What are some best AI applications in manufacturing

Manufacturers often face a variety of operational challenges, including unexpected machine breakdowns and product delivery defects.Manufacturers can leverage AI and machine learning to improve operational efficiency, inform new product launches, and customise product design. The result is improved product quality, increased speed and visibility throughout the supply chain, and optimised inventory management.

Implementing AI in manufacturing is becoming increasingly popular. Hundreds of variables affect the production process and it is very difficult for humans to analyse them, but AI and machine learning models can easily predict the impact of individual variables in these complex situations. The top five applications for AI in manufacturing are:

• Predictive Maintenance

Manufacturers can use Artificial Intelligence technology to predict potential downtime and incidents.

IoT sensors embedded in machines can help manufacturers detect defects on the production line and feed them into ML-based prediction tools to predict when or if functional equipment will fail. This allows you to schedule maintenance and repairs before a breakdown occurs. Malfunctioning equipment can cause significant disruption to the entire production line, increasing downtime and overall costs. Therefore, it is important to maintain your machines properly and in a timely manner.

• Quality assessment

Quality assurance is about maintaining the desired quality level of a product.

Internal problems throughout the manufacturing process are difficult to detect due to factors such as equipment defects. By observing the functionality of a product, experts often cannot determine the root cause of problems that lead to serious defects in the production process.

The assembly line is a data-driven, interconnected, autonomous network. These lines operate based on parameters and algorithms that provide guidance to produce the best end product. AI and ML technologies easily identify areas needing improvement, giving designers options to correct errors and improve products. Further along the process, AI systems can alert customers if the quality of the finished product is lower than expected, allowing them to respond with adjustments. This helps improve overall product quality and performance.

• Inventory Management

Inefficiencies in inventory management can result in significant overhead costs for manufacturing companies.

AI helps businesses predict consumer demand, manage supplier backorders, and optimise inventory stock levels. This technology provides the foundation for AI analytics that integrates, standardised, and enriches data such as cost, duration, and market-wide trends to provide data-driven recommendations that managers can choose to accept, reject, or modify. AI-based demand forecasting tools are more accurate than traditional demand forecasting methods, allowing businesses to monitor and manage inventory levels based on supply and demand.

• Productive design

The traditional design process for manufacturing is simple.

Creating designs requires a high level of technical expertise using sophisticated software and complex domain-specific tools. If any step in the validation or manufacturing phase is overlooked, a negative feedback loop can be created, resulting in product recalls, redesign efforts, and significant waste of resources. A designer's creativity is limited by how quickly they can produce a design, especially with tight schedules.

 

Productive design methods mimic an engineer's approach to design. The software considers a series of parameters such as material, size, weight, budget, manufacturing method, and CO2 emissions to provide several possible results. This method allows manufacturers to quickly generate thousands of design options. By reducing constraints on the design, negative feedback loops are minimised. For example, Under Armor used generative design algorithms to create shoes inspired by tree roots for optimal flexibility and stability.

• Robotics

One of the most popular applications of AI and ML for manufacturers is AI-based robots.

Industrial robots automate repetitive tasks, reduce human error, and free workers to focus on more creative and productive tasks. AI robots have the ability to monitor their accuracy and performance and be trained to do better. Some are even incorporating machine vision technology to achieve precision mobility when working with human teams on tasks that cannot be fully automated. AI robots are being used in assembly, product inspection, drilling, grinding, and welding, among other tasks.For example, the Tesla Gigafactory has been cited as one of the most advanced factories ever built . Autonomous indoor vehicles (AIVs) are free robots used to navigate on their own without being guided by beacons or magnets. Their main task is to efficiently transfer objects between workstations.

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