The development of toolwear conditioning monitoringmethod is proposed by applying ceramic piezoelectric sensor mounted on the tool holder in turning machine to monitor vibration signals due to the flank wear progression. Signals captured by the sensor are statistically analysed using Integrated Kurtosis-based Algorithm for Z-notch filter (I-kaz TM ) technique. This technique produces a 3D graphic form that is quantified by coefficient of I-kaz (Z ), representing the degree of scattering of data distribution. The result indicates that I-kaz 3D graphic is experiencing contractionary, while Z values are getting smaller as the flank wear and cutting speed increases.
In general, the captured signals are raw signals that undergoes signal processing to extract significant features out of them. During the feature extraction stage, the most appropriate features, which correlate well with toolwear and not affected by process conditions are extracted from the captured signals (Siddhpura and Paurobally, 2013). The overall signal represents the total energy content of all vibration sources at all frequencies. The overall vibration can be quantified in mathematical or statistical approach in terms of the values such as peak, peak-to- peak, root means square and average. Then the values can be determined so as to identify the machining condition (Hassan et al., 2018). Although this approach is useful in terms of its simplicity, it ignores the dynamic information contained in the signal. An alternative is to investigate the frequency distribution of the signal. Many researchers investigate the toolwear based on vibration signal analysis for multiple conventional machining processes that includes drilling, milling, lathe and grinding (Hassan et al., 2018; D’Addona et al., 2016; Zhang et al., 2008; Chen and Li, 2007). The concept and the method to analyse the signal is similar among the reaserchers. The filtering carried out from the raw signal eliminates noise from the machining process. The filtering signal were plotted into time domain and frequency domain to analyse the raw data signal from the machining process. Allowing the identification and categorization of the tool state i.e. sharp tool and worn tool from the vibration signal of the machining process. Subsequently, feature extraction is conducted on the signal for further application in tool condition monitoring (TCM).
Industrial technology have grown rapidly over the century, thus, machine monitoring system must undergo a tremendous change to suit the needs. Machine monitoring system is a process of monitoring the condition of machine when operate. The system is essential especially for unmanned machining, as it capable of identifying machining system defects or failures and their location. That way, maintenance works can be done according to plan, making sure the machine instrument and system are in good condition and can be used regularly. This indirectly prevent any further loss and shorten the time and cost needed to accomplish certain operation. A good machiningmonitoring can be developed by a better understanding on the basic operation of machine being handled, identified parameter, workpiece and cutting tool’s type of material, wear of tool or insert, and method for monitoring (Byrne et al., 1995; Sick, 2002).
The logic structure of this paper is as follows. Section 1 describes in detail the experimental set-up (including work material, tool material and geometry, and the cutting conditions) and the methods of measuring the tool-edge profile, the cutting forces and vibrations. Section 2 presents the experimental results of dynamic tool-edge wear and its effects on the cutting forces and vibrations. Representative examples of the tool-edge profiles and the variations of the cutting forces and vibration amplitudes at different cutting time intervals are provided. Section 3 performs time-frequency domain analysis of the vibration signals via wavelet packet transform. Representative examples of the third-level wavelet packet decomposition of vibration signals at different cutting time intervals are also provided. The major research findings are summarized at the end of this paper.
The proper understanding of the material removal mechanisms taking place during hard cutting is essential for process evaluation. The analysis of the work area is necessary to describe the chip generation in hardened materials. Depending on cutting parameters and workpiece material properties, cutting may either lead to continuous or discontinuous chip formation [2, 6, 7]. Continuous chips are formed during turning conventional soft steels (Fig. 2 - a decrease in flow stress due to thermal softening associated with increase in strain is less than offsets the associated strain hardening ), while hard turning can lead to a formation of segmented chips. Fig. 1, 2 illustrate the segmented chip in turning hardened steel. Fig. 1 illustrates that plastic deformation inside the segment is low and material in this area stays nearly untouched. Deformation processes are concentrated in the shear zone, tool – chip contact and tool – workpiece contact. Formation of continuous chip leads to homogenous dissipation of deformation across the whole chip (Fig. 2).
