General Industrial Environment and Health Design Software Using a Small Data-Driven Neural Network Model.

Area of Science:

• Industrial Design
• Artificial Intelligence
• Manufacturing Engineering

Background:

• Traditional industrial design processes are often hampered by knowledge loss and inefficient workflows.
• Manufacturing enterprises face challenges in retaining design expertise and optimizing the design cycle.
• The integration of advanced computing technologies into design is crucial for modern manufacturing.

Purpose of the Study:

• To develop an enterprise intellectual brain neural network platform for industrial environment and health design.
• To convert tacit designer knowledge into explicit data, facilitating knowledge retention and accumulation.
• To create a general parameter intelligent design software by integrating AI, big data, and IoT.

Main Methods:

• Utilizing small data-driven approaches within a neural network framework.
• Combining artificial intelligence, big data, Internet of Things, and parameterization technologies.
• Developing a large-scale, product-level, general parameter intelligent design software.

Main Results:

• The platform effectively converts designer wisdom into data, preserving enterprise design knowledge.
• Significant improvements in designer work efficiency and reduction in low-end repetitive labor.
• Establishment of a design think tank through accumulated enterprise design knowledge, driving an intelligent expert system.

Conclusions:

• The developed platform and software enhance industrial design efficiency and promote creative work.
• Digitization of the entire product lifecycle is achievable, improving overall workflow.
• The intelligent expert system acts as an enterprise brain, integrating various critical design and production resources.