By Hemant Aggarwal, COO, Netweb Technologies
The convergence of manufacturing and artificial intelligence (AI) has ushered in a new era of innovation in modern industry. AI-powered supercomputing systems are at the forefront of this movement, revolutionising traditional manufacturing processes, increasing efficiency, and allowing for new levels of automation and personalisation. The global AI in manufacturing market size reached USD 3.8 billion in 2022 and is expected to reach around USD 68.36 billion by 2032, growing at a CAGR of 33.5% from 2023 to 2032. In this article, we will discuss how AI is changing the face of the manufacturing industry in an unprecedented way.
Transformation of manufacturing in the digital age
Gone are the days when production relied solely on labour-intensive physical work and traditional machinery. In the digital age, the manufacturing landscape is undergoing a profound transformation thanks to the integration of modern AI algorithms and the remarkable processing power of supercomputers. These cutting-edge technologies have become the backbone of intelligent factories, where interconnected machinery can seamlessly communicate and make real-time decisions to enhance productivity and streamline operations.
One of the most significant impacts of AI-driven supercomputing on the manufacturing sector is its ability to maximise efficiency throughout the production chain. AI algorithms can detect inefficiencies and bottlenecks in vast volumes of data generated by sensors, machines, and other linked devices. This enables firms to optimise their processes and eliminate waste. This data-driven technique reduces costs while increasing productivity and improving the sustainability and profitability of manufacturing operations.
Now that we have discussed how manufacturing is getting redefined in the AI age let us look at some of the real-life applications of AI in Manufacturing.
Applications areas of AI supercomputing in manufacturing
AI supercomputing transforms manufacturing profoundly, enabling more efficient, flexible, and intelligent production processes. Here are some key application areas:
1. Digital twins: Creating digital twins with AI in manufacturing is an innovative approach combining artificial intelligence with digital twin technology to revolutionise how products are designed, produced, and maintained. A digital twin is a virtual replica of a physical product, process, or system that can be used for various purposes, including simulation, analysis, and control. When enhanced with AI, these digital twins become dynamic tools capable of learning and adapting, offering unprecedented insights and predictive capabilities.
2. AI-enhanced digital twins allow for the rapid prototyping and testing of new designs in a virtual environment. This enables manufacturers to experiment with different materials, designs, and processes without needing physical prototypes, significantly reducing time and cost. By simulating different scenarios and conditions, AI algorithms can analyze the performance of designs and suggest optimisations, leading to more efficient and effective products.
3. Predictive analytics: Using AI in the manufacturing industry, predictive analysis represents a significant leap forward in how companies anticipate maintenance needs, optimise production processes, and improve overall efficiency. AI algorithms trained with the help of massive processing and computing power analyze data from sensors on machinery to detect anomalies that may indicate a potential failure. With the help of inferencing these early signs, maintenance can be performed before a breakdown occurs, minimising downtime.
Furthermore, a robust AI model can predict the remaining useful life of equipment, allowing for better planning of maintenance schedules and budget allocations. This ensures the machine is serviced only when necessary, reducing unnecessary maintenance costs. Some real-life examples of predictive maintenance in manufacturing are vibration monitoring in CNC machines, temperature tracking in motors, oil quality in hydraulics systems, air pressure monitoring in pneumatic systems, wear analysis in conveyor belts, etc.
The use of AI Supercomputing in manufacturing is akin to the plant having a sixth sense anticipating breakdown even before they appear, thereby saving millions in the process. The potential of the predictive maintenance market can be gauged from the fact that it is set to rise from USD 5.93 billion in 2023 to a whooping USD 32.20 billion by 2030 at a CAGR of 27.4% from 2024 to 2030.
4. Quality control: AI-powered vision systems can inspect products on the production line in real-time, identifying defects (see Figure 3) that might be invisible to the human eye. This ensures that only products meeting quality standards reach the customer, enhancing brand reputation. Machine vision is a form of industrial automation utilised for inspection, sorting, and robot guidance. The idea is to use a combination of lighting, cameras, and software to extract information from a captured image.
This information can be as simple as a go/no-go signal or as complex as the identity, orientation, and position of each object in the image. While machine vision doesn’t involve AI per se, the two technologies are becoming more closely intertwined as developers turn to neural networks to augment machine-vision algorithms and improve their accuracy. Audi, for example, has begun using AI for quality control of spot welds at its Neckarsulm plant in Germany. Before deploying machine vision, employees had to manually check the welds’ quality using ultrasound, with samples drawn randomly.
5. Supply Chain Optimisation: This includes the crucial processes of demand forecasting and logistics optimisation. AI supercomputer-trained algorithms can analyze market trends, historical sales data, and other parameters to forecast future product demand more accurately. This helps optimize inventory levels, reducing the risk of stockouts or excess inventory. Furthermore, predictive analysis can optimise routing and delivery schedules based on anticipated orders, traffic conditions, and other variables, improving delivery efficiency and reducing transportation costs.
Conclusion
The potential and benefits of using AI in manufacturing are immense, provided some challenges, such as infrastructure & investment, data management, and skill gaps, are adequately addressed. Suppose we can tackle the challenges mentioned above. In that case, the technology is poised to drive a new era in manufacturing, offering solutions to long-standing challenges and opening new opportunities for innovation and efficiency. As technology continues to evolve, its impact on the manufacturing sector is expected to grow, reshaping the industry.