AI Industry

AI x Global 500 Manufacturing Companies | Robots, Data & Maintenance

Artificial intelligence is changing the operation modes of industrial production, and has become one of the key tools in the new era of global manufacturing.

Artificial intelligence is changing the operation modes of industrial production, and has become one of the key tools in the new era of global manufacturing. As the concept of “smart manufacturing” becomes more prevalent, AI-enabled technologies such as robotics, computer vision and Internet of Things (IoT) are being widely applied in various industrial manufacturing tasks, playing crucial roles in achieving intelligent production, and increasing the strategic value of Industry 4.0. Fortune Global 500 manufacturing companies are spearheading the deployment AI solutions in their factories to enhance production line precision and flexibility and improve data management efficiency.

Global Manufacturing Industry Market Size

Based on a new report from Interact Analysis, manufacturing industry output (MIO) value for the US, Asia and Europe is expected to reach US$10 trillion in 2022. Because the growth rate of productivity is higher than that of demand in the manufacturing industry, manufacturers have started seeking strategies to reduce production costs to offset any decline in profitability. This means that rather than purchasing new machines for the production line, most manufacturers prefer upgrading technologies for existing equipment to enhance productivity.

Comparison between industry production and machinery production growth from 2013-2018. (Source: Interact Analysis)

Primary Implementations of AI in the Manufacturing Industry

Industrial Vision Systems: Based onAI technologies like machine vision, image processing, pattern recognition and machine learning, industrial vision systems use sensors and/or robots to assist with perception tasks in manufacturing, e.g., accurate vision-based positioning, complex multi-body object detection and tracking, and quality control.

Industrial Robots: Leveraging machine learning, path planning and other advanced techniques, industrial robots can tackle highly precise and complex tasks on production lines. Unlike traditional single-use industrial equipment, AI-powered robots can be applied in a variety of working environments for different objects to improve manufacturing flexibility.

Industrial Internet of Things (Industrial IoT): Industrial IoT systems can monitor, visualize and collect real-time equipment data from smart devices such as sensors, actuators and dashboards. In addition, IoT systems supported by advanced machine learning algorithms can automatically conduct equipment anomaly detection and condition monitoring, and proactively schedule facility maintenance, thereby reducing machine failures and downtime.

Industrial Cloud Computing: Coupled with IoT system, big data and AI models, industrial cloud computing platforms rely on its data storage and computing power to support tasks like production line monitoring and predictive maintenance during a manufacturing process.

Applications of AI technologies in the Manufacturing Industry

AI Use Cases in Fortune Global 500 Manufacturing Companies

Mitsubishi Electric: Mitsubishi deploys multiple sensors and AI technologies on its industrial robots, allowing them to grasp targeted objects within 3.5 milliseconds and rapidly recognize and adapt to changing conditions while maintaining precise operations.

Weiqiao Textile: Coupled with sensors installed on production assets, Weiqiao acquires and integrates all its textile workshop data, including machine operating data, product quality information, personnel information, equipment power, room temperature and humidity, etc., into a big data cloud platform for further analytics. This enables manufacturing system and data management on a fully automated textile production line.

Siemens Gamesa: Leveraging computer vision and machine learning technologies, Siemens Gamesa collaborated with Fujitsu to deliver an AI solution that automatically detects flaws on its turbine blade products for use in wind farms, achieving 100 percent coverage of all defects. Most of the company’s manual inspection work has now been replaced with the AI solution, reducing scanning inspection time by 80 percent.

Caterpillar: Based on operation and performance data collected from shipboard sensors, Caterpillar’s intelligent asset management system uses machine learning algorithms to optimize hull cleaning frequency by analyzing cleaning cost and performance improvements under different levels of cleanliness.

Limitations of AI Implementation in the Manufacturing Processes

Technology Gap: Many countries still face problems closing the technology gap between chip design and industrial software development for implementation of AI solutions in manufacturing. They have to either import backbone digital products from foreign suppliers or build up their own tech capabilities, both of which can be costly and time consuming.

Data Scarcity: Machine learning models require a large high-quality dataset to train well-performing algorithms, yet some manufacturing processes still lack sufficient data for AI adoption.

Manufacturing Standard Variance: Manufacturing standards are different throughout the world, and this standard variance has become a major barrier to widespread AI deployment in the manufacturing industry.

Market Downturn: Compared with other industries, manufacturing has a relative low return on investment (ROI) and is less profitable in the short term. Consequently, many investors feel less confident about investing in new manufacturing technologies especially during a market downturn.

Future Trends of AI in the Manufacturing Industry

Rise of Digital Twins: Digital twin technology enables the monitoring and management of physical devices in a virtual space using sensors, historical data, models, etc.

Human-Machine Interface Innovations: By leveraging cloud platform and IoT technologies, humans and machinescan interact not only via computer screens, but also via mobile or augmented reality (AR) devices.

Adoption of Predictive Maintenance: Based on machine learning algorithms and IoT technologies, a predictive maintenance system eliminates the traditional “run to failure approach” to maintenance. Instead, it can use proactively predictive methods to help avoid costly repairs and asset failures.

Tapping the Power of Edge Computing: While cloud platforms bring computing support to IoT components, edge computing can further enhance performance on real-time monitoring, data processing and analytics near the source and at the edge of the network.

Source: Synced China

Localization: Tingting Cao | Editor: Michael Sarazen

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