Search

The temperature rise and the quantity of energy transferred to the workpiece were estimated in the grinding of bioceramic (pre-sintered zirconia) material with a diamond wheel based on the modeling and experimental method. The finite volume method has been used to investigate the temperature distribution under different environments such as dry, wet, and MQL. The power generated from the experimental method was given as input data to the thermal model analysis. The tribology of the grinding process was investigated depending on different factors such as temperature, energy partition, surface morphology, coefficient of friction, and surface energy. The rise in temperature and the quantity of heat transferred to the workpiece were estimated depending on the analysis of the inverse heat transfer method. The results indicated that the maximum energy partition assessed in the dry condition was around (39–60%) to the workpiece material, which differs from the wet condition as it was reduced to (15–40%). The energy partition for the MQL condition was close to the dry condition as (33–58%). The diamond wheel consumes more quantity of heat energy at the contact interface and reduces the temperature of the workpiece surface. The predicted temperature using the thermal model analysis was acceptable with the experimental method. After the thermal analysis, the coefficient of friction at the contact interface was calculated which was observed to be high for both the dry and the wet conditions compared to the MQL. Additionally, the quantity of surface energy consumed by the ploughed friction was also augmented for the dry and wet conditions. Evidently, it leads to more deformation for the work material under these conditions. Finally, the interaction between the sharp edges and the workpiece surface was reduced in the MQL condition.

Deep hole drilling process is extensively used in demanding applications, namely plastic injection moulding, oil and gas industry and automobile engines. This drilling process encounters problems such as improper chip evacuation, poor surface finish, roundness variation, and high tool wear, which affects the hole quality. To overcome these limitations, ultrasonic vibration-assisted deep hole drilling (UVADD) is performed in this work. The high-frequency vibrations were imparted into the ASTM A36 steel (workpiece) to induce reciprocating motion, which is mounted over a transducer. A special fixture arrangement was fabricated to hold the transducer in the drill bed. This helps to transfer the vibration to the workpiece. The machining performance of UVADD was analyzed by means of cutting force, tool wear, torque, hole quality, surface roughness, machining time and chip morphology, and the performances were also compared with the conventional deep hole drilling (CDD) process. Results suggested that a reduction in cutting force and torque was observed for UVADD. This is due to small discontinuous chips generated. Hole quality showed a drastic decrease in burr formation and surface roughness along with a uniform radius around the periphery. From the tool wear examination, it was also evident that no built-up edge formation occurred along the cutting edge owing to effective penetration of coolant and reduction in cutting temperature. This comprehensive analysis revealed that UVADD provided an enhancement in machining performance as compared to CDD.

Circularity is one of the frequently observed features in manufacturing industry. Most of the components manufactured are unions of the fundamental shapes, one of which is the circle. Due to very close tolerances, micro and nano-sized components’ manufacturing serves a very challenging task. Slight variance in the manufactured component can lead to incorrect assembly and inefficient system performance. Hence, the roundness evaluation of any circular component is important. The present article proposes a novel algorithm to evaluate the roundness error based on the minimum zone circle (MZC) concept. The proposed algorithm is an iterative field search technique. Compared to the existing approaches, the proposed method is very effective in determining the roundness error. Furthermore, it also performed well both on uniformly and non-uniformly spaced data. The results show that it is simple, robust and flexible enough to vary the search field area for better computational efficiency.

AA2024-T351 alloys are widely used in aerospace and automotive industries due to their high strength to weight ratio and ductility. The present paper investigates the machinability of AA2024-T351 material with uncoated carbide tools. Orthogonal machining experiments were performed at varying cutting speeds and feed rates with a constant depth of cut. Machinability studies were done using cutting forces, coefficient of friction and chip morphology. As the feed rate increased both cutting force and feed forces were found to be increased whereas the coefficient of friction decreased. Chip formation process was significantly influenced by the operating conditions. In addition, the shear angle and contact length were determined analytically.

