scholarly journals A Study on Proposal of Flank Wear Criterion by Using a Built-in Current Sensor when Manufacturing the Mold Materials in a Smart Machine Tool

2018 ◽  
Vol 207 ◽  
pp. 03007
Author(s):  
Seung-Yub Baek ◽  
Sung-Taek Jung ◽  
Dae-Yu Park
Keyword(s):  
Tool Wear ◽  
Machine Tool ◽  
Machine Tools ◽  
High Efficiency ◽  
Flank Wear ◽  
Acoustic Emissions ◽  
Measuring Method ◽  
Current Signal ◽  
Flank Face ◽  
Smart Machine

Recently, it has been increased with respect to the safe and reliable operations in industry of machine tools and intelligent of the machine tool has consistently been developing in term of an unmanned manufacturing. For such realization, diagnosis monitoring of machining must be carried out while being processed in real-time. When tool wear is reached to criteria of flank wear and crater wear, the tools must be changed to new tools for improving the manless rate of operation. However, time of tool change was when spark generated because of wear about 0.3 mm on a flank face during manufacturing in the field. So, built-in sensor system in a smart machine tool must be necessary for high efficiency unmanned of manufacturing. As mentioned earlier, the various technique for measuring the tool wear was already defined such as sensing of acoustic emissions, vibrations, sounds, currents, cutting force, and other. The representative one of measuring method is current signal, which is used as a representative index of tool state. In this study, we carried out the proposal of tool wear criterion by using built-in wireless current signal system when manufacturing the mold materials of KP-4M and it was investigated via smart machine tools.

scholarly journals Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosis

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Jianlei Zhang ◽  
Yukun Zeng ◽  
Binil Starly
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Machine Tool ◽  
Recurrent Neural Network ◽  
Machine Tools ◽  
Prediction Performance ◽  
Sequential Data ◽  
Diagnosis And Prognosis ◽  
Proposed Model

AbstractData-driven approaches for machine tool wear diagnosis and prognosis are gaining attention in the past few years. The goal of our study is to advance the adaptability, flexibility, prediction performance, and prediction horizon for online monitoring and prediction. This paper proposes the use of a recent deep learning method, based on Gated Recurrent Neural Network architecture, including Long Short Term Memory (LSTM), which try to captures long-term dependencies than regular Recurrent Neural Network method for modeling sequential data, and also the mechanism to realize the online diagnosis and prognosis and remaining useful life (RUL) prediction with indirect measurement collected during the manufacturing process. Existing models are usually tool-specific and can hardly be generalized to other scenarios such as for different tools or operating environments. Different from current methods, the proposed model requires no prior knowledge about the system and thus can be generalized to different scenarios and machine tools. With inherent memory units, the proposed model can also capture long-term dependencies while learning from sequential data such as those collected by condition monitoring sensors, which means it can be accommodated to machine tools with varying life and increase the prediction performance. To prove the validity of the proposed approach, we conducted multiple experiments on a milling machine cutting tool and applied the model for online diagnosis and RUL prediction. Without loss of generality, we incorporate a system transition function and system observation function into the neural net and trained it with signal data from a minimally intrusive vibration sensor. The experiment results showed that our LSTM-based model achieved the best overall accuracy among other methods, with a minimal Mean Square Error (MSE) for tool wear prediction and RUL prediction respectively.

scholarly journals Active vibration control for a CNC milling machine

2013 ◽  
Vol 228 (2) ◽  
pp. 230-245 ◽  
Author(s):  
DG Ford ◽  
A Myers ◽  
F Haase ◽  
S Lockwood ◽  
A Longstaff
Keyword(s):  
Control System ◽  
Tool Wear ◽  
Machine Tool ◽  
Vibration Control ◽  
Surface Finish ◽  
Machine Tools ◽  
Active Vibration Control ◽  
Milling Machine ◽  
Cnc Machine ◽  
Active Vibration

There is a requirement for improved three-dimensional surface characterisation and reduced tool wear when modern computer numerical control (CNC) machine tools are operating at high cutting velocities, spindle speeds and feed rates. For large depths of cut and large material removal rates, there is a tendency for machines to chatter caused by self-excited vibration in the machine tools leading to precision errors, poor surface finish quality, tool wear and possible machine damage. This study illustrates a method for improving machine tool performance by understanding and adaptively controlling the machine structural vibration. The first step taken is to measure and interpret machine tool vibration and produce a structural model. As a consequence, appropriate sensors need to be selected and/or designed and then integrated to measure all self-excited vibrations. The vibrations of the machine under investigation need to be clearly understood by analysis of sensor signals and surface finish measurement. The active vibration control system has been implemented on a CNC machine tool and validated under controlled conditions by compensating for machine tool vibrations on time-varying multi-point cutting operations for a vertical milling machine. The design of the adaptive control system using modelling, filtering, active vibration platform and sensor feedback techniques has been demonstrated to be successful.

