New Energy Lug Welding: Zero-Code Rapid Changeover

In new energy battery manufacturing, DaoAI 3D ACI equipment (self-developed 3D camera + 3D morphology reconstruction/point cloud, detecting 2D optical blind spot defects like hidden solder joints, coplanarity, micron-level morphology, pores, with 2D-3D fusion) significantly enhances the flexibility and efficiency of multi-variety small-batch production. Its unique zero-code rapid changeover capability reduces the changeover time for lug welding quality inspection from traditional hours to under 5 minutes. With the surge in global demand for clean energy, the new energy battery industry is experiencing unprecedented growth. High energy density, long cycle life, and high safety are core pursuits for battery manufacturers. Among these, lug welding within the cell is a critical process, with its quality directly impacting battery internal resistance, heat generation, and safety. Any tiny burr, cold solder joint, or morphological anomaly can lead to decreased battery performance or even thermal runaway.

Traditional lug welding quality inspection often relies on manual visual inspection or rule-based 2D AOI. However, these solutions increasingly fall short in changeover efficiency and inspection accuracy when faced with the growing demand for multi-variety small-batch production.

Pain Points: Why This Hurdle is Difficult to Overcome

Quality inspection of new energy battery lug welding faces multiple challenges. Firstly, in traditional production models, manual visual inspection suffers from a false negative rate ranging from 2-5% and is susceptible to operator fatigue, making consistent quality difficult to guarantee. Secondly, while rule-based 2D AOI can partially replace manual inspection, it has inherent blind spots in detecting lug burrs, cold solder joints, and micron-level morphological defects, with false positive rates generally exceeding 10%. This is particularly true for multi-layer lug stacking welds, where 2D images struggle to capture accurate 3D information. This leads to substantial human resource investment in downstream re-inspection, with data from a mid-sized battery factory showing re-judgment labor hours accounting for over 30%. More critically, in multi-variety small-batch production, traditional AOI requires engineers to spend hours or even days modifying code, adjusting parameters, and learning samples for each new product launch or model change. This results in excessive line downtime and inefficient changeovers, severely limiting production flexibility. This inefficient changeover mechanism prevents companies from responding promptly to rapidly evolving market demands, increasing inventory risks and operational costs.

The root cause of these difficulties lies in the diversity and complexity of lug welding defects. Lug materials, typically copper or aluminum, can exhibit reflective surfaces and uneven textures after welding, interfering with 2D imaging. Burrs vary widely in form, and micron-level cold solder joints and morphological anomalies are particularly challenging to identify accurately through planar images. Multi-variety small-batch production further amplifies the limitations of traditional solutions in programming and changeover, where every subtle product design or process adjustment implies an entirely new inspection logic, making "zero-code rapid changeover" a critical industry pain point.

Technical Principles

DaoAI 3D ACI equipment fundamentally solves the challenges of lug welding inspection through its self-developed high-precision 3D camera and advanced 3D morphology reconstruction algorithms. The equipment utilizes structured light or multi-view stereo vision principles to acquire high-density point cloud data of the weld surface, precisely reconstructing its 3D morphology. This enables DaoAI 3D ACI to directly measure micron-level geometric features of the weld, such as height, volume, coplanarity, and slope, thereby accurately identifying defects within 2D optical blind spots, including hidden solder joints, tiny burrs, morphological collapses due to cold solder joints, and pores. Compared to traditional rule-based 2D AOI, which relies solely on 2D information like brightness and contrast, DaoAI 3D ACI obtains richer, more intrinsic physical information, leading to more robust defect judgment. Furthermore, the equipment integrates 2D-3D detection capabilities, supplementing 3D morphological data with high-resolution 2D images for texture and color information, achieving more comprehensive defect coverage. When detecting lug burrs, the DaoAI 3D ACI system can build high-precision surface models from point cloud data, using normal analysis and curvature calculations to precisely locate and quantify burr height and volume, whereas traditional 2D AOI often only vaguely identifies edge anomalies or reflective spots, leading to higher false positive rates. In practical applications, DaoAI 3D ACI consistently keeps the false negative rate below <0.4%, significantly outperforming traditional methods.

A core advantage of DaoAI 3D ACI lies in its integrated DaoAI ACI OS operating system, which incorporates the feature recognition capabilities of visual foundation models and APDT positive/few-shot learning technology. This means that engineers only need to provide a small number of good samples (1-20 good samples), and the system can automatically learn product features and generate inspection models within 5 minutes, without writing any code. This "zero-code" programming method greatly simplifies the changeover process, allowing production lines to configure the inspection system extremely quickly when switching between different lug models or adjusting welding processes. Additionally, DaoAI ACI OS's semantic false positive filtering function effectively distinguishes between process fluctuations and true defects, further reducing false positive rates and the burden of manual re-judgment.

