Durul Ulutan

Investigation of Trochoidal Milling in Nickel-Based Superalloy Inconel 738 and Comparison With End Milling

Nickel-based superalloys are designed for use in extreme environments and are getting progressively better for these environments, therefore much harder to machine. They play a crucial role in elevated temperature applications where high strength, high resistance to corrosion and creep resistance are required. Machinability suffers as a result of these properties and harsh machining conditions occur, resulting in high cutting forces and tool wear. To combat the difficulties in the machining of nickel-based superalloys, such as poor thermal diffusivity and high levels of abrasive wear, trochoidal milling was introduced as an alternative method of milling. This method of milling combines linear motion with uniform circular motion, reducing chip load in exchange for increased machining time. Industry is averse to its widespread adoption due to increasing cycle times when compared to conventional milling methods, however it has been shown that overall productivity can be improved due to less tool wear with a more predictable behavior. This work characterizes the effects of trochoidal milling and provides a comparison of trochoidal milling with a traditional milling technique, end milling, for the machining of Inconel 738. In order to compare the trochoidal and conventional machining approaches directly, metrics of productivity normalized to tool wear are introduced. The normalized metrics introduced in this study aim to provide a more representative comparison of productivity and efficiency characteristics: volumetric material removal per unit tool wear (MR/VB) and the material removal rate per unit tool wear (MRR/VB). It was found that significantly higher volumetric material removal is possible using trochoidal milling, and fewer tools are needed; material removal rates that competitive with end milling can be achieved. When the amount of time spent on tool change for the same volume of material removal is considered, material removal rate of trochoidal milling can even be higher than end milling, indicating that better productivity and efficiency of the process is possible at reduced tooling costs.Copyright

A wavelet-based data-driven modelling for tool wear assessment of difficult to machine materials

In this work, wavelet packet decomposition along with principle component analysis are utilised for feature extraction using two low cost sensing methods: vibration and power sensors, in end-milling of gamma prime-strengthened alloy. The high wear rate of this material induces a rapid transition from a sharp state to a dull state of the tool, and hence limits the number of available data for model establishment. To address this challenge, a neural network with Bayesian regularisation is designed and its performance is compared with regression and time-series models. A maximum of 4% estimation error for Bayesian regularisation neural network, compared to 33% and 17% estimation error of the latter models, shows the good potential of this technique when a limited dataset is available. In addition, the use of low cost measuring sensors in this paper aligned well with the industrial applications to detect and avoid unscheduled downtime in machining situations.

Wavelet based sensor fusion for tool condition monitoring of hard to machine materials

Tool condition monitoring in modern manufacturing systems is gaining more attention due to the fact that excessive tool damage can cause workpiece surface deterioration and increase idle time. Therefore, monitoring tool condition from the initial to final stages of tool life is a task that is critical yet difficult, especially in hard-to-machine materials. In this work, Wavelet Packet Decomposition is used for extracting statistical features in the time-frequency domain of two low cost sensing technologies, i.e. vibration and power, in addition to Principal Component Analysis to reduce the dimensionality of feature vectors. A Recurrent Neural Network is then trained with Bayesian regularization backpropagation method and the estimated tool wear is compared to the actual measured wear. Results show a maximum of 13% relative error in estimating tool wear which proves the effectiveness of implemented sensory data fusion method to be used in automated control of manufacturing processes.

Prediction of Tool Wear Based on Cutting Forces When End Milling Titanium Alloy Ti-6Al-4V

Research regarding tool wear in the machining of difficult materials is important because it is a significant indicator of process failure in terms of degradation of part quality, and the resulting high cost and increased process time. Prior researchers have investigated the effects of cutting parameters on tool wear and as a result, tool life has seen significant improvement. However, these studies are not concerned with tool flank wear during machining; they instead focus on tool flank wear after a certain amount of cutting distance. This study proposes a new method of predicting tool flank wear during machining that has the capability of suggesting tool failure without directly measuring the tool. For this purpose, a detailed set of experiments on end milling of titanium alloy Ti-6Al-4V was conducted and analyzed. Then, the resultant force output, which can be monitored during machining, was used to establish a predictive algorithm for tool flank wear. Using the increase in the resultant force as well as the total energy spent on the workpiece, it was shown that tool flank wear can be effectively predicted during machining and this can decrease the time spent on tool failure inspection and early tool change, increasing the throughput of the process.Copyright

Parameter Estimation Using Markov Chain Monte Carlo Method in Mechanistic Modeling of Tool Wear During Milling

Several models have been proposed to describe the relationship between cutting parameters and machining outputs such as cutting forces and tool wear. However, these models usually cannot be generalized, due to the inherent uncertainties that exist in the process. These uncertainties may originate from machining, workpiece material composition, and measurements. A stochastic approach can be utilized to compensate for the lack of certainty in machining, particularly for tool wear evolution. The Markov Chain Monte Carlo (MCMC) method is a powerful tool for addressing uncertainties in machining parameter estimation. The Hybrid Metropolis-Gibbs algorithm has been chosen in this work to estimate the unknown parameters in a mechanistic tool wear model for end milling of difficult-to-machine alloys. The results show a good potential of the Markov Chain Monte Carlo modeling in prediction of parameters in the presence of uncertainties.Copyright