Summary. Several works have been presented about toolwearmonitoring systems (TWMS) using Artificial Neural Networks(ANN), fed with measurements from sensors. They show good results for detecting whether the tool is fresh or worn. In most of these works the tests were carried out for one type of tool (usually uncoated and with a flat rake face) artificially worn, one type of workpiece material and under a narrow range of cutting conditions. Although these results are important, they cannot be accepted as being good in practical situations, where coated tools with chip breakers and different materials are frequently used. The present work reports the results of research to build a TWMS for two types of tools and two different materials. During the tests for data acquisition the tools were continually used in a turning operation. Before interruptions to observe the stage of wear of the tool, measurements of force and acceleration were undertaken. The data obtained were used to train and to test the ANNs. The results suggest that for a TWMS to work successfully in practical situations, it must have a characteristic design different from those developed to be used for one type of tool and one type of material.
ing system of CNC Turning center ML-300 was used for wet conditions. It has a coolant pump of 0.8 kW rating which uses water based coolant oil shell dromus B. A steady flow rate of 6 L min −1 was maintained during wet cutting. High pressure cylinder XL-160 was used in the cryogenic setup. It can store 160 L of cryogenic media. Liquid nitrogen was selected as cryogenic media because of its effectiveness with titanium work material and carbide tool combination (Hong, 2006). Due to its worldwide availability and inert nature LN2 is the most widely used cryogenic media (Jawahir et al., 2016). Pressure of 20 psi was maintained using a pressure regulator developing a flow rate of 4 L min −1 . Vacuum insulated pipes were used to carry the media to two copper pipes with 4 mm dia through a bifurcated cryogenic needle valve. Previous re- searches (Bermingham et al., 2012b; Hong and Ding, 2001; Mia et al., 2019) have found that using dual jets one each at flank and rake face produced optimum results.
The present work concerned an experimental study of turning on Austenitic Stainless steel of grade AISI 202 by a TiAlN coated carbide insert tool. The primary objective of the ensuing study was to use the Response Surface Methodology in order to determine the effect of machining parameters viz. cutting speed, feed, and depth of cut, on the surface roughness of the machined material and the wear of the tool. The objective was to find the optimum machining parameters so as to minimize the surface roughness and toolwear for the selected tool and work materials in the chosen domain of the experiment. The experiment was conducted in an experiment matrix of 20 runs designed using a full-factorial Central Composite Design (CCD). Surface Roughness was measured using a Talysurf and toolwear with the help of a Toolmaker‟s microscope. The data was compiled into MINITAB ® 17 for analysis. The relationship between the machining parameters and the response variables (surface roughness and toolwear) were modelled and analysed using the Response Surface Methodology (RSM). Analysis of Variance (ANOVA) was used to investigate the significance of these parameters on the response variables, and to determine a regression equation for the response variables with the machining parameters as the independent variables, with the help of a quadratic model. Main effects and interaction plots from the ANOVA were obtained and studied along with contour and 3-D surface plots. The quadratic models were found to be significant with a p-value of 0.033 and 0.049. Results showed that feed is the most significant factor affecting the surface roughness, closely followed by cutting speed and depth of cut, while
The graphical comparison of the wear of both selected geometries of the cutting inserts shows that the RCMT 10T3M0 – F2 geometry had a higher flank wear resistance than the SNMG 120412 – MR3 during Inconel 625 machining. This could be due to the fact that the circular ge- ometry of the cutting insert is better able to with- stand stress along the entire part of the cutting edge, which is leaning towards the cut with the machined material. For machining with a cutting depth of 1.5 mm, the cutting inserts were in con- tact with a larger diameter of the removed layer of material. The outgoing chips and hard carbides caused more intensive wear on the rake of RCMT 10T3M0 – F2 than in the case of SNMG 120412 – MR3. The wear of the rake of a tool with circu- lar geometry and the occurrence of built-up edge proved to be the main cause for the termination of the tests preceding the breakage of the tool cutting edge.
