Once considered the rich imagination of science fiction writers, AI (Artificial Intelligence) has made its way into our daily lives and businesses in recent years. The manufacturing sector has embraced the use of AI and robotics to step into the next era of manufacturing with Industry 4.0.
Smart manufacturing powered by IoT devices has ushered the era of IIoT (Industrial Internet of Things) that allows for streamlining and expanding manufacturing outputs. The usage of these smart devices has helped manufacturers increase product quality, accelerate manufacturing speed and enhance production efficiency.
Cobots (collaborative robots) have taken over the factory floor across industries. These robots work hand-in-hand with human employees, helping them increase product efficiency as well as speed up production. The capabilities of Artificial Intelligence enhance human capacity, ensuring superior quality assurance.
AI empowers Factories of the Future to:
AI empowers Factories of the Future to:
- Detect and eliminate defects in the assembly line itself
- Deploy preventive maintenance to reduce expensive downtime
- Validate quality in the factory floor ensuring superior quality assurance
- Reduce costs of manufacturing small batches, paving the way for more customisation
- Improve employee satisfaction by migrating repetitive and mundane tasks to AI
- Respond and product in accordance with real-time supply demands
#1: Defect Detection & Elimination
In the factories of today, the assembly line and quality control team are two separate entities. There are no systems in place to identify defects in the production line. Quality control happens only after the product is produced, leading to extra time and wastage of materials.
Even those assembly lines that have quality control in-built into the system are very basic and rudimentary, and often expensive. They require employing skilled engineers who hard code algorithms to identify the defective components from functional ones. The problem with these systems is that it is manual requiring workers to identify defective components manually, which is time-consuming and not 100% accurate.
By introducing artificial intelligence and self-learning IIoT systems, manufacturers can reduce the time and resources spent on manual quality control. These sophisticated and smart systems will be able to identify defective products accurately, without increasing false positives. The efficiency of quality control can be increased significantly with smart systems, while reducing cost and time.
#2: Quality Assurance
Quality control is one of the biggest factors in manufacturing. Traditionally, quality control is mostly a manual job required trained quality control processors to check and verify each component individually. In industries like electronics, aerospace and several others, quality control is an absolute necessity with no margin for error.
Today, AI is ready to take over quality control. By using cameras and image processing algorithms, these smart systems can automatically check whether each product has been produced accurately to the required specifications.
#3: Get a Holistic View of the Assembly Line
One of the biggest challenges facing manufacturers today is that information tends to be siloed. Data collected from the factory floor is uploaded to the cloud. This data becomes obsolete as manufacturers lack the right algorithms to harvest and process these huge volumes of data.
Using AI, you can develop a smart app that gives you a holistic view of all operations. This app can pull data from all the IIoT devices in your network, and collate it in an easy-to-understand manner. Using this data, you can make smart business decisions.
#4: Assembly Line Optimisation
The benefits of integrating AI in the assembly line go further. For example, when equipment operators show signs of tiredness and fatigue, the IIoT sensor can send an automatic notification to the supervisor, preventing accidents. Another example would be when a key piece of equipment breaks down, the system can automatically trigger the reorganisation plans without wasting time.
#5: Predictive Maintenance
Predictive maintenance is proactive maintenance that helps in reducing costly downtime. In this scenario, data is collected in real-time to monitor the health of all equipment. By using data processing algorithms, and analysing usage patterns, machine learning algorithms can predict and prevent failures. A large number of factories are using predictive maintenance to make strategic decisions on when to perform maintenance. Predictive maintenance using machine-learning based solutions helps in increased cost savings and reducing unplanned downtime due to repairs and failures.
The Future of Manufacturing is Automated
Considering the incredible benefits of AI and machine learning in manufacturing, it’s not a surprise that 85% of manufacturing executives believe that AI helps you gain an edge over their competitors. At FutureAlgos, we empower your manufacturing solutions with our smart machine-learning and AI solutions. With years of experience in this industry, we ensure that each of our clients is poised for success. Our solutions are customised to help you transform your business from top to bottom, getting you ready for the future of manufacturing. To know more, get in touch with our experts at FutureAlgos today!