In surface grinding process, the insufficient coolant flow causes the accumulation of dislodged abrasives and chips in the grinding interfaces. It leads to poor performance by increased work-surface scratches and reduced material removal rate (MRR). This can be improved by enhancing the coolant flow in the grinding interfaces by creating grooves on grinding wheels surfaces. In this work, a vertical surface grinding process is employed to emphasize the influence of surface integrity of the work-piece with spiral grooved SiC grinding wheel surfaces. The grinding performance (force, power, MRR, wheel wear and G-ratio) and the surface characteristics (avg. roughness, peak-valley height and pitch value) were investigated by varying the process parameters (i.e., depth of cut = 16, 24, 32 and 40 μm and feed rate = 7 and 14 m/min at a constant speed of 1500 rpm). At first, the spiral grooves of four different patterns (Archimedes, Logarithmic, Fermat=t2 and radius=√t) were designed on wheel surfaces and investigated for the stress distribution with the centrifugal force (157 rad/s) by FEM analysis using ANSYS. It reveals that the stress-distribution was larger than the un-grooved wheel surface, irrespective of groove patterns. However, the stress-distribution was found to be lower in Archimedes spiral groove patterns compared to other patterns. Therefore, the case study on Archimedes spiral groove patterns on grinding wheels were performed and compared with the un-grooved wheels.

Microfluidics, emerging system of fluid-handling technology by using micro-channels are being applied to diverse fields due to everyday demand. To overcome high volume market needs; low cost and fast processing methods are necessary to fabricate microfluidic devices. There are many ways to produce the necessary channel and feature dimensions using photolithography to milling. This study focus on the fabrication of narrow cylindrical channels having a dimension of microns (width and depth) in a poly(methyl methacrylate) substrate by a thermal softening process using the Joule heating effect of nichrome wires. The channel fabrication tool is designed and developed to generate localized heat on the substrate with a high repetition rate by using the moving hot nichrome wire as a function of feed rate (600-9000 mm/min) in a CNC machine. 3D optical profilometer study reveals that the channels are cylindrical and smooth. By increasing the feed rate, the width and depth of the microchannels are controlled from 230-80 μm, and 60 to 17 μm, respectively. These results suggest that the thermal softening process using a nichrome wire is a promising tool for the fabrication of micro-channels on flexible polymeric substrates.

The performance of electrical discharge machining (EDM) primarily depends on the spark quality generated in the inter-electrode gap (IEG) between the tool and workpiece. A method for obtaining accu...

The effect of graphene nanoplatelets (GNPS) on the grindability of GNP reinforced ZrB2 was studied using a resin bonded diamond grinding wheel under dry and wet conditions. A comparative study of grinding forces was performed at selected wheel surface speeds and depths of cut for surface grinding. ZrB2-GNP showed lower normal grinding forces due to the improved fracture toughness and reduced hardness. The presence of GNP reinforcement in ZrB2 resulted in lower tangential forces and reduced specific grinding energy due to the role of GNP as a solid lubricant. The measured forces showed a good correlation with the micro cutting model for ZrB2 and ZrB2-GNP under dry conditions. The tangential forces showed the same trend as normal forces at different depths of cut and wheel surface speeds for ZrB2 and ZrB2-GNP with average force ratios of 0.3 and 0.35, respectively. The presence of porosity in ZrB2 increased the normal grinding forces during wet grinding. Scanning electron microscope (SEM) images of the grinding chips indicated a mixture of both the ductile mode and the brittle mode of material removal with predominantly brittle fractured chips. Energy-dispersive spectroscopy (EDS) confirmed the presence of GNPs in ZrB2-GNP grinding chips. The topography of the grinding wheel showed higher wheel loading after the dry grinding than that of wet grinding. The wet grinding resulted in relatively lower surface roughness (Ra values) than dry grinding.

Carbon fiber reinforced polymer (CFRP) based structures are widely used in aerospace and automobile application due to their excellent strength to weight ratio. Making a product using this CFRP component along with other components requires a hole to be drilled for assembly requirements. The holes drilled in CFRP leads to delamination at entry and exit. These delaminations occur due to improper selection of cutting conditions, drill tool geometry and wear. For delamination free drilling without compromising on productivity requires monitoring the process. In this work acoustic emission signals collected during the drilling process used to evaluate the extent of delamination and tool wear. The time, frequency domain and information based similarity approaches were used to evaluate the information content of the AE signals. The evaluated parameters clearly distinguish the delamination and the tool wear that occurred during the process. This approach can be easily adapted for online continuous monitoring of the process to assess the extent of damages for defect-free drilling of composites for various applications.

