Artificial intelligence can be simply understood as "artificial machines that are as intelligent as humans." Applying this intelligent "artificial machine" to manufacturing primarily aims to enable machines to "reach or even surpass the skill level of human technicians," thereby improving the production and operational efficiency of manufacturing enterprises.
The "intelligentization" process of "artificial intelligence + manufacturing" is fundamentally different from the past pursuit of "automation" in manufacturing. "Automation" aims for automated machine production, essentially replacing humans with machines and emphasizing large-scale machine production; while "intelligentization" pursues flexible machine production, essentially "human-machine collaboration," emphasizing that machines can autonomously cooperate with human work and adapt to environmental changes.
"Artificial intelligence + manufacturing" does not aim to simply replace humans with machines, but rather to bring the extremely specialized assembly line work of workers since the Industrial Revolution back to a "human-centered" organizational model, allowing machines and humans to do what they are better at, with machines taking on more repetitive, tedious, and dangerous work, and humans taking on more creative work.
Manufacturing is a highly complex industry. A single product may require dozens of raw materials or consist of millions of parts. Different companies producing the same product employ different production processes, equipment, and component inputs. These differences in processes, equipment interfaces, and data formats not only create significant challenges for digital connectivity across the supply chain but also require each company to embark on its own digital transformation, resulting in significant time and effort. Establishing a standardized, universally applicable, and plug-and-play industrial internet platform can address these issues in the "artificial intelligence + manufacturing" process. This platform provides the manufacturing industry with general-purpose computing power (industrial cloud computing and edge computing), data (industrial big data), and algorithmic capabilities (industrial artificial intelligence), thereby driving the transformation and upgrading of the entire industry.
Currently, the typical directions of "AI + Manufacturing" mainly fall into three categories: First, intelligent production, which achieves digital connection and high collaboration among production equipment, value chains, and supply chains, enabling production systems to possess capabilities such as agile perception, real-time analysis, autonomous decision-making, precise execution, and learning improvement, thereby comprehensively enhancing production efficiency. Second, intelligent products, which endow products with the ability to intelligently respond to external changes and user needs through cloud connectivity or by encapsulating pre-trained AI systems into hardware. Third, intelligent services, which monitor product status in real time and respond to user needs, providing value-added services such as lease-to-own, pay-as-you-go, remote diagnostics, fault prediction, remote maintenance, and integrated solutions, enabling manufacturing enterprises to shift from providing products to providing "products + services."
In conclusion, achieving high-level human-machine collaboration through "artificial intelligence + manufacturing" can drive quality, efficiency, and power transformation in the manufacturing industry, creating a better life for humanity.
