Investigating the performance of TiN and TiAIN coatings on milling cutter used for machining bimetal steel strip

Surface engineering of cutting tools (single point or multipoint) through advanced coatings (e.g., TiN) has contributed towards considerable improvement of tool life, productivity and machining quality [1] by modifying the tool substrate. New coating species (e.g., TiAlN) are also being developed to further improve the performance of cutting tools. In this study, milling tests were carried out with a TiN and TiAlN coated milling cutter to compare their performance. Physical Vapour Deposition (PVD) technique was used to deposit the coatings after carefully preparing the cutting edges. Flank wear measurement in the milling cutter teeth was used as the criterion for assessing performance of the coatings. It has been found that TiAlN coating has significantly reduced the flank wear in the milling cutter teeth compared to TiN coating both at new and reground conditions of the cutter. Abrasive and adhesive wear were identified as the main mechanisms of the flank wear in both TiAlN and TiN coated teeth. The information should be useful for tool designers, coating suppliers and manufacturing engineers

Detection Of Chipping In Ceramic Cutting Inserts From Workpiece Profile Signature During Turning Process Using Machine Vision

Ceramic tools are prone to chipping due to their low impact toughness. Tool chipping significantly decreases the surface finish quality and dimensional accuracy of the workpiece. Thus, in-process detection of chipping in ceramic tools is important especially in unattended machining. Existing in-process tool failure detection methods using sensor signals have limitations in detecting tool chipping. The monitoring of tool wear from the workpiece profile using machine vision has great potential to be applied in-process, however no attempt has been made to detect tool chipping. In this work, a vision-based approach has been developed to detect tool chipping in ceramic insert from 2-D workpiece profile signature. The profile of the workpiece surface was captured using a DSLR camera. The surface profile was extracted to sub-pixel accuracy using invariant moment method. The effect of chipping in the ceramic cutting tools on the workpiece profile was investigated using autocorrelation function (ACF) and fast Fourier transform (FFT). Detection of onset tool chipping was conducted by using the sub-window FFT and continuous wavelet transform (CWT). Chipping in the ceramic tool was found to cause the peaks of ACF of the workpiece profile to decrease rapidly as the lag distance increased and deviated significantly from one another at different workpiece rotation angles. From FFT analysis the amplitude of the fundamental feed frequency increases steadily with cutting duration during gradual wear, however, fluctuates significantly after tool has chipped. The stochastic behaviour of the cutting process after tool chipping leads to a sharp increase in the amplitude of spatial frequencies below the fundamental feed frequency. CWT method was found more effective to detect the onset of tool chipping at 16.5 s instead of 17.13 s by sub-window FFT. Root mean square of CWT coefficients for the workpiece profile at higher scale band was found to be more sensitive to chipping and thus can be used as an indicator to detect the occurrence of the tool chipping in ceramic inserts

Study On The Effect Of Tool Nose Wear On Surface Roughness And Dimensional Deviation Of Workpiece In Finish Turning Using Machine Vision.

Operasi pemesinan merupakan suatu kaedah umum bagi menghasilkan komponen-komponen mekanikal yang dikeluarkan di segenap pelusuk dunia. Permintaan terhadap perkakas mesin dalam setahun dilaporkan mencecah lebih daripada £10 bilion. The aim of this research is to study the direct effect of tool nose wear which is in contact to the surface profile of workpiece directly, on the surface roughness and dimensional deviation of workpiece using a developed machine vision in finish turning operation

Tool Wear Measurement Using Machine Vision.

Tool wear has been extensively studied in the past due to its effect on the surface quality of the finished product.Vision-based systems using a CCD camera are increasingly being used for measurement of tool wear due to their numerous advantages compared to indirect methods

Tool condition monitoring - An intelligent integrated sensor approach

Ph.DDOCTOR OF PHILOSOPH

Image processing and pattern recognition for industrial robotic vision

Imperial Users onl

Prediction of the Wear & Evolution of Cutting Tools in a Carbide / Ti-6Al-4V Machining Tribosystem by Volumetric Tool Wear Characterization & Modeling