Electrically-Assisted Machining of Titanium Alloy Ti-6Al-4V and Nickel-Based Alloy IN-738: An Investigation

Despite their increasing use in leading industries, manufacture of alloys with superior mechanical properties have been a big challenge in the recent years. Researchers have been working on using assisted or augmented processes to overcome this challenge, with methods such as ultrasonically assisted, thermally assisted, vibration-assisted, magnetic field-assisted, and laser-assisted machining. Utilizing electrical assistance in manufacturing has not caught much attention due to its difficult-to-apply nature. However, it is possible to increase the ductility and machinability (through reduced flow stress) of certain metallic materials through the use of electricity. In this method, the electrical current resistively heats the material while aiding in deforming the material through the electroplastic effect.The limited amount of work in this topic is mainly focused on exploring the forming characteristics of relatively softer materials. Application of this augmentation to alloys with superior mechanical properties at elevated temperatures, on the other hand, has not been explored. This study aims to fill in that void through an investigation of applying different currents through the tool concentrated on the tool-workpiece contact zone. Both the titanium alloy Ti-6Al-4V and the nickel-based superalloy IN-738 were investigated, and the results showed that for both materials, there are two separate thresholds that need to be considered in any analysis. The first threshold is where the material starts to get deformed, below which no significant divergence from the baseline (no current) tests was observed. After exceeding this value, machining forces start decreasing with increasing current up to a certain point (second threshold) where the effect of electric current is reversed. If the second threshold is surpassed, the machining forces increase rapidly. Findings of this study can be used in assisting the machining of such materials.Copyright

Investigation of the Relationship Between Vibration Data and Tool Wear During End-Milling of Gamma-Prime Strengthened Alloys

Condition Based Maintenance (CBM) systems are crucial for today’s high accuracy machining of exotic materials. For reliable results, CBM systems need early and reliable warning based on prediction models that use multiple types of sensors. In this study, tool flank wear during end milling difficult-to-machine alloys was measured using an optical microscope. Then, vibration data collected with an accelerometer was investigated for its relationship to tool flank wear. The developed relationship between accelerometer output and tool flank wear was validated with further experiments. It was observed from frequency domain responses of these outputs that specific harmonics of the tool pass frequency were dominant, and tool flank wear can be related to the amplitude of these harmonics during machining. This way, it was shown that through accurate online prediction of tool wear, premature interruption of the process as well as machining with a worn tool can both be avoided, improving end-product quality as well as reducing machining costs.© 2015 ASME

Effect of Thermal Assistance on the Joining of Al6063 During Flow Drill Screwdriving

An increase in fuel economy standards has affected automakers’ decision toward designing lightweight vehicles and therefore transitioning from steel-based bodies to ones predominantly composed of aluminum. An introduction to lightweight materials couples that of lightweight joining with a thermo-mechanical process, Flow Drill Screwdriving (FDS). This process is favored in terms of robustness, short installation time, and only requiring access to one side. The most significant challenge of this process is reducing the material sheet separation to minimize any possibility of corrosion buildup. Warm forming of aluminum has been shown to increase ductility and formability of the material and thus the process benefits from a reduced cycle time that leads to cost reduction. In this study, the effect of an auxiliary heat source on the flow of Al6063 is investigated for the FDS application. In order to accomplish this task, a conduction-heating ring is implemented into the FDS process to raise the material temperature and thus reduce the total cycle time. Different preprocess material temperatures are studied to determine the effect of material temperature on the process time, installation torque, and sheet separation. As a result, with the thermal assistance, a reduction in the process time up to 52%, the maximum installation torque by 20%, and sheet separation by 11% were attained, indicating better quality joints at a lower cost.Copyright

Multi-Objective Particle Swarm Optimization of Machining Parameters for End Milling Titanium Alloy Ti-6AL-4V

Titanium alloy Ti-6Al-4V is a material with superior properties such as high mechanical strength, corrosion and creep resistance, and high strength-to-weight ratio, which make it an attractive material for various industries such as automotive, aerospace, power generation, and biomedical industries. However, these superior properties as well as its low thermal conductivity and chemical reactivity make it a challenge to machine Ti-6Al-4V at optimal conditions. In order to overcome this challenge, researchers constantly develop new tools and new techniques, but the extent of machining rates that can be used efficiently with those tools and techniques are usually not clear. Considering only one variable in the process and optimizing according to that variable is not sufficient because of the interactions between parameters. Also, selecting one objective function from a pool of many is not beneficial since those objectives are in conflict with one another. Therefore, this study proposes the use of a combined optimization algorithm in order to account for three major variables in end milling of Ti-6Al-4V: cutting speed, feed, and depth of cut. These variables are optimized for multiple objectives. Although it is possible to optimize the process for many different objectives, some of them are heavily correlated to each other, hence two objectives representing machinability and efficiency are selected: tool flank wear and material removal rate. The study aims to establish an optimal Pareto front of machining parameters that would optimize the conflicting outputs of the process, utilizing the multi-objective particle swarm optimization technique.© 2014 ASME