While the industrial nature of this work has remained an important concern through- out, it is worth drawing attention to the further requirements needed for a full in- dustrial implementation of such a predictive model. The concern here relies on a real-time, on-line system implementation rather than the off-line development work focused on in this work to date. Considering the progressive nature of wear and the relatively long usage duration, this should be a somewhat straightforward task, as the time to process a sample and make a prediction is small in comparison to process length. For instance, say a tool remains useful for approximately two hours; taking a ten-second data sample and allowing five seconds for processing and prediction results in 480 predictions throughout its life; an adequate number given the smooth wear curves expected. Again, data storage can be kept to a minimum by discarding data samples once useful features are taken from them and a prediction made. The use of an embedded hardware solution is an attractive option given their inher- ent robustness and speed of bespoke task execution. The compactRIO from National Instruments, for example, contains a field-programmable gate array (FPGA) which can be configured to perform an application specific hardware task at great speed when compared to the everyday computer system. Such a system is also intended to be fully compatible with the compactDAQ hardware used throughout this work, sharing data acquisition modules and therefore providing a logical choice of hard- ware. Such a device (when programmed to suit the task at hand) has the ability to acquire data, perform feature extraction and wear prediction, and also to export these predicted values in such a way that they can be interfaced with a computer numerical control (CNC) system.
Surface finish is another essential output variable of machining process discussed in previous studies.de Oliveira Junior et al. performed tests with input variables such as cutting speed and machining environment with low and high fluid pressure. The study reported that better surface finish can be obtained while turning with PVD-coated inserts under high-pressure cooling . Selvaraj et al. discussed the effect of dry turning process using Taguchi method and optimization of surface roughness, cutting force, and toolwear of nitrogen alloyed duplex stainless steel. The study concluded that t the feed rate is the more critical parameter influencing the surface roughness and cutting force while cutting speed was responsible for more toolwear . A methodology to predict surface roughness in low-speed while turning of AISI316 steel is developed by Acayaba and de Escalona. They used Artificial Neural Network (ANN) model integrated with Simulated Annealing (SA) to predict the surface finish. The model obtained predicted the surface roughness variation of 15% under the same cutting conditions . Coolants play a significant role in achieving lower toolwear rate and better surface finish in a machining process. Sivaiah and Chakradhar compared machining performance during turning of 17-4 PH stainless steel under wet machining and cryogenic conditions. Cutting temperatures, cutting force, chip morphology, toolwear and surface finish under cryogenic machining environment were observed. Cryogenic environment resulted in better values of output compared to wet conditions . Therefore we can conclude that the surface roughness and toolwear depends on cutting speed and feed. As cutting speed increases with low feed rate better surface finish is obtained. Also, to reduce toolwear, cutting speed must be increased. Depth of cut and cutting speed affects material removal rate and cutting force. Literature reviewed suggests that studies were made on the relative performance of machining environment, parameters, and tools on toolwear and surface roughness. However, in the machining of austenitic stainless steel AI 316 studies found to be rarely given attention. Toolwear that adversely impacts the quality of the product and in general machining performance has been overlooked. A number of studies are presented by researchers where cutting parameters were optimized for surface roughness or cutting force(s) or both. However, tool life, which is an essential factor for the economic viability of the machining process, was not considered in most of the studies. Thus, there is a need for the development of a robust model which can predict the machining performance during turning of austenitic stainless steel.
