Artificial intelligence systems for tool condition monitoring in machining: Analysis and critical review

DY Pimenov, A Bustillo, S Wojciechowski… - Journal of Intelligent …, 2023 - Springer
The wear of cutting tools, cutting force determination, surface roughness variations and other
machining responses are of keen interest to latest researchers. The variations of these …

Advancements and challenges in machine learning: A comprehensive review of models, libraries, applications, and algorithms

S Tufail, H Riggs, M Tariq, AI Sarwat - Electronics, 2023 - mdpi.com
In the current world of the Internet of Things, cyberspace, mobile devices, businesses, social
media platforms, healthcare systems, etc., there is a lot of data online today. Machine …

Influence of duplex jets MQL and nano-MQL cooling system on machining performance of Nimonic 80A

ME Korkmaz, MK Gupta, M Boy, N Yaşar… - Journal of Manufacturing …, 2021 - Elsevier
Nickel based super alloys are considered as difficult to machine materials. These days, the
sustainable cooling system are applied at the cutting zone for enhancing the machining …

Tool wear prediction in face milling of stainless steel using singular generative adversarial network and LSTM deep learning models

M Shah, V Vakharia, R Chaudhari, J Vora… - … International Journal of …, 2022 - Springer
During milling operations, wear of cutting tool is inevitable; therefore, tool condition
monitoring is essential. One of the difficulties in detecting the state of milling tools is that they …

[HTML][HTML] Advance monitoring of hole machining operations via intelligent measurement systems: A critical review and future trends

R Binali, M Kuntoğlu, DY Pimenov, ÜA Usca, MK Gupta… - Measurement, 2022 - Elsevier
This review paper summarizes the application of smart manufacturing systems utilized in
drilling and hole machining processes. In this perspective, prominent sensors such as …

[HTML][HTML] Carbon emissions and overall sustainability assessment in eco-friendly machining of Monel-400 alloy

NS Ross, R Rai, MBJ Ananth, D Srinivasan… - Sustainable Materials …, 2023 - Elsevier
With increasing regulations about global warming, environmental pollution, and climate
change, reducing carbon emissions from energy-intensive industrial activities routes to …

Prediction of surface roughness using machine learning approach in MQL turning of AISI 304 steel by varying nanoparticle size in the cutting fluid

V Dubey, AK Sharma, DY Pimenov - Lubricants, 2022 - mdpi.com
Surface roughness is considered as an important measuring parameter in the machining
industry that aids in ensuring the quality of the finished product. In turning operations, the …

Resource savings by sustainability assessment and energy modelling methods in mechanical machining process: A critical review

M Sarıkaya, MK Gupta, I Tomaz, GM Krolczyk… - Journal of Cleaner …, 2022 - Elsevier
In machining processes, there are contradictions between high efficiency and
environmentally-friendly machining. This indicated that there is a major potential for …

Smart sheet metal forming: importance of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine for predicting …

C Kubik, SM Knauer, P Groche - Journal of Intelligent Manufacturing, 2022 - Springer
In consequence of high cost pressure and the progressive globalization of markets,
blanking, which represents the most economical process in the value chain of manufacturing …

Predicting the compressive strength of additively manufactured PLA‐based orthopedic bone screws: A machine learning framework

R Agarwal, J Singh, V Gupta - Polymer Composites, 2022 - Wiley Online Library
Additive manufacturing (AM) technology is an innovative technique that has shown potential
in several surgical innovations such as the fabrication of cost‐effective orthopedic screws. It …