Assessing the Condition of Rock Tools through Waveform Analysis Techniques of Rock Drilling Induced Vibration Signals

Detection of precise moment of button failure of a drill bit during a hard rock drilling process is vital to increase the efficiency in drilling. Sound generated during a hard rock drilling process was analysed using waveform analysis techniques. Wavelet transform analysis can detect the exact moment of button failure and the frequency range, which the failure occurred. Wavelet Transform is more effective than Fourier Transform and time series analysis in singularity detection

Quality and inspection of machining operations: Review of condition monitoring and CMM inspection techniques 2000 to present

In order to consistently produce quality parts, many aspects of the manufacturing process must be carefully monitored, controlled, and measured. The methods and techniques by which to accomplish these tasks has been the focus of numerous studies in recent years. With the rapid advances in computing technology, the complexity and overhead that can be feasibly incorporated in any developed technique has dramatically improved. Thus, techniques that would have been impractical for implementation just a few years ago can now be realistically applied. This rapid growth has resulted in a wealth of new capabilities for improving part and process quality and reliability. In this paper, overviews of recent advances that apply to machining are presented. Moreover, due to the relative significance of two particular machining aspects, this review focuses specifically on research publications pertaining to using tool condition monitoring and coordinate measurement machines to improve the machining process. Tool condition has a direct effect on part quality and is discussed first. The application of tool condition monitoring as it applies to turning, drilling, milling, and grinding is presented. The subsequent section provides recommendations for future research opportunities. The ensuing section focuses on the use of coordinate measuring machines in conjunction with machining and is subdivided with respect to integration with machining tools, inspection planning and efficiency, advanced controller feedback, machine error compensation, and on-line tool calibration, in that specific order and concludes with recommendations regarding where future needs remain

Multi-categories tool wear classification in micro-milling

Ph.DDOCTOR OF PHILOSOPH

Air-Drill With Accelerometer And Microphone For Composite Drilling Application

Impaired dimensional accuracy and assembly tolerance due to tool wear could leads to serious problem such as part dismissals in substantial amount on the structure of aircraft. To make sure that drilled holes in composite materials are of good quality from various aspects, current practice in the industries was the disposal of the drill bits after it is being used for a predetermined number of cycles. However, this leads to wastage of some of the expensive drill bit which can still produce holes without delamination even though it has been used for predetermined number of cycles. To monitor the tools condition, researchers have conduct various experiment to correlate tool wear with process variables such as cutting force, thrust force, vibrations, spindle current, acoustic emission etc. In this research, the effect of sharpness of drill bits on vibrations of air drill, sound produced during drilling and quality of holes produced will be studied. In this experiment, 3 accelerometers have been attached on locations near the spindle, at the middle and near the handle of air drill to measure the vibrations of the air drill. Microphone was placed about 1 meter away from the jig and the sounds produced during drilling process were collected. The vibrations of air drill and sound produced during drilling with different conditions of drill bit are analysed and compared. The result shows that vibrations measured by accelerometer placed nearest to the spindle are most sensitive to change in condition of drill bit. For CFRP plate, maximum amplitude of air drill vibration increases with condition of air drill in order: sharp, no drill bit, blunt, and chipped off. For GFRP plate, maximum amplitude of air drill vibration increases with condition of air drill in order: no drill bit, blunt, chipped off and sharp. Other than that, the fast Fourier Transform of the vibration signals from this accelerometer shows that there was a unique set of frequencies with significant peaks with different conditions of drill bits

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

A Probabilistic-Based Approach to Monitoring Tool Wear State and Assessing Its Effect on Workpiece Quality in Nickel-Based Alloys

The objective of this research is first to investigate the applicability and advantage of statistical state estimation methods for predicting tool wear in machining nickel-based superalloys over deterministic methods, and second to study the effects of cutting tool wear on the quality of the part. Nickel-based superalloys are among those classes of materials that are known as hard-to-machine alloys. These materials exhibit a unique combination of maintaining their strength at high temperature and have high resistance to corrosion and creep. These unique characteristics make them an ideal candidate for harsh environments like combustion chambers of gas turbines. However, the same characteristics that make nickel-based alloys suitable for aggressive conditions introduce difficulties when machining them. High strength and low thermal conductivity accelerate the cutting tool wear and increase the possibility of the in-process tool breakage. A blunt tool nominally deteriorates the surface integrity and damages quality of the machined part by inducing high tensile residual stresses, generating micro-cracks, altering the microstructure or leaving a poor roughness profile behind. As a consequence in this case, the expensive superalloy would have to be scrapped. The current dominant solution for industry is to sacrifice the productivity rate by replacing the tool in the early stages of its life or to choose conservative cutting conditions in order to lower the wear rate and preserve workpiece quality. Thus, monitoring the state of the cutting tool and estimating its effects on part quality is a critical task for increasing productivity and profitability in machining superalloys. This work aims to first introduce a probabilistic-based framework for estimating tool wear in milling and turning of superalloys and second to study the detrimental effects of functional state of the cutting tool in terms of wear and wear rate on part quality. In the milling operation, the mechanisms of tool failure were first identified and, based on the rapid catastrophic failure of the tool, a Bayesian inference method (i.e., Markov Chain Monte Carlo, MCMC) was used for parameter calibration of tool wear using a power mechanistic model. The calibrated model was then used in the state space probabilistic framework of a Kalman filter to estimate the tool flank wear. Furthermore, an on-machine laser measuring system was utilized and fused into the Kalman filter to improve the estimation accuracy. In the turning operation the behavior of progressive wear was investigated as well. Due to the nonlinear nature of wear in turning, an extended Kalman filter was designed for tracking progressive wear, and the results of the probabilistic-based method were compared with a deterministic technique, where significant improvement (more than 60% increase in estimation accuracy) was achieved. To fulfill the second objective of this research in understanding the underlying effects of wear on part quality in cutting nickel-based superalloys, a comprehensive study on surface roughness, dimensional integrity and residual stress was conducted. The estimated results derived from a probabilistic filter were used for finding the proper correlations between wear, surface roughness and dimensional integrity, along with a finite element simulation for predicting the residual stress profile for sharp and worn cutting tool conditions. The output of this research provides the essential information on condition monitoring of the tool and its effects on product quality. The low-cost Hall effect sensor used in this work to capture spindle power in the context of the stochastic filter can effectively estimate tool wear in both milling and turning operations, while the estimated wear can be used to generate knowledge of the state of workpiece surface integrity. Therefore the true functionality and efficiency of the tool in superalloy machining can be evaluated without additional high-cost sensing

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