Summary Background A cluster of seven cases of skin and wound infections caused by a multiply resistant meticillin-resistant Staphylococcus aureus (MRSA) were detected in a small-town community in South Yorkshire. Initial microbiological investigations showed that all isolates belonged to a spa type observed rarely in England (t1476). Aim To describe the epidemiology of t1476 MRSA in South Yorkshire. Methods Retrospective and prospective case ascertainment was promoted through communication with local microbiology laboratories. Public health investigation included a detailed review of clinical notes for a subset of nine cases. Genomic and phylogenetic analysis was undertaken on t1476 MRSA. Findings Thirty-two cases of t1476 MRSA infection or colonization were identified between December 2014 and February 2018. Cases were older adults (aged 50–98 years). Healthcare exposures for a subset of nine cases indicated frequent contact with a team of district nurses, with all but one case receiving treatment on the same day as another case prior to their own diagnosis. No cases were admitted to hospital at the time of specimen collection. Despite detailed investigations, no carriers were detected among district nursing staff. A long-term carrier/super-shedder was not found. Phylogenetic analysis indicated that t1476 MRSA cases from South Yorkshire were monophyletic and distant from both MRSA of the same lineage from elsewhere in the UK (N = 15) and from publicly available sequences from Tanzania. Conclusion Genomic and epidemiological analyses indicate community-based transmission of a multiply resistant MRSA clone within South Yorkshire introduced around 2012–2013, prior to the detection of a spatial–temporal cluster associated with a distinct risk group. Surveillance data indicate continued circulation.

Machining of Al-SiC metal matrix composite (MMC) is still being a challenging task in the automotive and aerospace sector due to its frequent tool wear while processing this MMC. The hard Polycrystalline diamond tools (PCD) are used as a cutting tool in many sectors for machining of MMC. But the usage of PCD is commercially not viable due to its high cost involved in the fabrication. In this regard, low-cost diamond-coated tungsten carbide (WC-Co) tools are considered as an alternative to PCD. However, the diffusion of cobalt from WC-Co during diamond deposition promotes higher graphitization at the diamond-carbide interface. This induces coating delamination during MMC machining. This outward diffusion of cobalt can be hindered by doping boron in the diamond lattice to enhance adhesion strength of the coating. Hence in this work, the boron doped graded layer diamond coating (BDD/transition layer/NCD) on WC-Co was proposed to improve the machining performance of MMC. In this work, four different CVD diamond coated WC-Co tools such as microcrystalline diamond coating (MCD), nano-crystalline diamond coating (NCD), boron-doped diamond coating (BDD) were considered for comparison along with the proposed tools. The different phases (sp3 and sp2) in the diamond coating were analyzed through cross-sectional Raman mapping technique. The Rockwell indentation test confirms that the better adhesion strength obtained in BMTN coated tools. The wear behavior of these diamond coated tools was tested by machining of AA2124/25%SiCp material. In addition, performances of these tools were compared with widely accepted industrial grade PCD tools. The temperature at the cutting zone, chip morphology and surface roughness of the machined component were studied through an appropriate test method. The occurrence of edge chipping while machining was continuously monitored through acoustic emission (AE) signals. The machining results show that BMTN coated tool exhibits least tool wear (VB = 0.25 mm) in comparison with MCD (VB=0.47 mm), NCD (VB=0.57 mm), BDD (VB= 0.33 mm) coated tools and PCD (VB = 0.29 mm) tools respectively. In addition, the results of AE signals show that MCD, NCD coated tools and PCD tools undergone edge chipping whereas there was no edge chipping observed in BDD and BMTN tools. Thus Nano –Engineered BMTN tools observed to be a preferred cutting tool for efficient machining of Al-SiC MMC material.

