloud-based options that reduce the need for on-site data servers and storage and enable more remote processes.
  • Enhanced machine-human collaboration: In an effort to overcome user distrust and make AI more accessible, there is a growing interest in developing more user-friendly algorithms and AI-powered tools. As manufacturers increase their focus on machine-human collaboration, they benefit from advanced planning and production assistants like digital twinning and augmented and virtual reality devices.
  • Zero-touch automation: Zero-touch automation is the move toward automating more manual and repetitive manufacturing tasks, such as preventive maintenance, to help machines operate with less need for human intervention. Skilled engineers can then use that extra time to focus on more impactful areas of operations, like enhanced designs.
  • Improved sustainability efforts: Because AI can help manufacturers make smarter and more accurate predictions for things like inventory management and flaw detection, facilities will continue to reduce unnecessary material waste and premature equipment breakdown. Tools will continue evolving to make production more sustainable. For example, AI algorithms will be able to make better decisions about machine optimization to reduce a facility’s overall energy consumption.

Optimize Your Manufacturing Processes With Caldera Manufacturing Group

AI is a critical component in the future of metal fabrication and may be the key to staying competitive in an increasingly diverse market. If your facility lacks the space or capital to add permanent upgrades, Caldera Manufacturing Group can help. Contact us today or explore our range of full-service metal fabrication services to see how we can supplement your operations with the aid of advanced, state-of-the-art equipment.

Previous ArticleManufacturing for Data Centers