Tool Wear Estimation in Drilling Using Acoustic Emission Signal by Multiple Regression and GMDH

2008 ◽  
Author(s):  
L. J. Sudev ◽  
H. V. Ravindra
Keyword(s):  
Regression Analysis ◽  
Tool Wear ◽  
Machine Tool ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Flank Wear ◽  
Cutting Tools ◽  
Regularity Criterion ◽  
Work Piece ◽  
Training Set

The cutting tool is the only element in a machine tool that requires frequent changes due to failure. Drill bit wear can cause catastrophic failure that can result in considerable damage to the work piece and the machine tool. Hence, there is an imperative need to keep a watch on the condition of the cutting tools during the machining process. Over the years, a wide variety of on-line or off-line techniques have been investigated for monitoring abnormal cutting tools. A variety of signals such as tool-tip temperature, forces, power, thrust, torque, vibrations, shock pulse, Acoustic Emission (AE) etc., have been used for monitoring tool failure by on-line technique. The detection and monitoring of AE is commonly used to predict tool failure. Present work involves estimation tool flank wear in drilling based on AE parameters viz., RMS, energy, signal strength, count and frequency by empirical methods of analysis like Multiple Regression Analysis and Group Method of Data Handling (GMDH). The experimental work consisted of drilling S.G Cast iron using high-speed steel drill bit and measuring AE parameters from the workpiece using AE measuring system for different cutting conditions. Machining was stopped at regular intervals of time and tool flank wear was measured by Toolmakers microscope. The experimental data were subjected to simpler methods of analysis to obtain a clear insight of the signals involved. The study of AE-time plots showed a similarity with three phases of tool wear, which implies that the measured AE parameters can be related to tool wear. Multiple Regression Analysis and Drilling is a major material removal process in manufacturing. Infact, the drills have been used widely in industry since the industrial revolution. It was estimated that 40% of all the metal removal operations in the aerospace industry is by drilling. Similar to the other cutting tools, after a certain limit, drill wear can cause catastrophic failure that can result in considerable damage to the work piece even to the machine tool [1]. GMDH methods were successful in estimating flank wear based on measured AE parameters. By Multiple Regression Analysis better estimation was obtained at lower cutting conditions. Three criterion functions of GMDH viz., Regularity, Unbiased and Combined were used for estimation with 50%, 62.5% and 75% of data in the training set. Estimation was done upto Level-4. The results of GMDH estimation showed that regularity criterion functions correlates well for the set of input variables compared with unbiased and combined criteria and least error of estimation was found when 75% of data was used in the training set. The optimum level of estimation increased with the increase in the percentage of data in the training set. Comparison of the performance of Multiple Regression Analysis and GMDH indicated that estimation by regularity criterion of GMDH had an edge over Multiple Regression Analysis.

Tool Wear in Ball-End Milling of Cr12MoV Die Steel Using an Indexable Cutter with the Asymmetric Inserts

2012 ◽  
Vol 500 ◽  
pp. 111-116
Author(s):  
Bin Zou ◽  
Chuan Zhen Huang ◽  
Zi Ye Liu ◽  
Xin Qiang Zhuang ◽  
Jun Wang
Keyword(s):  
Tool Wear ◽  
Flank Wear ◽  
End Milling ◽  
Cutting Conditions ◽  
Cutting Condition ◽  
Die Steel ◽  
Ball End Milling ◽  
Diffusion Wear ◽  
Flank Face ◽  
And Diffusion

Tool wear was investigated at the different cutting conditions in rough ball-end milling of Cr12MoV die steel using an indexable cutter with asymmetric inserts. The wear patterns on rake face and flank face of major insert and minor insert, and chip patterns were observed by VHX-600E large depth-of-view 3-D scanner. The relationships of tool wear and cutting conditions, and their mechanisms were discussed. The tool life was determined by the flank wear at No. 1 cutting condition. At Nos. 2-8 cutting conditions, the life of major inset and minor insert were determined by the wear of their rake faces and flank faces respectively. At No. 8 cutting condition, the tool wear was dominated by boundary wear, adhesion and diffusion wear, and the slight chipping. Both type and color of chips identified the cutting stability at the different cutting conditions.