Breast cancer is the world's second-leading cause of cancer death among women. It is the most frequent type of invasive cancer in women and the second biggest cause of mortality in the United States. Breast cancer is a disease in which the breast cells develop out of control. Many studies have been conducted in this area utilizing a machine learning technique, however some of them did not explore how to cope with imbalanced classes. The breast cancer dataset is an unbalanced dataset in which the total frequencies of benign class is larger than the total frequencies of malignant class. The problem with an unbalanced dataset is that it provides a high level of accuracy just by predicting the majority class, but fails to capture the minority class, which is usually the point of creating the model in the first place. Imbalanced classification approaches were employed in this study to predict breast cancer. The accuracy of prediction for multiple machine learning algorithms was determined utilizing imbalanced methodologies such as synthetic minority oversampling technique, random oversampling, and random under sampling technique respectively.

Texturing of surfaces is often done to improve the surface properties. The purpose of this study is to develop a methodology for two dimensional inspection of the quality of textured surfaces automatically using machine vision techniques. The quality is measured in an absolute sense, using the value of offset between ideal shapes that inscribe and circumscribe the boundary of a machined feature. This gives an idea about machining accuracy rather than similarity between the machined texture shape and intended ideal shape which most of the existing methods try to measure. The techniques developed are tested on textures created on polished titanium surface using laser micro machining technique. In this study the textured surfaces made up of four different shapes namely circle, ellipse, triangle and square are considered. The automatic classification of texture shapes into the mentioned categories is achieved by finding their correlation with ideal shapes. For the purpose of measuring the machining accuracy, this paper extends the ISO definition of circularity error to ellipse, triangle and square shapes and proposes a method to fit least square triangle and square shapes to a given two dimensional point clouds. The error and the average dimension of the features are calculated from circumscribing, inscribing and least square fit shapes. The results generated by the proposed algorithms are found to be in good agreement with the known dimension values set during machining of the samples and manual measurements done using a stereo-microscope images. This indicate the suitability of this procedure for characterization of textured surfaces for different applications.

Stamping tools are prone to an adhesive wear mode called galling. Adhesive wear on stamping tools can degrade the product quality and can affect the mass production. Even a small improvement in the maintenance process is beneficial for the stamping industry. Therefore, this study will focus on understanding and detecting the initiation of tool wear at the microscopic level in sheet metal stamping using acoustic emission sensors. Stamping tests were performed using a semi-industrial stamping process, which can perform clamping, piercing, stamping and trimming in a single cycle. The stamping test was performed using a high strength low alloy sheet steel and D2 tool steel for dry and lubricated conditions. The acoustic emission signal was recorded for each stamped part until severe wear on the dies was observed. These acoustic emission signals were later analyzed using time and frequency domain features. The time domain features such as peak, RMS, kurtosis and skewness could identify significant changes in the acoustic emission signal only when the severe wear was observed on the stamped parts for both dry and lubricated conditions. However, this study has identified that a frequency feature – known as mean-frequency estimate – could identify early stages of wear initiation at the microscopic level. Evidence of this early stage of wear on the part surfaces was not clearly visible to the naked eye, and could only be clearly observed via surface measurement instruments such as an optical profilometer. The sidewalls of the stamped parts corresponding to the initial change in AE mean-frequency trend were qualitatively correlated with 3D profilometer scans of the stamped parts, to show that AE mean-frequency can indicate the initial minor scratches on the sidewalls of the stamped parts due to the galling wear on the die radii surfaces. The results from this study can be used to develop a methodology to determine the very early stages of stamping tool wear, providing a strong basis for condition monitoring in the stamping industry.

Galling wear in sheet metal stamping processes can degrade the product quality and adversely affect the mass production. Studies have shown that acoustic emissions sensors can be used to measure galling. However, the link between the features of the acoustic emissions signal and galling during complex manufacturing processes, such as sheet metal stamping, are not well understood. In the first section of this paper, it is shown that time domain features of the acoustic emission signal, such as RMS and peak, can be used to identify when the tooling is unworn or severely worn. These results were correlated with the wear measurement of the maximum depth of the surface profile of the stamped part. In the second section of the paper, time-frequency techniques were used to understand the characteristics of the AE signal associated with wear of the sheet metal stamping dies. The results show that it is possible to identify changes in acoustic emission signal using time-frequency techniques much prior to the observation of major changes in the time domain features and visual observation of wear on parts. In the third section of the paper, a new acoustic emission feature known as mean frequency estimate is proposed for the condition monitoring of the stamping wear. This mean frequency estimate feature showed a clear shift in the acoustic emission signal much before the observation of severe wear. The methodology used in this paper to study wear progression can lay the basis for real-time monitoring of tool wear in the stamping industry.

