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Advantages and Practical Applications of Artificial Intelligence in Manufacturing

July 30,2025

Introduction

Artificial intelligence (AI) is becoming a core force transforming the manufacturing landscape. Its practical applications in production span a wide range of areas, from shop floor automation to smart supply chain management, enabling faster responsiveness and higher productivity for factories.
The accelerating adoption of AI in manufacturing and industrial scenarios demonstrates enormous growth potential. According to a recent Accenture study, AI applications are expected to create up to $3.8 trillion in economic value for the manufacturing industry by 2035.
A Deloitte survey on AI in manufacturing also reveals market optimism—a staggering 93% of companies surveyed believe AI will become a key driver of innovation and growth in the industry.

So, where will AI deliver the greatest benefits?
Its application scenarios span the entire manufacturing lifecycle, including collaborative robots that can safely collaborate with humans and high-value areas such as predictive maintenance based on real-time sensor data.
AI can also enable unmanned production, defect detection, and faster product development. Clearly, the potential of AI extends far beyond the laboratory and is gradually being integrated into production lines.
Next, we'll look at several industry examples to see which companies are already leveraging AI to achieve efficient production and green transformation.

Precision Gripping Empowers Food and Beverage Production
The food and beverage processing industry was one of the earliest sectors to adopt AI-powered automation. Especially in strictly regulated production environments, AI can ensure consistent and repeatable operations.
Robotic gripping technology is a particularly prominent application. The high-speed flow of items on the production line requires precise manipulation by robotic grippers to avoid damage.
The US-developed mGripAI system combines flexible robotic grippers, 3D vision, and AI algorithms to achieve high-precision gripping of meat, fruit, vegetables, and baked goods. The system excels at handling difficult ingredients like chicken nuggets.
Powered by NVIDIA's high-speed computing power, it acts as the "eyes and brains" of the gripper, helping food companies improve safety and expand production capacity.

Redefining Quality Inspection in Automotive Manufacturing
In the automotive manufacturing sector, Audi has introduced AI to the quality inspection of body spot welds. At its Neckarsulm plant, the new system can inspect 1.5 million welds on approximately 300 vehicles per shift, significantly improving efficiency compared to previous manual spot checks.
Previously, employees typically only used ultrasonics to inspect 5,000 welds per vehicle and performed random analysis. Now, AI technology automatically identifies anomalies, freeing human inspectors to focus on complex issues.
Volkswagen Group plans to expand this solution to more plants, including Emden and Audi's Brussels plant.
The collected inspection data can also be used for predictive maintenance, driving the digital factory transformation under its "Automotive 2025" initiative.

Making NC Programming More Efficient
CAM Assist is an AI-powered Autodesk Fusion 360 plug-in developed by CloudNC that automates the generation of process paths for three-axis machining.
It reduces repetitive manual operations in NC programming and generates toolpaths in seconds. Users report that the tool can handle 80% of machining strategies, significantly reducing production setup time and enabling rapid manual fine-tuning. In addition to accelerating programming, it also helps new employees quickly get up to speed and improves the accuracy of work order estimates and quotes. CloudNC is currently expanding this functionality to four- and five-axis machining.

Virtual Twins Promote Clean Energy Deployment
Siemens Gamesa is applying AI-powered virtual twin technology in the wind power sector to optimize wind farm layout and operation strategies.
This solution, combining physical modeling with machine learning, simulates power generation performance under varying wind directions, terrain, and turbine configurations, enabling precise determination of equipment placement and control strategies.
Globally, this technology has increased production capacity and reduced costs for wind turbines with a total installed capacity of over 100 GW. This optimization approach is particularly critical given that global wind power demand is expected to quadruple between 2020 and 2025.

Conclusion
These cases demonstrate the diverse application of AI in manufacturing—from food processing to automotive quality inspection, from CNC programming to renewable energy optimization, AI is improving efficiency, reducing costs, and promoting sustainable development.
Through analysis, optimization, and automation, AI not only helps companies reduce errors and downtime, but also accelerates decision-making and helps them seize market opportunities.
In the highly competitive manufacturing environment, proactively deploying AI not only means greater productivity and flexibility but also lays a solid foundation for future growth.

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