Metrology Process to Produce High-Value Components and Reduce Waste for the Fourth Industrial Revolution

Ahmad Junaid, Muftooh Ur Rehman Siddiqi*, Sundas Tariq, Riaz Muhammad, Ubaidullah Paracha, Nasim Ullah, Ahmad Aziz Al Ahmadi, Muhammad Suleman, Tufail Habib

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Conventionally, a manufactured product undergoes a quality control process. The quality control department mostly ensures that the dimensions of the manufactured products are within the desired range, i.e., the product either satisfies the defined conformity range or is rejected. Failing to satisfy the conformity range increases the manufacturing cost and harms the production rate and the environment. Conventional quality control departments take samples from the given batch after the manufacturing process. This, in turn, has two consequences, i.e., low-quality components being delivered to the customer and input energy being wasted in the rejected components. The aim of this paper is to create a high-precision measuring (metrology)-based system that measures the dimension of an object in real time during the machining process. This is accomplished by integrating a vision-based system with image processing techniques in the manufacturing process. Experiments were planned using an experimental design which included different lightning conditions, camera locations, and revolutions per minute (rpm) values. Using the proposed technique, submillimeter dimensional accuracy was achieved at all the measured points of the component in real time. Manual validation and statistical analysis were performed to check the validity of the system.

Original languageEnglish
Article number7472
JournalSustainability (Switzerland)
Issue number12
Publication statusPublished - 19 Jun 2022

Bibliographical note

© 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://

Funding Information:
This research was funded by Taif University, Taif, Saudi Arabia, under Taif University Researchers Supporting Project (TURSP-2020/121).


  • Industry 4.0
  • machine vision
  • metrology
  • quality control


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