Typical Application Scenarios

• **Lug Weld Burr Detection:** DaoAI 3D ACI equipment precisely measures tiny protrusions at the weld edge, identifying and quantifying burr height, width, and volume using 3D morphological data. Traditional 2D vision struggles to distinguish reflections from real burrs and cannot accurately measure their 3D dimensions, while 3D ACI provides centimeter to micron-level morphological information for precise judgment.
• **Cold Solder Joint and Weld Penetration Detection:** For weld morphological anomalies caused by cold solder joints or weld penetration, such as depressions, protrusions, or irregular edges, DaoAI 3D ACI can detect these micron-level morphological changes through high-precision 3D reconstruction. Traditional 2D inspection often relies on color or brightness differences for rough judgment, making it difficult to detect subtle cold solder joints or internal defects.
• **Weld Coplanarity and Flatness:** In multi-layer lug stacking welds, ensuring the coplanarity and overall flatness of the weld is crucial. DaoAI 3D ACI accurately measures planarity errors in the weld area, identifying defects like warping and tilting. This is decisive for ensuring uniform current distribution and preventing localized overheating.
• **Pore and Inclusion Detection:** Pores and inclusions can exist within or on the weld surface, weakening weld strength. DaoAI 3D ACI, through high-resolution 3D imaging, can detect the depth and shape of tiny surface pores, and combined with 2D-3D fusion technology, further enhances the detection capability for surface and near-surface defects.
• **Lug Dimension and Position Deviation:** Beyond defect detection, DaoAI 3D ACI can also precisely measure the overall dimensions of the lug and any post-weld position deviations, ensuring the accuracy and consistency of lug placement and preventing short-circuit risks due to positional offsets.

Case Study

A mid-sized new energy battery manufacturer, specializing in power and energy storage batteries with a wide range of product models, faced frequent production line changeover challenges. Before implementing DaoAI 3D ACI equipment, the factory's lug welding quality inspection primarily relied on traditional 2D AOI and manual re-inspection. Each new product launch or model change required engineers to spend 4-8 hours adjusting 2D AOI parameters and rules, leading to long line downtime and low capacity utilization. Concurrently, traditional 2D AOI had a false positive rate of over 15% for lug burrs and cold solder joints, necessitating 3-4 workers for prolonged re-judgment daily, resulting in high labor costs and low efficiency. In this case, the customer's traditional solution had a false negative rate of approximately 1.2%, impacting the final battery quality.

Following the introduction of the DaoAI 3D ACI solution, this battery factory saw significant improvements in production efficiency and quality control. Through DaoAI ACI OS's zero-code programming and few-shot learning capabilities, production line changeover time was drastically reduced from an average of 6 hours to under 5 minutes, enabling near-rapid switching. The detection accuracy of lug burrs and cold solder joints by the DaoAI 3D ACI system also significantly improved, with a measured false positive rate reduction of 82%, falling below 2.7%. This led to a 75% reduction in manual re-inspection volume, greatly freeing up human resources. Furthermore, the false negative rate of the DaoAI 3D ACI equipment was suppressed to <0.3%, effectively preventing potential quality risks. Post-implementation data showed that the overall OEE (Overall Equipment Effectiveness) of the production line increased by over 15%, bringing substantial economic benefits to the manufacturer.

DaoAI 3D ACI, with its zero-code rapid changeover capability, brings new efficiency improvements and a new paradigm of flexible production to new energy battery lug welding inspection.

DaoAI Solution and Products

DaoAI's core offering for the new energy battery lug welding quality gate is the 3D ACI equipment, which integrates a self-developed high-precision 3D camera and powerful 3D morphology reconstruction capabilities. The key advantage of this equipment lies in its deep integration with the DaoAI ACI OS operating system, enabling true "zero-code" deployment and rapid changeover. In practice, customers only need to place a small number of good lug weld samples (typically 1-20 images) in the inspection area, and DaoAI ACI OS can automatically learn their 3D morphological features and generate a complete inspection model in approximately 5 minutes. When switching product models, it's simply a matter of loading the corresponding pre-set model or performing a quick learning session with a few good samples again, without manual intervention for complex parameter adjustments or code modifications. This approach significantly reduces reliance on specialized vision engineers, enhancing line autonomy and flexibility. DaoAI 3D ACI equipment supports 100% local private deployment, ensuring customer data security and compliance with the stringent data regulations of the battery industry. Through 2D-3D fusion detection, DaoAI 3D ACI can not only detect micron-level morphological defects that are difficult for traditional 2D vision to find, but also incorporate 2D features like surface color and texture for more comprehensive quality control. Additionally, this solution integrates with existing MES/SCADA systems, uploading inspection data in real-time to build a complete product quality traceability chain, providing data support for the battery's entire lifecycle management.

The value of the DaoAI 3D ACI solution is not only reflected in high-precision inspection and rapid changeover but also in the intelligent, automated production model it enables through DaoAI ACI OS. The system continuously learns from production line feedback, constantly optimizing inspection models to achieve an adaptive "gets better with use" capability. In a real-world application at a mid-sized battery manufacturer, DaoAI 3D ACI reduced the false positive rate for lug welding inspection from over 15% to less than 2.7%, while maintaining a false negative rate of <0.3%, an industry-leading level. This high-precision, high-efficiency inspection solution significantly reduced battery rework and scrap rates, improved product consistency and reliability, saving the customer substantial operating costs and enhancing their competitiveness in the new energy battery market.

Full solution for this scenario: 3D ACI Equipment industry solutions · Tab Welding Quality Gate

This article was generated by AI. Customer cases are simulated scenarios based on real product capabilities and figures are illustrative; see product pages for official benchmarks.