A novel machine learning approach for surface roughness quantification and optimization of cast-on-strap...

Area of Science:

• Materials Science
• Electrochemistry
• Surface Engineering

Background:

• Surface roughness negatively impacts material lifespan by accelerating corrosion and charge loss.
• Controlled surface roughness is crucial for applications like automotive lead-acid batteries.
• The cast-on-strap machine significantly influences lead-antimony alloy surface roughness.

Purpose of the Study:

• To quantify surface roughness using two-point correlation functions.
• To optimize lead-antimony poles and straps in lead-acid batteries.
• To reduce electrochemical corrosion in highly corrosive media.

Main Methods:

• Utilizing two-point correlation functions to characterize surface microstructure.
• Applying statistical morphological descriptors for heterogeneous correlation functions.
• Employing simulated annealing procedures for surface micro-irregularity optimization.

Main Results:

• Successful representation and quantification of surface roughness.
• Demonstrated optimization of lead-antimony battery components.
• Validation of the two-point correlation function method for surface analysis.

Conclusions:

• The two-point correlation function is effective for quantifying and optimizing surface roughness.
• This method can reduce electrochemical corrosion in lead-acid batteries.
• The approach has potential applications in diverse fields like subsea pipelines and laser cutting.