5 ways industrial AI is revolutionizing manufacturing

liu, tempo Date: 2021-08-04 14:34:52 From:ozmca.com
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Artificial intelligence (AI) is the most commonly used method in manufacturing to improve the overall equipment efficiency (OEE) and the rate of first product produced. Over time, manufacturers can use AI to increase uptime and improve quality and consistency, leading to better predictions.


Artificial intelligence (AI) is the most commonly used method in manufacturing to improve the overall equipment efficiency (OEE) and the rate of first product produced. Over time, manufacturers can use AI to increase uptime and improve quality and consistency, leading to better predictions.


As with many components of digitisation, the implementation of AI seems unstoppable. How to effectively use and manage the billions of data points generated by intuitive computing power and the machines connected to them is a common concern among manufacturers. Many are unsure how to get started, and often attribute their caution in adopting AI to costs, IT needs, and/or fears of not being ready for “Industry 4.0”.


5 ways industrial AI is revolutionizing manufacturing


To remain competitive, it is important for manufacturers to adapt to more data-driven business models. This usually includes personnel reorganizations and hardware and software upgrades.


Artificial intelligence, a concept often associated with the future, is now a reality that can be applied to your factory. Here are five ways in which industrial AI is revolutionizing manufacturing and implementing techniques:


Predictive and preventive maintenance


Some of the largest downtime in production operations can be caused by mechanical or electrical failures that take core mechanical components offline. Usually, failures can be easily prevented by following the machine’s recommended preventive maintenance plan. Project managers are often overlooked or not optimized to achieve optimal timing. With the power of iot devices, sensors, MES data and machine learning algorithms, manufacturers can leverage many machine data points to predict failures. PM planning can be optimized prior to predicted failures to keep the machine in first-class condition and the production shop running smoothly.


Supply chain optimization


Today’s supply chain is a super-complex network of thousands of parts and hundreds of locations. Ai is becoming a necessary tool to bring products from production to customers in a timely manner. With machine learning algorithms, manufacturers can define optimized supply chain solutions for all products. Questions like “How many resistors should I order next quarter?” “Or” What is the best route for product A to be transported? “can eventually be answered without relying on the best guess approximation.


industrial AI


Internal inventory management is a major challenge in itself. Production lines rely heavily on inventory to ensure the supply of production lines and the production of products. Each process step requires a certain number of components to operate; Once used, it needs to be replenished to continue processing. Keeping the factory floor stocked with all the necessary inventory is a challenge that AI can help manage. The AI can view component counts, expiration dates, and optimize distribution across the plant.


Production optimization


Process optimization can be a data onerous task involving countless historical data sets. Determining which process parameters produce the highest product quality is not an easy task. Manufacturing and quality engineers have been conducting extensive experimental designs to optimize process parameters, but these designs are often expensive and time consuming. With the rapid data processing speed of AI, engineers can find optimal process formulations for different products. Questions like “What delivery speed or temperature should I enter to get the highest yield?” “Or” What machine should I use to make this high-pitched emerging technology circuit board?” AI will continue to learn from all production data points to improve process parameters.


The predicted yield


When discussing the use of artificial intelligence in manufacturing, the topic of volume forecasting always comes up. The return on investment for an AI model with high predictive accuracy is infinite. Forecasting production can better prepare the supply chain and inventory management for future component demand. Knowing if production is lower than expected can alert production management to increase production time to meet demand. Production forecasting is a complex problem that involves a lot of data and requires artificial intelligence to solve.


Augmented and virtual reality


As AUGMENTED and virtual reality technology continues to improve and more big companies develop devices for the market, it’s only a matter of time before manufacturing fully adopts them. Virtual reality can help better train product manufacturers to perform assembly or preventive maintenance tasks. Augmented reality provides real-time reports driven by machine learning on the factory floor or in the field to help quickly identify defective products and areas for operational improvement. The AR/VR manufacturing applications are endless and can play an important role in solving today’s challenges.


Benefits: Energy management


Ai can help the often overlooked field of energy management. Most engineers don’t have time to analyze the cost of a plant’s energy consumption. Ai research into the energy consumption of production operations can significantly reduce operating costs. In addition, reduced costs can allocate more capital to process improvement resources, leading to higher yields and quality.


What if you had a system that could automatically detect production problems in real time before they occurred?


The benefits will be predictive maintenance, inventory and product outlier detection, taking operational excellence to new levels in an accessible and intuitive manner.


This will change your competitive advantage. Yes. Data is the new bacon, and AI is taking it to new heights.

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