Tool-Wear Monitoring and Control

2005 ◽  
Author(s):  
J. Srinivas ◽  
Rao Dukkipati ◽  
V. Sreebalaji ◽  
K. Ramakotaih
Keyword(s):  
Tool Wear ◽  
Machine Tools ◽  
Flank Wear ◽  
Cutting Parameters ◽  
Monitoring And Control ◽  
Tool Wear Monitoring ◽  
Wear Monitoring ◽  
Trained Neural Network ◽  
And Control ◽  
Control Methodology

This paper presents, a control methodology based on experimental data of the tool wear as a function of cutting variables. In automatic machine tools there is strong need to control the tool wear by adjustment of the cutting parameters. In this connection, a control system, which can adjust the cutting parameters for a desired wear rate, is necessary. A regression relation is also established between the flank-wear and the cutting parameters. An inversely trained neural network model, which supplies the modified values of the cutting parameters, is used as a controller. The results are shown in the form of tables and graphs.

scholarly journals INVESTIGATION OF NOISE TRANSMISSION OF A MACHINE TOOL ENCLOSURE

2019 ◽  
Vol 19 (3) ◽  
pp. 5-17
Author(s):  
Friedrich BLEICHER ◽  
Christoph REICHL ◽  
Felix LINHARDT ◽  
Peter WIMBERGER ◽  
Christoph HABERSOHN ◽  
...  
Keyword(s):  
Machine Tool ◽  
Machine Tools ◽  
Dynamic Stiffness ◽  
Measuring Method ◽  
Work Piece ◽  
Noise Emission ◽  
Mechatronic Systems ◽  
Major Interest ◽  
Noise Transmission ◽  
Moving Parts

Machine tools are highly integrated mechatronic systems consisting of dedicated mechanic design and integrated electrical equipment - in particular drive systems and the CNC-control - to realize the complex relative motion of tool towards work piece. Beside the process related capabilities, like static and dynamic stiffness as well as accuracy behavior and deviation resistance against thermal influence, safety aspects are of major interest. The machine tool enclosure must fulfill multiple requirements like retention capabilities against the moving parts of broken tools, lose work pieces or clamping components. In regular use, the noise emission have to be inhibited at the greatest possible extent by the machine tool enclosure. Nevertheless, the loading door and the moving parts of the workspace envelope are interfaces where noise transmission is harder to be avoided and therefore local noise emissions increase. The aim of the objective investigation is to analyse the noise emission of machine tools to determine the local noise transmission of a machine tool enclosure by using arrays of microphones. By the use of this measuring method, outer surfaces at the front, the side and on the top of the enclosure have been scanned. The local transient acoustic pressures have been recorded using a standard noise source placed on the machine table. In addition, an exemplary manufacturing process has been performed to analyse the frequency dependent location resolved sound emissions.

A Cloud Computing-Based Approach for Efficient Processing of Massive Machine Tool Diagnosis Data

2021 ◽  
pp. 2150297
Author(s):  
Heng Li ◽  
Xiaoyang Zhang ◽  
Shuyin Tao
Keyword(s):  
Cloud Computing ◽  
Tool Wear ◽  
Data Processing ◽  
Machine Tool ◽  
Execution Time ◽  
Machine Tools ◽  
Visual Detection ◽  
Detection Method ◽  
Image Block ◽  
Local Threshold

