High Speed Machining for Enhancing the AZ91 Magnesium Alloy Surface Characteristics Influence and Optimisation of Machining Parameters

In this study, optimum machining parameters are evaluated for enhancing the surface roughness and hardness of AZ91 alloy using Taguchi design of experiments with Grey Relational Analysis. Dry face milling is performed using cutting conditions determined using Taguchi L9 design and Grey Relational Analysis has been used for the optimization of multiple objectives. Taguchi’s signal-to-noise ratio analysis is also performed individually for both characteristics and grey relational grade to identify the most influential machining parameter affecting them. Further, Analysis of Variance is carried to see the contribution of factors on both surface roughness and hardness. Finally, the predicted trends obtained from the signal-to-noise ratio are validated using confirmation experiments. The study showed the effectiveness of Taguchi design combined with Grey Relational Analysis for the multi-objective problems such as surface characteristics studies

An effective sensor for tool wear monitoring in face milling : acoustic emmision

Acoustic Emission (AE) has been widely used for monitoring manufacturing processes particularly those involving metal cutting. Monitoring the condition of the cutting tool in the machining process is very important since tool condition will affect the part size, quality and an unexpected tool failure may damage the tool, work-piece and sometimes the machine tool itself. AE can be effectively used for tool condition monitoring applications because the emissions from process changes like tool wear, chip formation i.e. plastic deformation, etc. can be directly related to the mechanics of the process. Also AE can very effectively respond to changes like tool fracture, tool chipping, etc. when compared to cutting force and since the frequency range is much higher than that of machine vibrations and environmental noises, a relatively uncontaminated signal can be obtained. AE signal analysis was applied for sensing tool wear in face milling operations. Cutting tests were carried out on a vertical milling machine. Tests were carried out for a given cutting condition, using single insert, two inserts (adjacent and opposite) and three inserts in the cutter. AE signal parameters like ring down count and rms voltage were measured and were correlated with flank wear values (VB max). The results of this investigation indicate that AE can be effectively used for monitoring tool wear in face milling operations.Fundação para a Ciência e a Tecnologia (FCT

Some thoughts on neural network modelling of micro-abrasion-corrosion processes

There is increasing interest in the interactions of microabrasion, involving small particles of less than 10 μm in size, with corrosion. This is because such interactions occur in many environments ranging from the offshore to health care sectors. In particular, micro-abrasion-corrosion can occur in oral processing, where the abrasive components of food interacting with the acidic environment, can lead to degradation of the surface dentine of teeth. Artificial neural networks (ANNs) are computing mechanisms based on the biological brain. They are very effective in various areas such as modelling, classification and pattern recognition. They have been successfully applied in almost all areas of engineering and many practical industrial applications. Hence, in this paper an attempt has been made to model the data obtained in microabrasion-corrosion experiments on polymer/steel couple and a ceramic/lasercarb coating couple using ANN. A multilayer perceptron (MLP) neural network is applied and the results obtained from modelling the tribocorrosion processes will be compared with those obtained from a relatively new class of neural networks namely resource allocation network

Evaluation of Effectiveness of Wavelet Based Denoising Schemes Using ANN and SVM for Bearing Condition Classification

The wavelet based denoising has proven its ability to denoise the bearing vibration signals by improving the signal-to-noise ratio (SNR) and reducing the root-mean-square error (RMSE). In this paper seven wavelet based denoising schemes have been evaluated based on the performance of the Artificial Neural Network (ANN) and the Support Vector Machine (SVM), for the bearing condition classification. The work consists of two parts, the first part in which a synthetic signal simulating the defective bearing vibration signal with Gaussian noise was subjected to these denoising schemes. The best scheme based on the SNR and the RMSE was identified. In the second part, the vibration signals collected from a customized Rolling Element Bearing (REB) test rig for four bearing conditions were subjected to these denoising schemes. Several time and frequency domain features were extracted from the denoised signals, out of which a few sensitive features were selected using the Fisher’s Criterion (FC). Extracted features were used to train and test the ANN and the SVM. The best denoising scheme identified, based on the classification performances of the ANN and the SVM, was found to be the same as the one obtained using the synthetic signal

Significance of Tribocorrosion in Biomedical Applications: Overview and Current Status

Recently, “tribocorrosion,” a research area combining the science of tribology and corrosion, has drawn attention from scientists and engineers belonging to a wide spectrum of research domains. This is due to its practical impact on daily life and also the accompanying economical burdens. It encompasses numerous applications including the offshore, space, and biomedical industry, for instance, in the case of artificial joints (Total Hip Replacement, THR) in orthopedic surgery, where implant metals are constantly exposed to tribological events (joint articulations) in the presence of corrosive solutions, that is, body fluids. Keeping the importance of this upcoming area of research in biomedical applications in mind, it was thought to consolidate the work in this area with some fundamental aspects so that a comprehensive picture of the current state of knowledge can be depicted. Complexity of tribocorrosion processes has been highlighted, as it is influenced by several parameters (mechanical and corrosion) and also due to the lack of an integrated/efficient test system. Finally a review of the recent work in the area of biotribocorrosion is provided, by focusing on orthopedic surgery and dentistry

Keyhole craniectomy in the surgical management of spontaneous intracerebral hematoma

