Haier / Qingdao

The Haier Qingdao Water Purifier Interconnected Factory and Water Ecosystem Interconnected Factory serve as the core smart manufacturing base for Haier Group’s smart water ecosystem. As industry pioneers, the factories synergistically integrate Industrie 4.0 technologies to build a full-process intelligent ecosystem covering “Prediction-Production-Inspection-Maintenance.” Powered by AI, the facilities achieved comprehensive empowerment in process self-optimization, precision quality prediction, and flexible assembly.

Facing the challenges of high precision process requirement, rising costs, and strict environmental requirements, the factories implemented a comprehensive intelligent manufacturing system centered on “AI and Data Dual Drive.” Through the deep integration of AI, IIoT, and Big Data technologies, the factories achieved full digitalization and intelligence from decision-making to green manufacturing. The Industrie 4.0 solution includes:

• Developing AI-driven process optimization models to enhance component manufacturing consistency.
• Leveraging big data for predictive quality diagnosis to build an active prevention loop.
• Deploying multimodal perception flexible systems to achieve high-precision automated assembly.
• Implementing green process management to drive sustainable operations.

To address raw material fluctuations in carbon rod sintering, an adaptive temperature control system driven by reinforcement learning was developed. Based on over 1.04 million historical process data records, the model dynamically matches sintering temperature curves. This improved the pass rate by 71% and reduced energy consumption by 58%.

To handle leakage risks in complex water circuits, an intelligent diagnostic system based on LSTM and Bayesian networks was established. Utilizing 9,800 historical leakage cases, the system enables second-level positioning of micron-level leak points and root cause analysis. This improved the product anti-leakage performance compliance rate by 68%.

To address process fluctuations caused by manual monitoring of tank liquid concentration, an online spectral analysis detection module and AI prediction system were introduced. By real-time sensing of absorbance changes, the system improved concentration detection precision by 67%, increased the enamel process first-pass yield by 70%, and saved over 170 operational hours annually.

To approach inner tank high-temperature deformation and multi-spec assembly challenges, a system combining high-precision 3D vision point cloud algorithms and seven-axis force control robots was deployed. It enables real-time 3D model reconstruction and dynamic path compensation, improving the inner tank pre-assembly pass rate by 69% and achieving zero fastening defects.

To deal with high energy consumption in reliability testing, an intelligent test control platform integrating energy-feedback electronic load technology was developed. The system feeds over 92% of test energy back to the grid during operation, creating a “Test-Feedback-Reuse” energy cycle that reduced annual test energy consumption by 92%.