Estimation of Compound Layer Thickness and Porosity in Nitrocarburized Hardening

Nitrocarburization is a process for surface hardening of metallic components. For quality control purposes, regular inspection of the achieved hard surface layer, known as the compound layer thickness, is necessary. This is done by cutting the samples and inspecting a cross-section with microscopy. Since this is a destructive and time-consuming technique, it limits the frequency of the testing, and hence there is a need to complement this method with a rapid, non-destructive alternative. This paper demonstrates how ultrasound in combination with a supervised learning approach, can be used to estimate compound layer thickness down to 10-20 microns.

Authors

Institutions

Publication Details

Published
2024-09-22
DOI
https://doi.org/10.1109/uffc-js60046.2024.10793719
Primary Topic
Metal and Thin Film Mechanics
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Estimation of Compound Layer Thickness and Porosity in Nitrocarburized Hardening

Johan E. Carlson, Narendra Kumar, Payal Gupta
Metal and Thin Film Mechanics
article

Estimation of Compound Layer Thickness and Porosity in Nitrocarburized Hardening

Johan E. Carlson, Narendra Kumar, Payal Gupta
article en

Abstract

Nitrocarburization is a process for surface hardening of metallic components. For quality control purposes, regular inspection of the achieved hard surface layer, known as the compound layer thickness, is necessary. This is done by cutting the samples and inspecting a cross-section with microscopy. Since this is a destructive and time-consuming technique, it limits the frequency of the testing, and hence there is a need to complement this method with a rapid, non-destructive alternative. This paper demonstrates how ultrasound in combination with a supervised learning approach, can be used to estimate compound layer thickness down to 10-20 microns.

Luleå University of Technology (SE)
VINNOVA
Openalex Percentile: Top 74%
Metal and Thin Film Mechanics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.