The mechanical engineering industry in Vietnam is facing an critical challenge in adapting to the next generation of automated systems that are intelligent and autonomous in order to successfully compete in the international arena. The answer to this critical challenge for the mechanical industry in Vietnam will lie in the fusion of Artificial Intelligence with other enabling technologies such as Big Data, IoT, amongst others, but this will not be easily accomplished due to certain impediments.
1. From Automation to Intelligent Autonomy
The mechanical industry in Vietnam has depended on automation and numerical control technology for several decades. Currently, the role of AI is revolutionizing this base into intelligent autonomous production, wherein decisions are made independently by equipment, robots, and sensors based on the production environment. Mr. Nguyen Lac Hong, the Vice Chairman of the Vietnam Association of Mechanical Industry, added that “technology such as Big Data, the Internet of Things (IoT), industrial platforms, and the Digital Twin have brought about a powerful change in the field of mechanical design and production.”
2. AI-Assisted Design Optimization
Machine learning algorithms can create various design alternatives and assess them regarding durability, production cost, and weight before finally settling on the best solutions. This aspect plays a pivotal role in industries that require precision, such as aerospace, automotive, and robotics industries. In CNC machining, AI systems are now integrated to optimise cutting processes in real time, varying parameters such as feed rate and cutting speed.
3. Quality Control and Predictive Maintenance
Camera systems equipped with AI technology are able to identify any defects, such as cracks or warping, at speeds unattainable for human evaluators. Predictive maintenance, based on IoT sensors, analyzes data in order to forewarn producers when a component might fail, stopping production before it occurs. As AI predictive maintenance becomes more prominent, it is now at the forefront of smart factories in Vietnam.
4. Barriers to AI Adoption
Despite the huge potential that AI holds in this area, it has still not been widely adopted. More than 90% of mechanical industries do not have the means to implement AI on a larger scale. This is because they have high operation costs in terms of infrastructure, require specialized software, as well as high-performance computing. A major drawback that data fragmentation poses in machining, inspection, and design data is that it is not in a compatible format.
5. Talent Shortages and Technology Dependence
In Vietnam, there is a lack of engineers with inter-disciplinary knowledge in mechanical engineering, AI, and numerical simulation. There is also a demand for engineers competent in operating and maintaining smart machinery using sensor-integrated machine learning. Adding fuel to the fire is the considerable importation cost of smart machinery that uses AI through “black box” systems. The local expertise for the use of sensor modules, data acquisition systems, and AI-integrated simulation software is also poor.
6. National Strategy and Data Infrastructure
“Vietnam’s National Strategy on Research, Development, and Application of AI to 2030” focuses on the application of AI in the field of mechanical engineering. It has been suggested to have a digital data repository related to design, manufacture, simulations, and sensors in the field of mechanics in the country. This data repository would be the starting point to train AI models.
7. Localization & Ecosystem Development
The promotion of “Make in Vietnam” solutions such as machine control software, machine vision, and digital twins can cut costs and enhance autonomy of technology. By developing the smart mechanical ecosystem through collaboration between research institutes, universities, and companies, technology development and experimentation can progress. It is understood that training in smart factory at Samsung Vietnam has increased maturity from 0.9 to 2.1. Such activities clearly bring value to the change process.
8. International Best Practices in AI Production
International cases provide important learnings. Siemens’ AI-powered Amberg Electronics Plant processes more than 50 million data points a day to attain product quality above 99.99 percent. GE’s AI-driven digital twins of jet engines and turbines have already resulted in the saving of millions every year with predictive maintenance. These examples not only point to the effectiveness of AI but also to the manner in which the industry in Vietnam can learn from the seamless loop that AI provides.
9. Strategic Roadmap for Vietnam’s Industry Leaders
To address the current gaps, the mechanical industry in Vietnam should begin with highly successful AI pilots in the predictive maintenance or computer vision quality inspection areas. Collaboration with technology firms can mitigate talent gaps, with the leverage of modular AI solutions that can be readily integrated with traditional systems. Subsequent scaling of successful pilots would establish a strong AI capacity foundation, making the Vietnam mechanical industry globally competitive. “AI has shifted from being an additive, or premium, feature to a strategic imperative. By choosing to focus on the development of their data infrastructure, talent, and tech, the mechanical engineering industry in Vietnam can look to transition their experience-driven designs to a data-driven, AI-driven future,” concluded Gong.
Density of Talent