IIUM Repository

Review of intelligence for additive and subtractive manufacturing: current status and future prospects

Rahman, M Azizur and Saleh, Tanveer and Jahan, Muhammad Pervej and McGarry, Conor and Chaudhari, Akshay and Huang, Rui and Tauhiduzzaman, M and Ahmed, Afzal and Mahmud, Abdullah Al and Bhuiyan, Md. Shahnewaz and Khan, Md Faysal and Alam, Md. Shafiul and Shakur, Md Shihab (2023) Review of intelligence for additive and subtractive manufacturing: current status and future prospects. Micromachines, 14 (3). pp. 1-53. E-ISSN 2072-666X

[img] PDF (Article) - Published Version
Restricted to Repository staff only

Download (14MB) | Request a copy


Additive manufacturing (AM), an enabler of Industry 4.0, recently opened limitless possibilities in various sectors covering personal, industrial, medical, aviation and even extra-terrestrial applications. Although significant research thrust is prevalent on this topic, a detailed review covering the impact, status, and prospects of artificial intelligence (AI) in the manufacturing sector has been ignored in the literature. Therefore, this review provides comprehensive information on smart mechanisms and systems emphasizing additive, subtractive and/or hybrid manufacturing processes in a collaborative, predictive, decisive, and intelligent environment. Relevant electronic databases were searched, and 248 articles were selected for qualitative synthesis. Our review suggests that significant improvements are required in connectivity, data sensing, and collection to enhance both subtractive and additive technologies, though the pervasive use of AI by machines and software helps to automate processes. An intelligent system is highly recommended in both conventional and non-conventional subtractive manufacturing (SM) methods to monitor and inspect the workpiece conditions for defect detection and to control the machining strategies in response to instantaneous output. Similarly, AM product quality can be improved through the online monitoring of melt pool and defect formation using suitable sensing devices followed by process control using machine learning (ML) algorithms. Challenges in implementing intelligent additive and subtractive manufacturing systems are also discussed in the article. The challenges comprise difficulty in self-optimizing CNC systems considering real-time material property and tool condition, defect detections by in-situ AM process monitoring, issues of overfitting and underfitting data in ML models and expensive and complicated set-ups in hybrid manufacturing processes.

Item Type: Article (Journal)
Uncontrolled Keywords: intelligent manufacturing; digital twin; feedback control; smart system; data analytics
Subjects: T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering
Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User: Dr. Tanveer Saleh CEng MIMechE
Date Deposited: 24 Feb 2023 09:32
Last Modified: 24 Feb 2023 09:35
URI: http://irep.iium.edu.my/id/eprint/103791

Actions (login required)

View Item View Item


Downloads per month over past year