- maintaining the monitoring system and defining the parameters is often demanding and complicated. It is possible to identify the system condition when failure shows in the measuring signal. The influence of failure on the measuring signal should not only be theoretical, but should act on the signal flow with the possibility of reproduction (wear forces, vibrations, sound emission, etc.). Unfortunately, it is not always (if ever) possible to establish a simple link between the condition of the system and the signal, but signal changes can result due to various causes, so that the interpretation of failures significantly influences the efficiency and reliability of the monitoring system. The development of sensory methods and systems is led by the tendency to realize the maximum reliability in most machining conditions, and the improvement of sensitivity on the observed phenomenon . Regarding the required reliability of the monitoring system, sensors have to satisfy various needs with regard to detection of the condition. On the one hand, failures need to be detected very quickly, and on the other hand, the decisions have to be trustworthy, so as to eliminate losses due to false alarms. The problems of noise analyses, and the often contradictory information of senses in signal analysis, represent the focus of research, since even the most successful strategy of decision-making is limited if the input information is not sufficiently extensive and reliable.
19 experiments. They machined 35 mm diameter 400 series stainless steel flats with a surface finish of 25 nm Ra. Without cryogenic conditions, the rapid toolwear caused cutting to cease before the flat could be fully machined. The specific chemical mechanism was not discussed. For further analysis of the specific reactions that are occurring, molecular dynamics (MD) simulations could help to predict the exact chemical mechanisms taking place. Narulkar et al.  used MD simulations between diamond and iron to investigate the chemical interaction taking place. They performed the simulations at three temperatures (300, 800 and 1600 K) and three contact times (0, 40 and 80 picoseconds). In the simulations, diffusion only occurred when a diamond-graphite interlayer was added to the diamond and no diffusion occurred at the lower temperature (300 K). In later MD simulations, Narulkar et al.  claimed to provide evidence of graphitization during the machining of pure iron with diamond.
Two PCD and one diamond coated tool were compared- shown in Fig. 2. These will be referred to as tool (a), (b) and (c). Tool (b) is a solid carbide diamond coated tool, with nanocrystaline CVD coating. It is a burr style tool with a segmented helix and 12 flutes. Multiple cutting teeth are created when the primary helix is intersected. There are two cutting edges on each tooth, and a third edge which allows material removal. Tools (a) and (c) are both PCD with three flutes. The PCD Tool (a) has variable helix angle flutes with one negative one positive and one zero to minimise delamination. Tool (c) has 3 positive helix angle flutes. The cutting conditions and toolmachining distance reached before catastrophic tool failure are shown in Table 1, and each test was repeated once. Cutting feeds and speeds were chosen according to manufacturer’s recommended cutting parameters, and a similar feed per tooth was used across the three tools. In industry machining time needs to be optimised while maintaining surface quality. Therefore, a relatively high feed was chosen to challenge the capability of the milling tools. Table 1. Machining parameters and meters Machined by tool before failure.
Control chart (originally developed by Walter Shewhart in late 1920s) has played a key role in monitoring products’ quality. The idea behind the developing of Shewhart control chart is that repeated measurements from a process will exhibit variation. In a stable process, the variation can be easily predicted and can be approximated by one of several statistical distributions. The sole purpose of control chart is to keep the process near the target value and within boundaries of natural variations (Benneyan, Lloyd, & Plsek, 2003). On the other hand, response surface methodology (RSM) is a structured methodology of design of experiments (DoE) for systematically applying statistics to experimentations allowing the user to find relationships between the different input factors affecting the outputs. RSM typically involves setting up a combination of experiments, in which all relevant factors are varied systematically. These experiments are then analysed, allowing the user to find optimal parameters and the main factors affecting the results as well as identifying the interactions and synergies between factors if existed. It can be adopted whenever a phenomenon is to be investigated whether to gain more understanding or to achieve a better performance regardless of their background (Maged, Haridy, Kaytbay, & Bhuiyan, In Press).
