# CLM App: Interlamellar Distance of Pearlite via CLM Revisited and Automated

## TL;DR

This paper introduces an automated tool using Python to measure pearlite microstructure in steel images, aiming to reduce manual work in quality control.

## Contribution

A Python-based automation of the circular line method for measuring pearlite interlamellar distance is proposed.

## Key findings

- The CLM automation performs well on certain types of pearlite microstructures.
- Manual data from 465 images was used to assess the performance of the automated method.
- The paper identifies microstructure types where automation is less effective.

## Abstract

Pearlitic (stainless) steel is used in automotive, aerospace, and other industries where high strength, hardness, and wear resistance are required. Its quality control can be performed using mechanical tests or by examining the lamellar microstructure, namely, determining interlamellar distance. One of the related approaches is the circular line method (CLM). This paper reviews the challenges to automate employment of the CLM using custom Python code in order to reduce human time costs during image-based quality assessment of pearlite. The goal is to perform intersection counting automatically once the human operator has configured the application and selected the locations of measuring circles. Performance assessment using manually processed data from some 465 images is performed. We divide the imaged pearlite microstructures into different “types” when the code performs well or, respectively, not so well. We conclude with possible extensions of the work presented here.