The objective of this research work is to create a comprehensive microstructural wear mechanism-based predictive model of tool wear in the tungsten carbide / Ti-6Al-4V machining tribosystem, and to develop a new topology characterization method for worn cutting tools in order to validate the model predictions. This is accomplished by blending first principle wear mechanism models using a weighting scheme derived from scanning electron microscopy (SEM) imaging and energy dispersive x-ray spectroscopy (EDS) analysis of tools worn under different operational conditions. In addition, the topology of worn tools is characterized through scanning by white light interferometry (WLI), and then application of an algorithm to stitch and solidify data sets to calculate the volume of the tool worn away. The motivation for this work is two-fold. First, the evolving dominance of different wear mechanisms with time, as well as with significant tool and process factors has been characterized only in a limited fashion for this tribosystem. Traditional modeling of tool wear treats wear mechanisms individually. Hence, quantifying the mechanism-dominance at different operational conditions through a comprehensive approach of combining and weighting wear mechanisms is essential for understanding wear. Second is the critical need for better quantifying the wear itself. Wear is a 3D phenomenon. However, machining tool wear has historically been measured only in 1D which is inadequate to capture the true tool wear status, even with standardization. The methodology was to first combine and weight dominant microstructural wear mechanism models, to be able to effectively predict the tool volume worn away. Then, by developing a new metrology method for accurately quantifying the bulk-3D wear, the model-predicted wear was validated against worn tool volumes obtained from corresponding machining experiments. The changing dominance of different microstructural wear mechanisms was captured by formulating mechanism-weighting-factors from SEM imaging and EDS analysis. These were formulated for each of the three speed-regimes, which then fed into a multi-mechanistic volumetric wear rate model. On comparing this model-predicted wear to the actual tool volume worn away, prediction on the order of the observed wear was achieved, with better prediction at low and medium surface speeds - this was quantified by sum-of-squares computations. On analyzing worn crater faces using SEM/EDS, adhesion was found dominant at lower surface speeds, while dissolution wear dominated with increasing speeds - this is in conformance with the lower relative surface speed requirement for micro welds to form and rupture, essentially defining the mechanical load limit of the tool material. It also conforms to the known dominance of high temperature-controlled wear mechanisms with increasing surface speed, which is known to exponentially increase temperatures especially when machining Ti-6Al-4V due to its low thermal conductivity. Thus, straight tungsten carbide wear when machining Ti-6Al-4V is mechanically-driven at low surface speeds and thermally-driven at high surface speeds. Further, at high surface speeds, craters were formed due to carbon diffusing to the tool surface and being carried away by the rubbing action of the chips - this left behind a smooth crater surface predominantly of tungsten and cobalt as observed from EDS analysis. Also, at high surface speeds, carbon from the tool was found diffused into the adhered titanium layer to form a titanium carbide (TiC) boundary layer - this was observed as instances of TiC build-up on the tool edge from EDS analysis. A complex wear mechanism interaction was thus observed, i.e., titanium adhered on top of an earlier worn out crater trough, additional carbon diffused into this adhered titanium layer to create a more stable boundary layer (which could limit diffusion-rates on saturation), and then all were further worn away by dissolution wear as temperatures increased. At low and medium feeds, notch discoloration was observed - this was detected to be carbon from EDS analysis, suggesting that it was deposited from the edges of the passing chips. Mapping the dominant wear mechanisms showed the increasing dominance of dissolution wear relative to adhesion, with increasing grain size - this is because a 13% larger sub-micron grain results in a larger surface area of cobalt exposed to chemical action. On the macro-scale, wear quantification through topology characterization elevated wear from a 1D to 3D concept. From investigation, a second order dependence of volumetric tool wear (VTW) and VTW rate with the material removal rate (MRR) emerged, suggesting that MRR is a more consistent wear-controlling factor instead of the traditionally used cutting speed. A predictive model for VTW was developed which showed its exponential dependence with workpiece stock volume removed. Also, both VTW and VTW rate were found to be dependent on the accumulated cumulative wear on the tool. Further, a ratio metric of stock material removed to tool volume lost is now possible as a tool efficiency quantifier and energy-based productivity parameter, which was found to inversely depend on MRR - this led to a more comprehensive tool wear definition based on cutting tool efficiency

An Investigation of Chipping Generation and Propagation on Carbide Tool under Various Cutting Conditions in End Milling of Low Carbon Steel

