Manufacturing industry is one of the hardest hit industries during the COVID-19 pandemic. With factories being shut across the globe, the viral outbreak has affected everyone from frontline workers to owners. There are numerous lessons to learn from this crisis. One of which is to emerge more resilient and grow stronger after the worst is over. Artificial intelligence in manufacturing will play a pivotal role in reviving the industry. Therefore, it’s time to strengthen AI-driven pilots and leverage the analytics offered by the technology.
Why AI significant for Manufacturing Industry?
Let’s go over the role of AI in manufacturing industry:
1- Quality Assurance
Quality assurance necessitates attention to detail. For instance, in microprocessor manufacturing, companies hire highly skilled engineers for quality assurance. However, with the advent of AI, this task has been greatly simplified and automated. Artificial intelligence-powered cameras can detect minute defects through image processing algorithms in real-time.
Machine learning in industry 4.0 can play a decisive role in improving the quality assurance factors in near future.
2- Generative Design
Besides quality assurance, AI in manufacturing is also being utilized for designing products. For that purpose, researchers and scientists have created design algorithms that can help generate various design alternatives. Each iteration is tested and improved using machine learning. The engineer or designer can feed design goals using which AI can generate design models.
3- Assembly Line Integration
Much of the information sent by manufacturing equipment to the cloud is siloed and lacks integration. Without the use of AI in manufacturing, companies require subject matter experts and several dashboards to get an overall picture and make sense of such enormous data. However, the Internet of Things (IoT) and artificial intelligence can together solve this problem.
Manufacturers can facilitate this process by integrating IOT-powered equipment with an AI-powered application. Therefore, the role of AI in production provides a holistic picture rather than separate views.
4- Sustainable Manufacturing
Most of the materials used in the production process, including electronic components, are harmful for the environment. It leads to generation of e-waste, huge energy consumption, and plastic production. Moreover, fabrication of lithium-ion batteries requires extraction of graphite, cobalt, and nickel which is degrading for the environment.
According to Jahda Swanborough, a world leader for environmental issues, artificial intelligence in the manufacturing market can solve the sustainability problem to a great extent. It can help develop eco-friendly materials. This has already been tested out by Google at its data centers.
5- Price Prediction
Prices of necessary resources in manufacturing can fluctuate greatly. It makes the decision to buy materials all the more difficult. Managers have to determine the right time to buy materials as well as the competitive prices. For instance, if they were to purchase stainless steel, they will have to figure out the current price of nickel and metal exchange listings.
One of the best AI in manufacturing examples is price forecasts. Machine learning algorithms are being used to analyze competitive and historical data at Amazon to offer competitive prices thereby boosting their profitability.
Future Prospects of AI in Factories
The future of AI in manufacturing is yet to unfold. The technology is still in its nascent stage and there is a lot to develop and uncover. Artificial intelligence can enable greater customization and reduce costs of single-run and small-batch goods. Not just that, real-time changes in the supply chain can be tracked using AI.
Manufacturers can significantly reduce downtime by deploying predictive maintenance and speeding-up the process. In addition, mundane and repetitive tasks generally performed by humans can be delegated to AI in order to improve employee satisfaction. In future, manufacturers will be able to perfect the production process by eliminating defects.