Cohort: 1 

Location: Ningde, China 

Application Title: Data-efficient industrial AI systems for manufacturing decision intelligence 

Team Members: 

AIMS: Yangbing Lou (executive sponsor), Siqi Zhu, Zhiyong Zhang, Guoyang Yan, Tianhua Lu

CATL: Jun Ni (executive sponsor), Zhiyang Wu, Lin Ma, Chao Guo, Wenjing Jin

The challenge 

Industrial manufacturing environments generate large volumes of data, but the most critical events are often rare. Equipment failures, quality deviations and process anomalies occur infrequently by design. This makes it difficult to learn from historical examples. 

Before the use of AI, operations at scale relied on manual inspections, scheduled maintenance and engineer-defined control rules. These approaches depend heavily on experience and static thresholds. They work under stable conditions but struggle when processes fluctuate or when early signals of failure are subtle. In battery manufacturing, where quality consistency is essential, small variations can lead to downstream losses that are hard to detect in time. 

Conventional machine-learning systems are not well suited to these conditions. They typically require large datasets and frequent retraining, which are rarely available in high-stakes industrial settings.

The solution 

A hybrid AI platform designed for limited data 

CATL partnered with AIMS to deploy an industrial AI platform designed to operate where data is scarce and reliability is critical. 

The platform combines physics-informed models with expert process knowledge and machine learning. Physics-based constraints ensure predictions remain consistent with known process behaviour. Expert rules encode operational understanding developed over years of manufacturing practice. Machine-learning components focus on identifying deviations and trends in real-time data. 

This hybrid design allows the system to generate useful insights without relying on large volumes of labelled failure data. 

How it works 

Learning from few examples in real time 

The system uses few-shot learning to detect rare events. This allows it to recognise early warning signals even when historical examples are limited. Digital twins simulate process behaviour under different conditions, helping predict how changes upstream may affect quality downstream. 

Decisions are made close to the production line using edge AI. This reduces latency and allows the system to respond quickly to changing conditions. Operators receive alerts and recommendations through existing interfaces rather than new standalone tools. 

The platform integrates with CATL’s existing manufacturing systems, avoiding disruption to established workflows.

How it differs from conventional approaches 

Moving beyond data-heavy models 

Traditional AI approaches depend on large datasets and offline analysis. In contrast, the CATL–AIMS solution is designed to operate with incomplete and noisy data. 

By embedding physical constraints and expert knowledge directly into the model, the system remains interpretable and stable. It adapts continuously without requiring constant retraining. This makes it suitable for production environments where downtime and false alarms carry real cost. 

Impact 

• 50% reduction in quality variation 
• 50% decrease in operator workload 
• Faster production cycles achieved through earlier detection and process adjustment 

Why it matters 

Supporting reliable decision-making in complex manufacturing 

Manufacturers increasingly need systems that can support decisions when data is limited and consequences are significant. The collaboration between CATL and AIMS shows how hybrid AI can complement human expertise rather than replace it. 

By focusing on data efficiency and real-time operation, the platform supports more consistent production outcomes without requiring major changes to existing infrastructure.