Tool conditions are the essential factors in determining the geometric accuracy and the machined surface quality in the milling process. The different mechanisms of tool condition can be classified as tool wear, chipping, and built-up edge. The chipping, which is one of the decisive tool conditions when the brittle milling tools are used in milling, has not been well investigated by previous studies since the chipping is randomly occurs. Therefore, the main objective of this study is to comprehensively investigate the generation and propagation of chipping in the milling process. To realize this objective, the carbide milling tools were used to dry cut 1020 low carbon steel with different combinations of cutting speed and chip load. Under each combination, the cutting tool was evaluated in terms of various tool conditions over a certain cutting distance until the milling tool failed. The result showed that the chipping mainly occurred under the low spindle speed or high chip load per tooth since the cutting force was high. Once the chipping occurred on one flute, other flutes also had the chipping at the same position since the chipping occurred initially increased the chip load per tooth of the next flute. After the chipping was generated, it extended in the following milling process until the width of chipping met the failure criterion. It is found that most of the chipping extended and met the failure criterion in a short cutting distance. However, the chipping which propagated slowly shown three stages with different expansion rates before the end of tool life. Meanwhile, the flank wear was observed on the outline of chipping and was considered as a factor for the chipping propagation since the flank wear increases with the cutting force. The milling test was stopped at the end of tool life, and it was found that the tool life of all the milling tools was shorter than the tool life estimated by using the Taylor equation. However, the Taylor equation only considers the flank wear as a factor for the tool life, whereas, the chipping was dominated in this study

Chip Production Rate and Tool Wear Estimation in Micro-EndMilling

abstract: In this research, a new cutting edge wear estimator for micro-endmilling is developed and the reliabillity of the estimator is evaluated. The main concept of this estimator is the minimum chip thickness effect. This estimator predicts the cutting edge radius by detecting the drop in the chip production rate as the cutting edge of a micro- endmill slips over the workpiece when the minimum chip thickness becomes larger than the uncut chip thickness, thus transitioning from the shearing to the ploughing dominant regime. The chip production rate is investigated through simulation and experiment. The simulation and the experiment show that the chip production rate decreases when the minimum chip thickness becomes larger than the uncut chip thickness. Also, the reliability of this estimator is evaluated. The probability of correct estimation of the cutting edge radius is more than 80%. This cutting edge wear estimator could be applied to an online tool wear estimation system. Then, a large number of cutting edge wear data could be obtained. From the data, a cutting edge wear model could be developed in terms of the machine control parameters so that the optimum control parameters could be applied to increase the tool life and the machining quality as well by minimizing the cutting edge wear rate. In addition, in order to find the stable condition of the machining, the stabillity lobe of the system is created by measuring the dynamic parameters. This process is needed prior to the cutting edge wear estimation since the chatter would affect the cutting edge wear and the chip production rate. In this research, a new experimental set-up for measuring the dynamic parameters is developed by using a high speed camera with microscope lens and a loadcell. The loadcell is used to measure the stiffness of the tool-holder assembly of the machine and the high speed camera is used to measure the natural frequency and the damping ratio. From the measured data, a stability lobe is created. Even though this new method needs further research, it could be more cost-effective than the conventional methods in the future.Dissertation/ThesisDoctoral Dissertation Mechanical Engineering 201

Evaluation of 3D tool wear in machining by successive stereo-photogrammetry and point cloud processing

Procjena trošenja alata ima značajan utjecaj na kvalitetu proizvoda, kao i na učinkovitost proizvodnog procesa. Primijenjena je procjena trošenja alata temeljena na iskustvu i ukupnom vremenu trajanja obrade. Taktilni mehanički uređaji i optički mikroskop su također primijenjeni. Predlaže se i primjenjuje inovativna optička metoda mjerenja trošenja alata koja se zasniva na 3D skeniranju uporabom stereo-fotogrametrije i triangulacije. Metodom je moguće točno izmjeriti trodimenzionalne devijacije na ukupnoj površini rezne pločice, jer je trodimenzionalni vektor odstupanja oblika dobiven pomoću milijun točaka. Trodimenzionalnu funkciju svekupnog istrošenja alata moguće je dobiti i bez uklanjanja alata s alatnog stroja. Određivanje istrošenja alata kao trodimenzionalne funkcije nudi mnoštvo informacija prema kojima je moguće dovesti u vezu pojedinačne oblike trošenja alata s mogućim uzorcima trošenja.The tool wear evaluation has a very strong impact on the product quality as well as efficiency of the manufacturing process. Experience-based assessment of tool wear and total cumulative time of operation has been applied. Tactile mechanical sensing devices and optical microscopes have been applied as well. This paper proposes and applies an inovative optical tool wear measurement method. It is based on 3D optical sensing using stereo-photogrammetry and triangulation. It offers high accuracy 3D dimensional deviation measurement spanning over the total tool surface, hence 3D deviation vectors from some reference shape are obtained simultaneously for millions of points. The overall tool wear shape function in 3D is generated, in many cases even without disassembly of the tool. Capturing the tool wear as a 3D shape function potentially offers abundant information towards diagnostics in terms of correlating the particular tool wear shape function with respective potential causes