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

Investigation of Chip Thickness and Force Modelling of Trochoidal Milling

With the ever increasing pressure to reduce processing time and cost, researchers in machining have begun to develop a body of work centered around increasing the throughput of machining operations. While standard toolpaths exist, such as raster and zig-zag, alternative toolpaths have been developed to achieve beneficial kinematics and dynamics for the cutting tool to better achieve high-speed machining conditions. One such toolpath, trochoidal milling, has been identified to decrease machining process time and increase overall tool life. Understanding the undeformed chip thickness produced utilizing trochoidal milling is critical to developing advances in the field. This paper presents a novel approach to modelling the chip thickness of the process for low to medium range cutting speeds. It has been found that the tool path cannot be described as a purely circular path, instead requiring the model of a true trochoid, which is presented in this work. Utilizing efficient, numerical method, the instantaneous chip thickness is solved for and validated experimentally with cutting force measurement, using a semi-mechanistic force model, where the experimental cutting forces find good agreement with the simulated results

Investigation of Chip Thickness and Force Modelling of Trochoidal Milling

With the ever increasing pressure to reduce processing time and cost, researchers in machining have begun to develop a body of work centered around increasing the throughput of machining operations. While standard toolpaths exist, such as raster and zig-zag, alternative toolpaths have been developed to achieve beneficial kinematics and dynamics for the cutting tool to better achieve high-speed machining conditions. One such toolpath, trochoidal milling, has been identified to decrease machining process time and increase overall tool life. Understanding the undeformed chip thickness produced utilizing trochoidal milling is critical to developing advances in the field. This paper presents a novel approach to modelling the chip thickness of the process for low to medium range cutting speeds. It has been found that the tool path cannot be described as a purely circular path, instead requiring the model of a true trochoid, which is presented in this work. Utilizing efficient, numerical method, the instantaneous chip thickness is solved for and validated experimentally with cutting force measurement, using a semi-mechanistic force model, where the experimental cutting forces find good agreement with the simulated results

Evaluation of Orthodontic Palatal Expansion in the Treatment of Nocturnal Enuresis

The purpose of this research was to evaluate the effect of palatal expansion in the treatment of"nnocturnal enuresis. Enuresis still remains a problem experienced by children and the reason is unclear."nFinding a final solution is being sought by the medical profession."nNocturnal enuresis may lead to numerous physical and emotional problems. Currently available"ntreatment options showed satisfactory results in some cases."nA treatment sample of 6 boys and 4 girls who ranged in age from 6 to 9 years were selected."nResearch showed that with palatal expansion (4-7 mm), patients could breath through their nose rather"ntheir mouth; as a result, enuresis is reduced significantly

Extended Kalman Filter for Stochastic Tool Wear Assessment in Turning of INC718 Hard-to-Machine Alloy

An Extended Kalman Filter (EKF) is employed in this work for tracking tool flank wear area in wet-turning of Inconel 718 (INC718) Nickel-based alloy in variable feed condition. The tool wear area evolution is modeled with a 3rd order polynomial empirical function and an analytical solution for discrete state space system is derived. The state uncertainty was found to decrease up to 200-250μm of average flank wear length and then increase abruptly with an increase in tool wear. Therefore, the tool wear uncertainty was modeled with failure probability density, i.e. the bathtub function. While a constant uncertainty was considered for the measurement signal (spindle power). The root mean square error (RMSE) and the mean absolute error (MAE) were calculated in estimation of the tool wear area with experimental results and it was shown that the EKF was able to estimate the tool wear area with less than 0.05mm 2 RMSE but did not perform well in estimating the rate of the tool wear area

In-process Tool Flank Wear Estimation in Machining Gamma-prime Strengthened Alloys Using Kalman Filter

Monitoring tool wear in machining processes is one of the critical factors in reducing downtime and maximizing profitability and productivity. A worn out tool can deteriorate the surface finish or dimensional accuracy of the part. Due to the uncertainties that originate from machining, workpiece material composition, and measurement, predicting tool wear is a challenging task in modern manufacturing processes. Low cost sensing technology for measuring spindle current is commonly deployed in the CNC machine to measure spindle power consumption for predicting tool wear. In this study, spindle power information was integrated into a Kalman filter methodology to predict tool flank wear in cutting hard-to-machine gamma-prime strengthened alloys. Results show a maximum of 18% error in estimation, which indicates a good potential of using Kalman filter in predicting tool flank wear

Parameter Inference Under Uncertainty in End-Milling γ′-Strengthened Difficult-to-Machine Alloy

Nickel-based alloys are those of materials that are maintaining their strength at high temperature. This feature makes these alloys a suitable candidate for power generation industry. However, high wear rate and tooling cost are known as the challenges in machining Ni-based alloys. The high wear rate causes a rapid failure of the tool, and therefore, fewer data will be available for model development. In addition, variations in material properties and hardness, residual stress, tool runout, and tolerances are some uncontrollable effects adding uncertainties to the currently developed models. To address these challenges, a probabilistic Bayesian approach using Markov Chain Monte Carlo (MCMC) method has been used in this work. The MCMC method is a powerful tool for parameter inference and quantification of embedded uncertainties of models. It is shown that by adding a prior probability to the observation probability, fewer experiments are required for inference. This is specifically useful in model development for difficult-to-machine alloys where high wear rate lowers the cardinality of the dataset. The combined Gibbs–Metropolis algorithm as a subset of MCMC method has been used in this work to quantify the uncertainty of the unknown parameters in a mechanistic tool wear model for end-milling of a difficult-to-machine Ni-based alloy. Maximum of 18% error and average error of 11% in the results show a good potential of this modeling in prediction of parameters in the presence of uncertainties when limited experiments are available

Stochastic tool wear assessment in milling difficult to machine alloys

In the machining industry, maximising profit is intuitively a primary goal; therefore continuously increasing machining process uptime and consequently productivity and efficiency is crucial. Tool wear plays an important factor in both machining uptime and quality, and since tool failure is related to the surface quality and the dimensional accuracy of the end product, it is essential to quantify and predict this phenomenon with the best possible certainty. One of the most common ways of tool wear prediction is through the use of low cost spindle current sensing technology which is used to measure spindle power consumption in CNC machines and relate power increase to tool wear. In this work, two methods of stochastic filtering (i.e. Kalman and particle filter) were used in predicting tool flank wear in machining difficult-to-machine materials through spindle power consumption measurements. Results show a maximum of 15% average error in estimation, which indicates the good potential of using stochastic filtering techniques in estimating tool flank wear. In addition, the particle filter was used for online estimation of a spindle power model parameter with uniform and Gaussian mixture models as the initial probability density functions, and the evolution of this parameter to the true posterior density function over time was investigated