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International Journal of Computer Aided Manufacturing Cover

International Journal of Computer Aided Manufacturing

E-ISSN: 2456-642X | Peer-Reviewed Journal (Refereed Journal) | Hybrid Open Access

About the Journal

International Journal of Computer-Aided Manufacturing [2456-642X(e)] is a peer-reviewed hybrid open-access journal launched in 2015 that reports new research as well, as a new application of the technology that could be of use in the ongoing research and in framing an educational ground for the students. The journal aims to cover computer-aided manufacturing software and the automation of machining processes.

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Journal Information

Title: International Journal of Computer Aided Manufacturing
Abbreviation: ijcam
Issues Per Year: 2 Issues
E-ISSN: 2456-642X
Publisher: JournalsPub
DOI: 10.37591/IJCAM
Starting Year: 2015
Subject: Engineering
Publication Format: Hybrid Open Access
Copyright Policy: CC BY-NC-ND
Type: Peer-reviewed Journal (Refereed Journal)

Address:

JournalsPub, An imprint of Dhruv Infosystems Pvt. Ltd. A-118, 2nd Floor, Sector-63, Noida, U.P. India, Pin - 201301

Editorial Board

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ijcam maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.

Editor in Chief

Editor

Dr. Arindam Kumar Chanda, Professor

Delhi Skill and Entrepreneurship University, Delhi, India, 110091

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Latest Articles

Ahead of Print

Application of Artificial Neuron Network in Roughness Prediction: A Case Study in Turning of Stainless Steel

Artificial neural networks (ANNs) offer a practical and efficient way to choose the best machining parameters for the turning process to reduce surface roughness, the resulting cutting forces, and maximize tool life.

Neuron network, artificial intelligence, linear regression, prediction of surface roughness

Capturing Student’s Attendance Using Face Recognition

In instructive foundations, organizations, and different associations, one of the fundamental measures is to keep a record of individuals present every day.

Face recognition, machine learning, deep learning, face detection, student’s attendance