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DC 53 steel has emerged as a possible replacement of AISI D2 steel possessing competitive hardness and better toughness. In the current work, turning of DC 53 steel was conducted via Xcel modified inserts by varying workpiece hardness levels (40 and 60 HRC), cutting speed (130 and 160 m/min), feed rate (0.07 and 0.112 mm/rev), and depth of cut (0.07 and 0.17 mm). A two-level 4-factor full factorial design was employed entailing 16 runs. An analysis of variance (ANOVA) was conducted to statistically analyze the effect and contributions of input parameters on response variables namely tool life, surface roughness, volume of material removed, power consumption, and machining zone temperature. Results show that the tool life, surface roughness, volume of material removed, and machining zone temperature are primarily affected by the hardness of DC53 with PCRs of ~ 96%, ~ 25%, ~ 62%, and ~ 25%, respectively. At a 40 HRC hardness value, true crater wear was observed due to continuous chips sliding at the rake face while for the workpiece having a 60 HRC, discontinuous chip formation produced less prominent crater wear. SEM images revealed complete delamination of the coating from the tool surface with adhesion and attrition wear identified as the main wear mechanisms. The formation of a groove pattern was also noticed on the flank face. The minimum surface roughness was 0.90 µm-Ra for the workpiece having a 40 HRC hardness level, and the same value was obtained for 60 HRC as well. The threshold value of the feed rate for the excellent performance of these inserts was less than 0.20 mm/rev. Additionally, the turning process proved to be productive for this material along with a lower surface roughness value in comparison to the wire EDM process.

This work reports on the performance of eco-friendly spray-based approach employing vegetable oils to precision machine nickel based superalloy Inconel 718. Taguchi L9 (33) orthogonal array was used to evaluate two vegetable oils namely sunflower and castor oil for turning operation and their performance was compared with dry machining mode. Three levels of cutting speed (30, 50, 70 m/min.), feed rate (0.168, 0.281, 0.393 mm/rev.) and air pressure (2, 3.5, 5 bar) were used while keeping constant depth of cut (1) mm and spray standoff settings (50 mm). Tool wear, surface roughness and machined surface integrity were taken as response parameters. Tool wear analysis was further supplemented with scanning electron microscopy (SEM). The effects of the input variables on response parameters were quantified by analysis of variance (ANOVA). It was found that feed rate is the most contributing factor for surface roughness with a contribution of 95.71%, 97.86% and 93.19% for MQLSO, MQLCO and dry machining conditions respectively. As for the tool wear, cutting speed is the most contributing factor with a contribution of 76.55%, 80.87% and 69% for MQLSO, MQLCO and dry machining conditions respectively. Minimum tool wear was obtained with sunflower oil with lowest feed rate (0.168 mm/rev.) and cutting speed (30 m/min.) combination. Further insight on worst and least tool wear conditions via SEM images indicate grooving, incidences of adhered material and attrition/chipping to be observable for dry machining condition. On the other hand, lesser incidences of adhered chips and uniform wear which extended up-to the tool nose was observed for least tool wear condition obtained with MQLSO indicting its effectiveness. The minimum values for tool wear (116 μm) and surface roughness (0.43 μm) as obtained with MQLSO, were 26% and 52.09% better (respectively) when compared to the dry machining at same conditions. In the context of surface integrity, a strain hardened layer extending up to the depth of 270 μm was observed at optimum machining parameters. Approximately, 16% higher hardness value was observed closer to the machined surface, than that of bulk material.

