The world of manufacturing is changing in front of our eyes. Advanced artificial intelligence systems today use data to make decisions and find ways to improve production line efficiency and productivity. AI-enabled systems offer other benefits such as shorter downtimes, production optimization, and predictive maintenance.
The entire process depends on tiny sensors placed across all devices in a production line. The sensors collect operational data and send it to the AI that makes decisions and improves efficiency. Let's take a closer look at how AI optimizes production.
Ways AI Helps Optimize Production and Product Quality
AI is making a real revolution in the way we think about product development and production. Unlike humans that use senses to make decisions, AI uses pure data. It uses heaps of data to find patterns, identify weak spots, and it offers better solutions.
The approach is changing manufacturing practices forever. For example, product development used to be a daunting process that required months of work and multiple prototypes. An average AI solution can develop products by using data. As a result, those products have odd shapes and designs, but they work better than anything a human could invent.
Moreover, AI's application in manufacturing is leading to other business practices that improve efficiency and quality. For example, the digital twin technology can copy an entire production system in a digital environment, allowing you to run various simulations to see how the real-world counterpart would react.
Advanced AI solutions can also help optimize production by minimizing energy consumption. Other practices such as predictive maintenance can help reduce downtimes and increase production efficiency in the long run. Let's take a closer look at how AI aids manufacturing.
Use of AI in Manufacturing
As mentioned above, AI is redefining manufacturing practices by introducing advanced features that are otherwise impossible to achieve. Let's see exactly how it helps improve manufacturing practices.
1. Quality Control Improvement
One of the ways how AI changes manufacturing practices is through highly accurate quality control. The AI gathers manufacturing process data over time, closely monitoring how every piece of equipment behaves. It can find all inconsistencies and quickly identify machines with defects and other issues.
The moment a machine starts behaving suspiciously, the AI signals the maintenance team with a detailed description of what's going on. That way, your maintenance crew can replace the faulty parts and ensure that the product quality stays as high as possible. AI can spot issues the human eye simply can't. It keeps everything aligned and measured to the smallest detail to reduce the number of defective products.
The same approach can help improve many different industries and manufacturing practices. Surprisingly, AI can also help improve 3D printing by learning from video footage of the printing process. It can identify defects and find solutions almost instantly.
2. Creative Product Design
Product design will never be the same. It's one of the areas where AI makes the most difference, and the results are often much better than anything humans can create. Unlike people, AI approaches product design using data rather than aesthetics. As long as you provide it with accurate design information, AI can create some impressive solutions.
The algorithm will review all information and try to create the best design out of many possibilities. Once it's done, it presents the ideal product that offers the best mix of features. All your product designers have to do is create design limits. Moreover, AI can easily create multiple versions of the same product designed for specific applications. The best thing about the design process is that there are no prototypes involved. Every product will work as intended, and it will be better than anything a designer can invent.
3. Predictive Maintenance
Maintenance can be a huge problem when it comes to production. Whenever a machine breaks down or needs regular maintenance, the production has to stop until the issue is resolved. Long downtimes mean higher losses in ROI and customer satisfaction. However, AI can change all that with a practice called predictive maintenance.
It's one of the most beneficial features of production optimization, and it can save manufacturers a lot of headaches and expenses. Instead of dealing with issues when a machine breaks down completely, predictive maintenance offers a proactive solution.
Since AI systems use sensors to monitor all machines in a production line, it can identify the moment when a machine starts behaving out of the ordinary.
That is only possible because of the ML model that is able to predict potential component failures based on the data it gets. The predictive maintenance approach is much better than preventive or reactive maintenance because it maximizes asset life and reduces repair costs. You won't need a spare part for everything anymore. Simply get the part suggested by the AI and replace it before the machine breaks down completely. Service only the parts that need maintenance and focus more energy on other business operations.
4. Production Line Optimization and Integration
Most production lines consist of many different types of machinery and other equipment. Once the IoT sensors are installed, they are able to generate a ton of various data and send it to the cloud. However, all of that data doesn't mean much without a cohesive structure. Imagine how many dashboards and team members you'd need to analyze all data and get a better picture of what's going on with the production.
AI can quickly scan and filter out essential data, allowing you to overview the entire production process. Moreover, the data can also help automate some tasks in the assembly line. For example, if a piece of equipment stops working correctly, the system can notify the supervisor immediately. If something does break down, the system will reorganize activities to keep the production running.
5. Digital Twin Technology
The digital twin technology is one of the most revolutionary practices the manufacturing world ever saw. It uses the same IoT sensors to gather data and recreate an entire factory plant, product, service, or machine in a digital environment. It copies all physical characteristics of any device using cameras, sensors, and other data-collecting practices.
Once it has enough data, the digital twin can recreate real-world information in a digital environment, allowing you to run all kinds of simulations. The technology is helpful for other essential practices such as production line monitoring and proactive management. Companies use DT to simulate all kinds of problems and scenarios to see how they impact real-life machinery and production efforts.
The same approach can improve product development and performance testing. Instead of real-world prototypes, manufacturers can use digital twin technology to test products and their features virtually. They can use what they've learned from these simulations to improve product design.
Conclusion
IoT and AI are truly revolutionary technologies that use real-world data to improve business practices and help companies make better decisions. AI and ML systems become smarter over time, allowing you to improve manufacturing practices even further.
Knowing all the how's and why's before anything bad happens can help you streamline your production, increase product quality, and increase customer satisfaction. Moreover, all the decisions you make will be based on accurate data, which will give you a significant edge over your competition.