Pre-sintered zirconia is preferred as a restoration material in dental applications due to its excellent strength and fracture toughness. When abrasive processes were used to obtain the required shape of (Y-TZP) yttria-stabilized tetragonal pre-sintered zirconia, it resulted in material strength degradation in the presence of coolant. Therefore, experiments were carried out on pre-sintered zirconia with diamond grinding wheel to evaluate the performance of cooling conditions such as dry, wet and minimum quantity lubrication (MQL). The effects of different environments on the grinding performance were studied based on the temperature distribution, phase transformation, flexural strength, microhardness and edge chipping damage. The Raman spectroscopy and X-ray diffraction analysis were used to estimate the quantity of monoclinic phase in pre-sintered zirconia. The temperature rise of the workpiece material during the grinding experiment was not higher and insufficient to cause the thermal stresses. The microstructural changes induced by grinding under different cooling strategies were associated with the quantitative assessment of monoclinic phase. The flexural strength of ground components was improved in the dry condition compared to the other process due to the absence of the defective layer and the occurrence of Y3+ ions segregation. After grinding, there was a slight decrease in the hardness value by (1–8 HV), which was due to the formation of microcracks in the subsurface layer of the ground surface. In addition, to ensure the presence of microcracks, the edge chipping depth was measured. The damage depth obtained from the wet condition showed a higher value of 30 µm compared to the dry and MQL conditions.

After stress corrosion crack testing in a MgCl2 environment, turned surface of super duplex stainless steel showed four types of cracks. The residual stress and surface defects such as feed marks and long grooves generated after machining played a significant role in the direction and orientation of these cracks. The generated cluster of cracks acted as a precursor to the formation of pits. The biaxial tensile stress in the surface promoted successive branching of cracks, which led to high crack density. The subsurface results showed that cracks passing perpendicular to feed marks found more detrimental than the other types.

Composite electrode fabrication is an effective strategy to improve the performance of the Electric Discharge Machining (EDM) process. In this work, a new copper/carbon nanotube (Cu/CNT) metal matrix composite electrode at a different volume fraction of CNT (0.35%, 0.70% and 1.05%) is developed using a spark plasma sintering (SPS) method. The real-time functional performance of the developed electrode on the EDM machining is also examined by changing the EDM process parameters (discharge current, pulse-on time and voltage). The composite electrode characterization results indicated that as the percentage of CNT increases from 0.35 to 1.05 vol%, the density reduced and electrical resistivity increased due to the rise in porosity. Performance review of CNT-infused electrodes during EDM machining revealed a higher Material removal rate (MRR) and good surface finish than the Cu electrodes due to high discharge energy and enlargement in the spark gap. However, Cu/CNT electrodes wore out faster than Cu electrodes by 90%, possibly due to the reduction in thermal conductivity induced by porosity effects. Comprehensively, although the addition of CNT affected the mechanical attributes of the electrode, it offered significantly better output (MRR and Surface finish) than the Cu electrode with limitations on electrode wear.

Die sinking electrical discharge machining is used extensively to produce very complex geometries on difficult-to-machine materials for aerospace, automotive, mold and die applications. The quality of the component produced mainly depends on the quality and intensity of spark generated between the tool and the workpiece. The present research work focused on using the acoustic emission signals to characterize the spark activity in the EDM process under different machining conditions. Experiments were conducted by varying the machining conditions and using three tool electrode materials namely graphite, copper and copper tungsten. The acoustic emission signal features were extracted from the data collected from different experimental conditions and are related to the various parameters including current, voltage, pulse on-time, surface roughness parameters (Ra and Rq) of the workpiece and material removal rate (MRR). The effect of tool material on the AE phenomenon was also investigated. The process conditions were found to have to have a good correlation with the acoustic emission signals features. Multiple linear regression models were built to predict the surface roughness (Ra) of the workpiece and MRR using only the current and pulse on-time as predictor variables. The prediction accuracy of the models improved by the addition of AE signal features to the predictor variable set. The results obtained indicate a suitability of employing acoustic emission signals for monitoring the spark activity, MRR and surface finish of the workpiece in the EDM process. Based on this, a reliable monitoring and diagnosis tool for EDM can be developed using the AE sensor and provides an opportunity to implement the industry 4.0 initiatives effectively.

