Emily Newton is the Editor-in-Chief of Revolutionized, an online magazine showing how technology is disrupting many industries.
There’s growing interest in using artificial intelligence (AI) in manufacturing to achieve numerous benefits ranging from better worker productivity to less machine downtime. Here are five specific advantages that AI can bring to metal fabrication. They’ll show you what’s possible.
1. Reduce Waste
Laser cutting is a popular and reliable metal fabrication technique. Implementing manufacturing automation while using it could lead to even better outcomes. Many laser cutters on the market work with metals and numerous other materials. They typically include cameras as identification measures. It’s essential that the laser cutter correctly recognize the material. A mistake could create messes or even release hazardous chemicals. In this scenario, having a safety kit including things like gloves manufactured by a company that evaluates what users really need and provides products to meet these needs, and preparing different kits for different hazards, becomes really important.
AI and computer science professionals at MIT recently developed the SensiCut system. It identifies 30 materials with deep learning and an optical method that examines a material’s microstructure with a laser. It can also suggest cutting adjustments or handle surfaces containing multiple materials.
Mustafa Doga Dogan, a doctoral candidate working on the project, said, “By augmenting standard laser cutters with lensless image sensors, we can easily identify visually similar materials commonly found in workshops and reduce overall waste.”
People interested in applying AI to get this benefit should first take the time to see which practices or materials typically cause the most waste. That information can guide the next steps concerning how and when to use AI to cut down on waste.
2. Decrease Equipment Downtime
Unexpected equipment failures can become costly problems for metal fabricators and other industrial factories. That’s a primary reason why more companies use AI in manufacturing with the goal of cutting down those outages.
If leaders get notifications of impending equipment failures soon enough, they can adjust workflows, order parts or take other proactive steps to stop equipment problems from causing shutdowns. Moving ahead with AI-based maintenance gets factories closer to the zero-downtime goal. It can also aid decision-makers in choosing when to replace aging machines and see the best return on investment.
A company’s budget may limit its ability to invest in AI for the improved maintenance of all equipment. In such cases, the ideal approach is to determine which machines fail most often or are out of service for the longest periods. That information gives a good starting point when selecting where AI would get the best results when minimizing maintenance.
3. Meet Rising Customer Demands
AI is also useful for helping metal fabricators deal with increasingly high workloads. For example, aluminum is a diverse material used in everything from disposable food trays to fitness equipment. It’s in continually high demand, but certain societal trends can make people want it even more.
During the COVID-19 pandemic, many people bought recreational vehicles to travel and stay away from home safely. Aluminum and other metals are key components in RVs.
Some metal fabricators publicly disclose their annual production capabilities. For example, one aluminum company offering billet casting and specialty alloy manufacturing can make more than 200 pounds every year at two of its facilities.
It’s worthwhile for factory leaders to see which factors severely limit production ramp-up efforts. From there, they can further explore how AI and manufacturing automation could reduce those obstacles.
4. Explore Complementing Technologies
Committing to using AI in manufacturing may encourage metal fabricators to investigate other advanced technologies that could cause a wider future-oriented transformation. For example, 3D printing with metals can create prototypes more quickly or make on-demand products for customers.
Company leaders interested in using AI in manufacturing may even find existing options for combining it with 3D printing. Massachusetts company Markforged has a cloud-based platform for additive manufacturing that uses AI to function. Incorporating machine learning into the product reportedly makes it smarter with every new part produced. The cloud-based model also means that a 3D printer automatically receives software updates.
If metal fabricators are interested in pursuing AI, additive manufacturing or both of those but lack in-house resources, they should think about working with a service provider. Doing that could mean getting access to purpose-built technologies and well-known companies rather than hiring people with the expertise to create the tools from scratch.
5. Achieve Better Quality Control
Manufacturing automation can also bring significant gains to quality improvement efforts. In one recent example, John Deere partnered with Intel to use AI to spot defective welds on its tractors and other industrial equipment.
Finding defecting welds is a challenging task, especially due to the fast-paced nature of most industrial assembly lines. However, this AI application uses advanced algorithms to detect problematic welds and stop a robotic welder after finding them.
More specifically, a neural network-based inference engine can spot issues in real-time and make the necessary adjustments before continuing. Plus, the computer-vision camera used for this application is just 12-14 inches away from the welded material.
When people want to get quality improvements with AI, choosing metrics to track before moving ahead with any new product is a good idea. Then, it’ll be easier to determine if the expected gains happen to the expected extent.
Using AI in Manufacturing Can Help Metal Fabricators Succeed
These potential use cases should help people feel excited about the potential of using AI and other manufacturing automation options to enhance operations. Before finalizing any decisions, the affected parties should remember that the advantages may not be immediately apparent. Still, they typically become obvious if leaders allow enough time to investigate how to best use the technology.