This paper proposes a cloud computing-based approach to efficiently process the massive data produced in intelligent machine tool diagnosis flow. By collecting and extracting the vibration, power and other useful system signals during the machining operation of machine tools, the cutting process samples and cutting gap samples of machine tools can be accurately segmented, in order to construct a set of signal samples that can effectively and completely characterize the level of tool wear. We propose a visual detection method that relies on local threshold segmentation to predict tool wear status. The machine tool image is divided into several small blocks, and each image block is segmented to obtain the segmentation threshold, which is defined as the local threshold of each block. Then, the detection method scans the whole image based on the maximum local threshold among all blocks. Considering the complicated flow of visual detection and the high volume of machine tool diagnosis data, we further propose a big data processing approach which is implemented on a cloud computing architecture. By modeling the workflow of the proposed visual detection method as a directed acyclic graph, we develop a scheduling model that aims at minimizing the execution time of massive tool diagnosis data processing with available cloud computing resources. A effective metaheuristic based on search strategy of artificial bee colony is developed to solve the formulation scheduling problem. Experimental results on a cloud-based system demonstrate that, the visual detection method enhances the accuracy of tool wear detection, and the cloud-based approach significantly reduces the execution time of tool diagnosis flow by means of distributed computing.

NURBS Interpolator Based on Dynamic Property of the Machine Tool

2008 ◽  
Vol 392-394 ◽  
pp. 355-360
Author(s):  
H.Y. Shen ◽  
Jian Zhong Fu ◽  
Zi Chen Chen
Keyword(s):  
Machine Tool ◽  
Dynamic Property ◽  
Machine Tools ◽  
High Efficiency ◽  
Simulation Experiment ◽  
Machining Parameters ◽  
Nurbs Interpolator ◽  
Look Ahead ◽  
Trace Back

The proposed Axis-based Look-ahead NURBS Interpolator (ALANI) strictly confines the component acceleration and jerk at each axis during every interpolating period based on the mechanics of the machine tools, so that the acceleration and jerk at axes can not exceed the limit in order to avoid immoderate vibrations and shocks. Contour precision is also strictly guaranteed while interpolating. The interpolator can trace back and recalculate previous data if necessary after forwards calculation and verification. And the recalculation algorithm is able to choose optimized machining parameters to attain high efficiency. The introduction of ALANI and simulation experiment is present in this paper.

Edge Preparation on Cutting Force, Cutting Temperature and Tool Wear for Hard Turning

2014 ◽  
Vol 800-801 ◽  
pp. 424-429
Author(s):  
Pei Rong Zhang ◽  
Zhan Qiang Liu
Keyword(s):  
Tool Wear ◽  
Cutting Force ◽  
Hard Turning ◽  
Flank Wear ◽  
Cutting Temperature ◽  
Cutting Edge ◽  
Crater Wear ◽  
Cutting Edge Preparation ◽  
Flank Face ◽  
Edge Preparation

The paper investigates the effects of cutting edge preparation on cutting force, cutting temperature and tool wear for hard turning. An optimized characterization approach is proposed and five kinds of cemented tools with different edge preparation are adopted in the simulations by DEFROM-2DTM. The results show that both the forces and cutting temperature on the rake face climb up and then declines with the increasing of factor K (Sγ/Sα). While the temperature on flank face decrease with the increasing of the factor K. When the cutting conditions are identical, flank wear reduces while crater wear exacerbates before easing with the increasing of the factor K. The simulation results will provide valuable suggestions for optimization of cutting edge preparation for hard turning in order to obtain excellent machining quality and longer tool life.

scholarly journals Brawn + Brains > 1

2002 ◽  
Vol 124 (09) ◽  
pp. 46-49
Author(s):  
Paul Sharke
Keyword(s):  
Crystal Structure ◽  
Machine Tool ◽  
Shape Memory Alloys ◽  
Shape Memory ◽  
Low Temperatures ◽  
Machine Tools ◽  
Basic Research ◽  
Intelligent Machines ◽  
Intelligent Tools ◽  
Smart Machine

This article highlights that shape memory alloys are finding their way into more earthbound applications. Alloys of nickel and titanium, named Nitinol after their primary constituents and for the Naval Ordnance Laboratory where they were discovered, can assume two shapes, depending on their crystal structure. Exhibiting martensitic structures at low temperatures and austenitic structures at higher ones, the alloys, in effect, remember two distinct patterns. Some aspects of intelligent machines, like detecting and adjusting the offsets in part location and tool dimensions, are almost routine, yet other aspects of smart machine tools are a long way off. Some areas await basic research. Making the goal of intelligent tools no less challenging is the difficulty of getting machine tool makers, sensor manufacturers, and other suppliers to the machinists’ trade to cooperate and share ideas in a normally competitive atmosphere.

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