Fig. 5A–C indicates the amperometric responses of AA on mouse IgG immunosensors. AA was produced through ALP enzyme. With the potentials of 0.40 V, 0.40 V, and 0.50 V, the experiment was conducted on the basis of electrodes of poly(o-ABA) together with GC, Au, as well as SPC correspondingly. On every electrode immunosensor, the fitful potentials could be gained if measured through hydrodynamic voltametric. The current density of target were 22.49 μA/cm 2 , 33.10 μA/cm 2 and 43.50 μA/cm 2 for poly(o-ABA) together with SPC, GC as well as Au correspondingly. Through the analysis of the experiment, the maximum response ratio of current density between control group and target group, roughly 297, was achieved with mouse IgG immunosensor on poly(o-ABA)/Au. This suggested the poly(o-ABA) on the appearance of Au were likely to generate an appearance which has the minimum no-certain adherence. Therefore, in the following research, such immunosensor was preferably adopted as to mouse IgG. The use of typical ensemble properties of nanoparticles as a recognition signal resulted in the detection limit for the target IgG of about 100 pM. The target IgG at the 10 pM level, however, was identified utilizing the electrocatalytic amplification of single nanoparticle collisions . For the behavior of larger molecule of substrate may exhibit slower turnover in the enzymes reaction and may also exhibit slower diffusion though the biolayer towards the electrode surface. Under such circumstances, using small molecules of AAP, after the AAP substrate is added in the Tris buffer solution, it can be generated to AA very fast, becoming a stable current within 30 s at all of three immunosensors. Preparing mouse IgG SAM/Au immunosensors achieved optimization 6-mercapto-1-hexanol with diverse proportions was obtained from 11- mercaptoundecanoic acid. The adoption of 1:9 to be single layer mixture contributed to the generation of the maximum proportion for target to control, probably since the elecated proportion of 6-mercapto- 1-hexanol could achieve the minimization of non-certain adsorption in an efficient way. Meanwhile, adequate binding could be supplied by 11-mercaptoundecanoic acid to anti-mouse IgG.
Consequently there is a light decrease in the electrical resistance to the "starting value" during the crossing of the tool to the beginning of the next cut. The electrical resistance does not fall to zero, but to the value corresponding with the electric system and with practical influence on resistance layer wear, potential created built up edge with other particles arising in the machining process.
The machining tests were carried on a lathe-turning machine “LEADWELL LTC25iL” (2500 rev/min and a maximum power of 24 kilowatts). A three-component force sensor (9275B) was placed on the tool to acquire the three force components. A charge amplifier (Kistler 5019B) was used to convert the electrical charge yield by the piezo- electric sensor into proportional voltage. The analog signals output are amplified by a charge amplifier (Kistler 5019B). Thereafter, they are passed to an analog/digital converter. The evolution of the effort over time is provided by the Cat- man Easy software. A Seco JetStream PCLNR 2525M12 tool was used with CNMG120412-23 H13A insert showing
An alternative for offline methods of studying toolwear is online methods, which require measuring external signals such as Acoustic Emission (AE), machining forces, spindle power consumption, and vibration, and relating them to toolwear (Cuppini et al., 1990; Li, 2002; Lin and Yang, 1995; Wang et al., 2002). The advantage of online methods over offline methods is that there is no need to interrupt the process, so online methods can be incorporated in automated machining processes without the loss of productivity. The main disadvantage is the existence of noise in signals, which requires some additional signal processing for extracting the related features of the signal to relate to toolwear. Another challenge is to find a suitable model for estimation. There are various models proposed by researchers which are categorized as (i) empirical models that characterize toolwear through running multiple experiments in different cutting conditions and fitting an appropriate function (Li, 2002), (ii) mechanistic models that relate cutting force or power through mechanically derived equation to toolwear (Cuppini et al., 1990; Fu et al., 1984; Xu et al., 2011), and (iii) dynamic models that characterize the progress of toolwear in state space time series (Danai and Ulsoy, 1987a; Danai and Ulsoy, 1987b).
The thermocouple displays a lag of approximately one second, while the IR sensor shows a fast response, showing a spike in surface temperature quite soon after engagement. However, the IR sensor signal does not display reliable temperature data for the entire cutting operation, due to chips blocking the path of the beam. Further work is required to either redirect the chips away from the sensor, or to find another suitable location for temperature measurement during machining.