Grinding is an expensive and complex machining process, characterized by cutting grits undergoing non-uniform wear. The worn out grits influence the surface finish of the part, necessitating timely dressing. Conventionally, the dressing interval is decided either based on the wheel life end criteria viz. visual identification of the workpiece burn mark, chatter occurrence and deterioration in the part finish or on the number of parts produced. Improper dressing interval increases auxiliary machining time and grinding wheel wastage. Prevailing demands towards next generation smart manufacturing include product and process related benefits such as low operational cost, better customer service support, operation optimization and control. In the present work, we propose a low cost, process non-intrusive sensor technology with IoT enabled operational intelligence platform to estimate the redress life of grinding wheel based on wheel condition. Traverse grinding tests were carried out in a CNC surface grinding machine installed with Al2O3 wheel against D2 tool steel under wet condition. During experimentation, the spindle motor current, grinding forces and grinding wheel surface images were acquired using the Hall-effect sensor, dynamometer, and CCD camera respectively. Data acquisition, network connectivity, and cloud communication were empowered by serial output. Statistical time, frequency and wavelet domain features signifying the wheel life characteristic were extracted. To show the usefulness of motor current signals, the extracted features thereof were confirmed against grinding forces and wheel surface images. A time series Auto-Regressive Moving Average (ARMA) predictive model was developed to estimate the grinding wheel redress life using the selected root mean square (RMS) feature of a current signal. An android application was also developed for a graphical visualization of dressing time based on the RMS value of the spindle motor current signal. The developed methodology thus, allows operators and machines with sensors to communicate with each other and facilitates real-time traceability, visibility and control over the dressing action to perform automatic dressing before the wheel reaches its end of life.

Aluminum based metal matrix composites (Al-SiC) are widely used in automotive and aerospace industries due to its high strength to weight ratio, high wear resistance, and higher thermal conductivity. Though it has lots of commercial applications, machining of this MMC still a challenging task due to its hard SiC reinforcement in the soft aluminum material. These two phases cause adhesive and abrasive wear on the cutting tool. Hence tooling with a property of combined hardness and toughness required to machine this material effectively. In this connection, CVD diamond on WC-Co is considered as the suitable cutting tool for the precise machining of this MMC material. Even though it is commercially available, the usage is limited due to the poor interfacial adhesion of CVD diamond on WC-Co leads to coating delamination. In this work, the boron doped diamond (BDD) and boron doped graded layer diamond coating (BMTN) on WC-Co were considered. Machining experiments were conducted on a lathe using AA2124/SiCp MMC material to evaluate the wear performance of the BMTN, BDD, MCD (Microcrystalline diamond coating) and NCD (Nanocrystalline diamond coating) coated cutting inserts. The tool wear results indicate that the BMTN outperformed in comparison with MCD and NCD coated tool inserts.

Grinding characterized by low material removal rate and high surface finish is an abrasive machining process. Geometrically undefined grits of grinding wheel influences part surface finish. Cutting edges worn out during grinding are retained by timely dressing. Operators on shop floor decides on the dressing interval based on end of wheel life such as burns, chatter marks and deterioration in the workpiece surface finish. Incorrect dressing of grinding wheel increases unnecessary machining time and wheel wastage. The present work defines the usefulness of grinding force signal and ground surface texture analysis in the estimation of grinding wheel redress time. Grinding experiments were performed in surface grinding machine with white Alumina wheel on D2 tool steel specimen. During the experiments, the force signals were acquired from 9257B Kistler dynamometer, for each grinding pass with 16 KHz sampling frequency. Ground workpiece surface images were captured using multi-sensor Coordinate Measuring Machine (CMM). Experiments were continued till the grinding wheel reaches its end of life. Grinding force signal features prominent to the assessment of grinding wheel deterioration in time, frequency and time-frequency domains were extracted based on prognostic metric evaluation. Hough transform based image texture features were also extracted. The test results depict that a good correlation exists between grinding force signals and ground surface images in measuring the wear deterioration level and thus the ground surface features can be considered as an explicit criterion in the redress life estimation of the grinding wheel.