March 16, 2021
Artificial intelligence (AI) technology has progressed rapidly in the past decade. AI algorithms can process data and make decisions in real-time, achieving a level of decision-making that typically requires human input.
Manufacturers began incorporating AI in the 1960s. However, early AI technologies essentially provided automation and could not make complex decisions.
Manufacturers now use AI for a wide range of applications. More companies are starting to discover the value of industrial artificial intelligence. About 63% of manufacturers are already reporting revenue increases from AI adoption.
Here is a closer look at some of the most impressive ways to incorporate AI in manufacturing.
Industrial AI for Product Development
AI is used throughout every manufacturing process, including product development. Manufacturers can use AI software to develop digital twins. A digital twin is a digital representation of a real-world asset.
Manufacturers can use digital twins to evaluate products before physically producing the products. The use of AI allows manufacturers to detect design flaws or limitations that may result in less efficient production runs. Detecting these issues before production helps boost efficiency.
The use of digital twins and AI software also simplifies design customisation. Designers, engineers, and clients can review and test different designs. AI software can evaluate the potential performance of the design, allowing clients to use performance metrics to select the best product.
AI and ML for Generative Design
Along with providing increased design customisation, AI combined with machine learning (ML) technology can provide generative design options. The generative design relies on ML algorithms to perform the work of an engineer.
After entering design parameters, the generative design software begins producing all possible variations of the design that meet the user-defined parameters. Manufacturers can almost instantly generate a multitude of design options for a single product.
The variety of designs may help manufacturers and clients address specific problems or quickly generate a prototype of a product. General Motors already employed AI-powered generative design technology to generate over 150 design ideas for a seat bracket that was 20% stronger and 40% lighter compared to the previous bracket.
AI-Powered Predictive Maintenance
AI systems are commonly used in manufacturing for predictive maintenance. The software can analyse data captured by sensors connected to machines and equipment. Based on past performance and current performance, AI technology can provide better forecasts for maintenance and repair activities. The software may analyse the gradual reduction in output or increased occurrences of defects.
Manufacturers can plan for maintenance and repair on specific machines instead of waiting for scheduled maintenance. This targeted approach is more efficient, as it involves less downtime. Better predictions of failure also help extend the service life of machines.
Advanced Robotics with AI
Manufacturers have used robotics for automation for several decades. However, adding artificial intelligence (AI) increases the effectiveness of robotics systems. AI-powered robotics do not need to follow a predefined set of instructions. They can analyse data and make real-time decisions to improve the efficiency of manufacturing processes.
For example, an AI-powered welding machine may detect common weld defects, such as inclusions or incomplete fusions. It may then take corrective action to address the defect.
AI can also allow robotics systems to self-monitor their performance. The latest robotics can assess accuracy and output to increase its productivity.
AI Software for Price Forecasting
Manufacturers must deal with the volatile prices of raw materials. Prices fluctuate frequently, creating challenges for estimating the cost of future manufacturing processes. AI software can help manufacturers accurately forecast the price of raw materials.
AI-powered technologies are available to monitor the market and detect price trends. The software can estimate the price of different raw materials for specific periods, allowing manufacturers to better forecast the cost of manufacturing.
Improving Quality Assurance with AI
Improved quality assurance is one of the most innovative uses of AI in manufacturing. AI systems can monitor every manufacturing process to detect defects instantly.
A typical AI-powered quality assurance system includes the use of a camera-based computer vision system. The camera scans incoming parts. The AI system then compares images of the parts to images of non defective parts.
When a defect is detected, the AI system may trigger an alert, allowing workers to adjust settings and prevent additional defects. Depending on the available features of the software, the AI system may also self-correct to prevent defects. By reducing defects, manufacturers cut down on waste and increase the speed of their operations.
AI for Process Optimisation
Along with preventing defects, manufacturers can optimise additional manufacturing processes using AI software. AI-powered tools can monitor any process or activity. For example, a manufacturer may use AI software to analyse bottlenecks in its production cycle, such as delays in the supply chain or limited output from specific machines.
The advantages of process optimisation grow when manufacturers share big data on a decentralised platform. By comparing similar manufacturing processes across different facilities and regions, manufacturers can select the most efficient solutions.
Better Inventory Management with AI
AI-powered software can improve inventory management practices. The software can analyse the needs of the production run at different stages and automatically submit orders for necessary materials. When combined with accurate price forecasting, manufacturers may enjoy significant cost-savings.
The advantages of AI for inventory management may allow manufacturers to maintain smaller facilities and warehouses. Manufacturers can avoid over ordering and under ordering, streamlining the way that they deal with their inventory.
Conclusion
These are just a few of the current applications of AI in manufacturing. Artificial intelligence has evolved to provide a wide range of advantages for businesses in almost any sector. However, the manufacturing sector can currently be seen as one of the sectors, benefitting most from these technologies.
Manufacturers can implement AI to improve predictive maintenance, shorten production cycles, and identify issues that lead to waste. Levering AI technologies lead to cost-savings and faster decision-making. Manufacturers can also improve the safety of their plants with AI-powered robotics and software.
Companies that want to enjoy these benefits should begin mapping out their AI opportunities. In the coming years, the adoption of AI technologies may become essential in the manufacturing sector.
