For instance, GPS is now smarter than spatial navigation and we have started to rely on Google Assistant, Apple’s Siri, Amazon Echo. AI has seen great progress and this is because of enhanced processing, algorithms, and a lot of data involved. By utilizing Machine Learning, a lot of data can be analyzed, and insights can be collected.
With the Fourth Industrial Revolution, Artificial Intelligence is expected to eliminate half of the human jobs in the upcoming years. Every single industry will opt to replace human workers that can be performed through Artificial Intelligence. Algorithms and Automation will be the major threats since they offer enhanced efficiency at a lower cost.
So, what can we expect from the Manufacturing industry? Will it make the industry sustainable for the future? In this blog, we have discussed the importance of AI in the Manufacturing industry.
How can AI be the disruptor in the Manufacturing Industry?
Machine, Machinery, and Quality are the leading AI transformation projects in the manufacturing sector as of today. Moreover, the BMW Group makes use of AI to evaluate the component images in ongoing production lines to spot deviations from the real standards in real-time.
Real-time monitoring provides many benefits, including troubleshooting production bottlenecks, tracking scrap rates, meeting customer delivery dates, and more. It’s an excellent source of contextually relevant data that can be used for training machine learning models. Supervised and unsupervised machine learning algorithms can interpret multiple production shifts’ real-time data in seconds and discover previously unknown processes, products, and workflow patterns.
So, here are the key uses of implementing AI in Manufacturing:
Figuring out the defects
In the current scenario, many Assembly lines have no specific system or technologies in place to determine the defects across the entire production line. Even if there are a few in place, they are very basic which requires skilled engineers to build and hard-code algorithms to differentiate between functional and defective products.
Moreover, the majority of the systems aren’t in the learning process or integrate new information which results in numerous false positives that require a manual check by the employees out there. Thus, by integrating this system with AI, manufacturers can save countless hours by seamlessly reducing these false positives and also the hours required for it.
Manufacturing requires complete attention to detail, a thing which is the major need in the electronic space. When considering the history, quality assurance of the Manufacturing is considered a manual job which is dealing with experienced professionals to make sure electronic and microprocessors are being manufactured effectively and the circuits are properly manufactured.
In the present situation, image processing algorithms are responsible for validating whether the specific item is perfectly produced or not automatically. By installing cameras at the major points along with the factory floor, this can be made possible in real-time.
Assembly Line Integration
Most equipment manufacturers today use a vast amount of data on the cloud. However, this information tends to be siloed and doesn’t look good together. In order to get a holistic picture, it requires several dashboards and also a subject matter expert to make sense.
Thus, by creating an app that can pull down the data from the breadth of IoT equipment that is connected to the device, you can make sure that you are getting a complete view of the operation.
Assembly Line Optimization
Added, by layering in Artificial Intelligence into your IoT ecosystem, with this countless amount of data, you can create a variety of automation.
For instance, when equipment operators are showing signs of issues, supervisors would get notified. Hence, when a piece of equipment breaks down, the system can automatically trigger contingency plans or other reorganization activities.
In addition to this, AI can also help in organizing design products. So, here is how it works:
A designer or design engineer will input the design goals into generative design algorithms. These algorithms will be responsible for exploring all the possible permutations of solutions and generate design alternatives.
Artificial intelligence and its subset, Machine Learning are the crucial elements in the Manufacturing factory 4.0. They are also responsible for improving the Supply Chain, making them interactive to changes in the current market. Hence, managers can improve their tactics and strategy vision by considering AI suggestions.
These insights are generated via AI-based or linking together a number of various factors such as political situations, weather conditions, consumer behavior, economical status, etc. When it comes to predictions, it can calculate staff, inventory, the material supply, etc.
Better Product Development
As we have discussed the Generative Design, this method allows in providing a detailed brief created by humans into an algorithm. The information in the brief can consist of various parameters such as budget, the available number of resources, the time needed.
This algorithm analyzes various possible variations and offers a few optimal solutions. These solutions are further analyzed by pre-trained deep learning models which can add increased insights and pick certain options. This process can be repeated until you find the product an effective one.
In the next 20 years, we can expect digital technologies such as Robotics, AR & VR, Data Analytics to rule the world in addition to Artificial Intelligence to enhance the business operation in the Manufacturing industry.
Being one of the Digital Transformation Companies, we have realized the role of AI in various sectors and help businesses in automating their real-time operations which can improve the efficiency of their processes. If you are one among them, you can also get in touch with us to enhance your business and your revenue.