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Manufacturing Technology Insights | Monday, June 28, 2021
Manufacturers can use AI systems to predict when or whether functional equipment will fail, allowing maintenance and repair to be scheduled ahead of time.
FREMONT, CA: Manufacturers face several challenges regularly, such as unexpected machinery breakdown or poor product delivery. Manufacturers may increase operational efficiency, launch new goods, adjust product designs, and plan future financial measures to advance their Artificial Intelligence (AI) transformation by leveraging AI and machine learning. The COVID-19 pandemic has also raised manufacturers' interest in AI applications. The panic caused by lockdowns may have prompted manufacturers to turn their focus to AI.
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Some of the most common AI uses in the manufacturing industry include:
Quality Assurance
The maintenance of a desired degree of quality in a service or product is known as quality assurance. Assembly lines are networks that are data-driven, networked, and self-contained. These assembly lines are guided by a set of parameters and algorithms that help them produce the finest possible end products. Because most flaws are apparent, AI systems can detect variations from expected outputs using machine vision technologies. When a final output is of poorer quality than planned, AI systems alert users, allowing them to respond and make changes.
Predictive Maintenance
By evaluating sensor data, manufacturers use AI technology to identify potential downtime and accidents. Manufacturers can use AI systems to predict when or whether functional equipment will fail, allowing maintenance and repair to be scheduled ahead of time. Manufacturers can increase efficiency while lowering the cost of machine failure thanks to AI-powered predictive maintenance.
Inventory Management
Machine learning systems, which are adept at demand forecasting and supply planning, can help to promote inventory planning activities. AI-powered demand forecasting technologies outperform traditional demand forecasting approaches (ARIMA (AutoRegressive Integrated Moving Average), exponential smoothing, and so on) used by engineers in manufacturing plants. These solutions enable firms to manage inventory levels better, reducing the likelihood of cash-in-stock and out-of-stock events.
Generative Design
Machine learning algorithms are used in generative design to emulate an engineer's approach to design. Designers or engineers enter design parameters into generative design software (such as materials, size, weight, strength, production processes, and cost limits), and the software generates all conceivable outcomes based on those factors. Manufacturers can swiftly produce thousands of design choices for a single product using this technology.
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