Results 11 to 20 of about 69,084 (264)

A Novel Multivariate Cutting Force-Based Tool Wear Monitoring Method Using One-Dimensional Convolutional Neural Network

open access: yesSensors, 2022
Tool wear condition monitoring during the machining process is one of the most important considerations in precision manufacturing. Cutting force is one of the signals that has been widely used for tool wear condition monitoring, which contains the ...
Xu Yang   +4 more
doaj   +1 more source

Milling Tool Wear Prediction Method Based on Deep Learning Under Variable Working Conditions

open access: yesIEEE Access, 2020
Tool wear prediction is essential to ensure part quality and machining efficiency. Tool wear is affected by factors such as the material, structure, process, and processing time of the parts.
Mingwei Wang   +4 more
doaj   +1 more source

Tool Wear Prediction in the Forming of Automotive DP980 Steel Sheet Using Statistical Sensitivity Analysis and Accelerated U-Bending Based Wear Test

open access: yesMetals, 2021
The forming process of ultra-high-strength steel (UHSS) may cause premature damage to the tool surface due to the high forming pressure. The damage to and wear of the tool surface increase maintenance costs and deteriorate the surface quality of the ...
Junho Bang   +5 more
doaj   +1 more source

The Study of Tool Wear in Dry Milling 300M Ultra high strength Steel

open access: yesJournal of Harbin University of Science and Technology, 2019
Aiming at the problem of the rapid failure of 300M ultrahighstrength steel due to tool wear in the process of machining, the test of 300M steel in dry milling with cemented carbide coated tools was carried out.
ZHANG Hui-ping   +3 more
doaj   +1 more source

Comparative analyses on tool wear in helical milling of Ti-6Al-4V using diamond-coated tool and TiAlN-coated tool

open access: yesJournal of Advanced Mechanical Design, Systems, and Manufacturing, 2014
This paper aims to establish the wear mechanisms of tungsten carbide (WC) tools coated with TiAlN and coated with diamond respectively when helical milling Ti-6Al-4V.
Xuda Qin   +6 more
doaj   +1 more source

Vision Based On-Machine Measurement of Flank Wear in Drill Tool for Smart Machine Tool [PDF]

open access: yes한국정밀공학회지, 2018
Tool wear is an essential parameter in determining tool life, machining quality and productivity. Current or power signals from motor drivers in machine have been used to estimate tool wear.
Tae-Gon Kim, Kangwoo Shin, Seok-Woo Lee
doaj   +1 more source

Machinability analysis of Ti-6Al-4V under cryogenic condition  

open access: yesJournal of Materials Research and Technology, 2023
Aerospace alloys are termed as hard to cut materials owing to their low thermal conductivity, high temperature strength and elevated chemical reactivity. Cryogenic conditions can be effectively deployed to improve the machinability and productivity while
Muhammad Ali Khan   +3 more
doaj   +1 more source

Woodworking Tool Wear Condition Monitoring during Milling Based on Power Signals and a Particle Swarm Optimization-Back Propagation Neural Network

open access: yesApplied Sciences, 2021
In the intelligent manufacturing of furniture, the power signal has the characteristics of low cost and high accuracy and is often used as a tool wear condition monitoring signal.
Weihang Dong   +3 more
doaj   +1 more source

Measurements of Tool Wear Parameters Using Machine Vision System

open access: yesModelling and Simulation in Engineering, 2019
Monitoring tool wear is very important in machining industry as it may result in loss of dimensional accuracy and quality of finished product. This work includes the development of machine vision system for the direct measurement of flank wear of carbide
Avinash A. Thakre   +2 more
doaj   +1 more source

Effect of Cutting Parameters on Tool Chipping Mechanism and Tool Wear Multi-Patterns in Face Milling Inconel 718

open access: yesLubricants, 2022
Tool wear behavior is mainly influenced by cutting parameters for a given tool–workpiece pair and cutting process. Rapid tool wear increases production costs and deteriorates machining quality in manufacturing industries.
Delin Liu, Zhanqiang Liu, Bing Wang
doaj   +1 more source

Home - About - Disclaimer - Privacy