Background and Objective: Although the surgical management of spontaneous intracerebral hematoma (SICH) is a controversial issue, it can be life saving in a deteriorating patient. Surgical techniques have varied from the open large craniotomy, burr hole and aspiration to the minimally invasive techniques like stereotactic aspiration of the SICH, endoscopic evacuation and stereotactic catheter drainage. The authors report their experience with a keyhole craniectomy for the surgical evacuation of SICH. Methods: Ninety-six cases of SICH were treated using the keyhole craniectomy technique. A small craniectomy of 2-2.5 cm diameter was made using a vertical incision over a relatively ‘silent area’ of the cortex closest to the clot. Using a small cortical incision the hematoma was evacuated and decompression was achieved. Hemostasis was achieved using standard microneurosurgical techniques. Results: Good to excellent outcome was achieved in 55 cases. Mortality was noted in 23 patients. Blood loss was minimal during the procedure. Good evacuation of the clot was seen in all but 5 cases as judged by the postoperative CT scan. Conclusion: The keyhole craniectomy technique is minimally invasive, safe and can achieve good clot evacuation with excellent hemostasis. It can be combined with microscopic or endoscopic assistance to achieve the desired result

Evaluation and Modeling of the Effect of Tool Edge Radius on Machined Surface Roughness in Turning UNS A92024-T351 Aluminum Alloy

Tool edge radius plays a significant role in affecting the surface integrity of machined products. The vast majority of existing research, however, takes no account of the effect of tool edge radius in the evaluation and modeling of machined surface roughness, an essential indicator of surface integrity. The present study fills this important research gap and has performed a total of 45 turning experiments on Unified Numbering System (UNS) A92024-T351 aluminum alloy with carefully selected cutting tools with three levels of tool edge radii. This article describes the experimental setup and measurements of tool edge radius and machined surface roughness. Machined surface roughness was evaluated using five parameters, including average roughness, root-mean-square roughness, peak roughness, maximum roughness height, and five-point average roughness. The experimental evidence presented in this article shows that the tool edge radius has a profound effect on machined surface roughness, cutting forces, and cutting vibrations. Based on the experimental data, three types of predictive models are developed, including a multiple regression model, multilayer perceptron neural network model, and radial basis function neural network model. The prediction accuracy of the three models is compared based on average mean squared errors. The results show that different models lead to different prediction accuracy for different surface roughness parameters

A New Computational Intelligence Approach to Predicting the Machined Surface Roughness in Metal Machining

Machined surface roughness is an important parameter used in the evaluation of the surface integrity of machined parts and components. This paper proposes a new computational intelligence approach to predicting the machined surface roughness in metal machining. In this approach, wavelet packet transform (WPT) is incorporated into artificial neural networks (ANN) to develop two ANN models for predicting average roughness Ra and root-mean-square roughness Rq, respectively. Each model has eight inputs, including the cutting speed, the feed rate, energy of wavelet packets for three cutting force components, and energy of wavelet packets for three cutting vibration components. Forty-five machining experiments were performed to collect relevant data to train and test the ANN models. Based on the test data, the average mean square errors (MSE) were 1.23% for predicting average roughness Ra and 2.85% for predicting root-mean-square roughness Rq. These results show that the ANN models developed from the present study have high prediction accuracy

Vibration signal analysis for monitoring tool wear in high speed turning of Ti-6Al-4V

652-660<span style="font-size:11.0pt;mso-bidi-font-size: 10.0pt;font-family:" times="" new="" roman","serif";mso-fareast-font-family:"times="" roman";="" mso-ansi-language:en-us;mso-fareast-language:en-us;mso-bidi-language:ar-sa"="" lang="EN-US">Vibration signals from metal cutting processes have been investigated for various purposes, including in-process tool wear monitoring. High speed machining is a machining process, where the speeds are at least 2-50 times greater than conventional machining. Titanium based alloys are difficult-to-machine materials which are widely used in various applications. Tool wear is a major problem in these materials because of their lower thermal conductivity and high hardness. In this context, this paper studies vibration signals acquired during high speed turning of Ti-6Al-4V, which is a widely used titanium based alloy for evaluating tool wear, mainly flank wear. Two types of inserts have been considered in the investigation namely an uncoated and a coated carbide insert. The experiments have been conducted with coolant and without coolant. The vibration signals have been subjected to wavelet transform (WT). The average energy of wavelet coefficients calculated from the vibration signals can be employed to monitor the tool wear in both the types of inserts investigated.</span

Surface Roughness Analysis in High Speed Turning of Ti-6Al-4V Using Coated Carbide Inserts: Experimental and Modeling Studies

Machining and achieving good surface finish in Ti-6Al-4V is a challenging task. Several factors influence surface finish of which some can be controlled and are mainly the machining parameters and some are considered uncontrolled and happens to be the output of the machining process like tool wear and cutting tool vibrations. Several researchers have made attempts to identify these parameters and study both experimentally and through modeling. This study deals with the experimental correlation between machining parameters namely cutting speed, feed rate, depth of cut and uncontrollable parameters namely tool flank wear and cutting tool vibrations on two surface roughness parameters Ra and Rt, while turning Ti-6Al-4V using coated carbide inserts. Further, in order to study the effect of individual parameters statistically on the responses, a widely used statistical technique namely Response Surface Methodology (RSM) and a new technique, Random Forest Regression (RFR) have been applied to these experimental data to model and predict the values of surface roughness parameters. Results revealed that RFR performed better than RSM. It has been found that tool wear is the most dominant parameter affecting surface roughness parameters followed by feed rate and cutting tool vibrations have a direct correlation with surface roughness parameters