Manuscript received October 5, 2025; accepted November 19, 2025; published December 19, 2025
Abstract—Traditional quality control for automotive metal
stamping parts relies heavily on Checking Fixtures (C/Fs).
These fixtures are custom-engineered for individual components,
resulting in high initial design and manufacturing costs, limited
versatility, and time-consuming processes for each new part.
Moreover, C/F inspection is a manual and subjective process,
which introduces human error, measurement variability, and
slower throughput in high-volume production environments.
This study proposes an automated imaging-based quality
inspection framework utilizing a 3D laser scanner and the k-
Nearest Neighbors (k-NN) machine learning algorithm. The
framework systematically analyzes complex point cloud data of
scanned parts through a structured sequence of steps: Data
Acquisition, Segmentation, Pre-processing, Feature Recognition,
Data Analysis, Post-processing, and Final Decision-making. To
ensure both high accuracy and maximum speed, each step
involves direct and immediate comparison with nominal
Computer-Aided Design (CAD) data or a pre-established
training set. The k-NN algorithm plays a central role in the
analysis phase, effectively using Euclidean distances to
distinguish noise from true features, recognize geometric
elements such as holes, and reliably detect defects including
material burrs and dimensional springback. The proposed
system offers significant advantages over traditional C/Fs,
including greater versatility across diverse component
geometries and substantially reduced labor costs through full
automation. Additionally, it ensures faster inspection times and
consistent, objective accuracy, thereby eliminating the
subjectivity, human error, and physical degradation associated
with conventional fixtures. This automated framework
represents a more sustainable, efficient, and robust quality
control solution, aligning with the future needs of the automotive
stamping industry.
Keywords—metal stamping parts, checking fixture (C/F),
quality control, k-NN method
Cite: W. S. Widodo and T. Supriyono, " A Machine Learning-Based Framework for Image Quality Inspection of Automotive Metal Stamping Parts ," International Journal of Machine Learning vol. 15, no. 4, pp. 92-97, 2025.
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).