Solution 02

Process Quality Control

Solution 02

Process Quality Control

Steel Production Tobacco Manufacturing

Redefining the Quality Control Defense Line

What
Combining deep learning visual recognition with multivariate correlation analysis to achieve precise, proactive capture and quantitative analysis of key process parameters and product surface quality for hidden, costly quality defects.
Why
Quality defects are the most hidden and expensive cost source in industrial production. Maxta employs industrial vertical models infused with top industry expert know-how, upgrading quality control from post-inspection to proactive early warning.

The system combines "deep learning visual recognition" with "multivariate correlation analysis" as twin engines. On the result side, vertical vision models precisely capture micro defects in massive HD images (achieving 99.6% crack recognition rate); on the root cause side, predictive models (such as Random Forest) deeply mine hundreds of complex process parameters, automatically ranking and tracing key quality factors.

Steel Billet Inclusion Analysis & Prediction

Industry Pain Point

Billet quality defects directly affect final product quality. Some defects hide below the surface and aren't found until rolling, causing massive quality losses.

System Benefit

Collect data from steelmaking, casting, and rolling processes. Using big data and ML, develop intelligent defect prediction methods to eliminate problems before rolling.

Crack & Process Variable Correlation Analysis

Industry Pain Point

Complex crack causes and numerous process parameters make it impossible to systematically identify which variables correlate most strongly with crack quality issues.

System Benefit

Using Random Forest for crack data correlation analysis, ranking variable importance, progressively selecting optimal variable subsets to build the highest-accuracy crack classification model.

Secondary Cooling Nozzle Blockage Prediction

Industry Pain Point

Billet crack images at 1920×1200 resolution require manual frame-by-frame identification in massive HD images, extremely low efficiency with high miss rates.

System Benefit

Deep learning vision model preprocesses billet images, extracts recognition regions, trains and validates on sample sets, achieving 99.6% recognition rate in real production.

Cooling Moisture Intelligent Control

Industry Pain Point

Cooling moisture control entirely relies on manual experience. Drum wall temperature, steam valve opening, and cooling moisture are difficult to co-optimize.

System Benefit

Real-time prediction of drum wall temperature, steam valve opening, and cooling moisture; moisture control homogenization; reduced dry head/tail waste; full-process intelligent control replacing manual operation.

Cigarette Draw Resistance Intelligent Control

Industry Pain Point

Draw resistance affected by intra-batch weight fluctuations. Inter-group resistance deviation hard to eliminate. Traditional manual target weight adjustment lacks real-time precision.

System Benefit

Predict future draw resistance trends through in-machine short cigarette weight. Model automatically controls cigarette machine target weight, dynamically reducing inter-group fluctuation for full-process intelligent draw resistance control.

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Whether it's intelligent manufacturing quality control, predictive maintenance, smart scheduling, or industrial energy optimization, Maxta delivers complete industry AI solutions validated in production environments, built on MaxtaOS + MaxModel.

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Process Quality Control