AI-powered tools are being rolled out in all types of industries, and manufacturing is no exception. But what can AI even do in manufacturing? Here’s an overview.

AI-Powered Robotics

Using robotic arms in manufacturing is nothing new – industrial robots have been deployed in the assembly line for a while now, carrying out repetitive tasks like welding and gluing under human oversight.

However, software limitations mean that these robots are limited to a very small subset of tasks. They can only interact with components placed in very exact positions, and perform very specific tasks in well-defined order. This means they struggle in any unordered physical space, and even a slight misalignment can bring it to a halt. 

That is where AI-powered robotics comes in. These new-age RPA (Robotics Process Automation) solutions can use AI algorithms to “learn” tasks in a more flexible way than before, and even use image recognition to more naturally interact with an unstructured physical space instead of being limited to exact layouts. 

This makes them usable for simple repeated tasks like stocking crates or transporting inventory, which can free up skilled workers for more complex tasks while speeding up the workflow.

Quality Control and Inventory Tracking

Machine learning-based protocols are also good at spotting specific patterns in datasets, or finding outliers. This makes them well suited to quality control stages of manufacturing, picking out defects from finished products more thoroughly than a human can. 

This ability can also be utilized to locate parts and components, since it can keep track of the features of every piece semantically. A person just has to describe their needs for the AI to generate the complete description and name of the component in question, easing the search for parts.

Efficient Resource Management

At its core, a machine-learning algorithm functions by performing predictive analysis on large datasets. This ability also makes it amazing at predicting data trends, which can be used to more efficiently manage resources. 

Supply chains have always been very variable and complicated since demand can shift swiftly while it takes many stages for a product to make its way to the end. But with predictive analysis, an AI can anticipate these fluctuations in demand and adjust the supply chain in preparation, letting you meet your goals without any disruptions. 

Not just the supply chain, this technology can be applied to workforce and energy management as well. AI can look at past data to understand the usage patterns and suggest schedules that make the best use of the resources you have, allocating the hours more efficiently than possible otherwise.

Predictive Maintenance

We have mentioned how AI algorithms are good at predicting future trends in a dataset and how this is used to predict market shifts and adjust supply chains and other resources accordingly. But this can also be used to accurately predict mechanical breakdowns and other failures. 

Faults are the biggest time wasters in the manufacturing process, as the breakdown of even a single machine in the assembly line can bring the entire operation to a grinding halt. Of course, regular maintenance can prevent that, but that eats up valuable production time itself. 

With AI, the likelihood of impending faults can be predicted in advance, letting you slot in maintenance at the ideal time. This predictive maintenance is far more efficient and effective in reducing failures and ensures that the production line runs smoothly.

Generative Design

AI is good at generating designs given limiting parameters, but it is not perfect. As such, it is not possible to have the AI effectively design products or solutions meant for deployment. 

At the same time, it can be a great way to quickly iterate through diverse design templates in the ideation phase. Engineers can use AI to generate multiple possible options that they can then explore in-depth, working out the kinks to create a final polished solution. 

This lets them quickly “bootstrap” design, spending more time on fleshing out the important details than struggling to get the ball rolling. Not to mention introducing design directions that they might not have considered otherwise.

Is AI Manufacturing the Future?

AI is making inroads into a lot of industries and seems poised to disrupt even more. For manufacturing, this currently means incremental gains – efficient management, faster prototyping, and automation of repetitive tasks. 

However, in the long term, there are more ambitious projects in the works. “Factory in a Box”, for example, is a concept of a modular manufacturing system that uses purely automated units and IoT sensors to produce goods. The idea is that these modular units can be easily deployed in any location, allowing for local production and cutting down logistical difficulties. 

Even without that, the arrival of AI-capable embedded systems has made the deployment of AI solutions a practical prospect. Infusing the power of AI into your manufacturing process is a viable way of improving productivity without massive changes.