A review of the available literature on powder mixed electrical discharge machining (PMEDM) indicates that most of the research has been done for “die sinking machining mode” whereas the “wire cut machining mode” has not received due attention despite being an important process variant. This work employs silicon carbide (SiC) powder mixed dielectric fluid for machining of AISI D2 in “wire cut” mode with re-circulating molybdenum wire (an economic and chemically stable proposition as a tool). The effect of five process parameters (powder concentration, peak current, pulse on time, nozzle flushing pressure and stand-off distance) has been evaluated on surface roughness, kerf width, material removal rate, and wire wear ratio using Taguchi’s approach followed by analysis of variance (ANOVA) for determining statistically significant factors at 95% confidence level. It is found that for surface roughness, higher current and low to moderate concentration levels (2 to 4g/l) deteriorate surface quality; higher values of pressure and stand-off distance are also seen to adversely affect it. For material removal rate, pulse on time as well as its interactions with powder concentration and current, are statistically significant. A higher pulse on with smaller and moderate powder concentrations (2g/l and 4g/l) reduces MRR. For the wire wear ratio, the current is the sole significant factor (PCR of ~65%). SEM analysis of the machined workpiece for the maximum MRR condition quantifies the recast layer as ~19microns. An indirect comparison with the reported values for non-powdered EDM process indicates that for the similar wire (molybdenum), the use of SiC powder maintains the surface roughness and kerf values, for a much harder D2 material used in this work.

With the tremendous growth of Internet and its related technologies, the Service Oriented Architecture (SOA) became a dominant paradigm shift for enterprise computing. In SOA, business functionalities are offered by many different Service Providers as services. In order to get served by different service providers, the client has to authenticate with those service providers at multiple times. Single Sign On (SSO) mechanism provides the client to login only one time so that access to different services is made possible without needing to re-authenticate. Here, the identity of the logged-in client is federated among the enterprise computing nodes. This is one of the simplest forms of federated identity. The goal of identity federation is to benefit ease of use, flexibility, productivity and reduced cost of the authentication process, but trust and security is a major concern in this situation. Major threats on federated identity management are due to identity misuse, identity theft, and trust deficit between identity providers and services providers. As of now, the Security Assertion Markup Language (SAML), Open Authorization (OAuth) and OpenID are the three important federated identity management standards in the industry. However, none of them is equipped by itself to provide comprehensive security protection for identity federation even within a single enterprise computing environment. In fact, these federated solutions result in additional security vulnerabilities due to their openness of identity federation. The security threats are becoming severe when federated identity is spanned into the inter-organizational and intra-organizational computing environment. This paper analyses the vulnerabilities and security gaps in the existing federated identity solutions. To overcome these gaps, an adaptive security architectural model is proposed for identity federation at inter and intra-organizational level using public key infrastructure that adheres to the SOA security standards and specifications. The proposed architecture is implemented and tested in a large-scale federated identity enterprise computing environment with security-centric financial data to acquire the desired results. A cross-sectional comparative analysis is done between existing and proposed solutions to validate the improvement in the protection of identity federation environment.

Object detection plays a vital role in the fields of computer vision, machine learning, and artificial intelligence applications (such as FUSE-AI (E-healthcare MRI scan), face detection, people counting, and vehicle detection) to identify good and defective food products. In the field of artificial intelligence, target detection has been at its peak, but when it comes to detecting multiple targets in a single image or video file, there are indeed challenges. This article focuses on the improved K-nearest neighbor (MK-NN) algorithm for electronic medical care to realize intelligent medical services and applications. We introduced modifications to improve the efficiency of MK-NN, and a comparative analysis was performed to determine the best fuse target detection algorithm based on robustness, accuracy, and computational time. The comparative analysis is performed using four algorithms, namely, MK-NN, traditional K-NN, convolutional neural network, and backpropagation. Experimental results show that the improved K-NN algorithm is the best model in terms of robustness, accuracy, and computational time.

Municipal solid waste (MSW) management is an essential element of present-day society. The proper storage and disposal of solid waste is critical to public health, safety, and environmental performance. The direct recovery of MSW into useful energy is a critical task. In addition, the demand for conventional power supplies is high. As a strategy to solve these two problems, the technology to directly convert municipal solid waste into conventional energy to replace fossil fuels has been obtained. The hydrothermal carbonization (HTC) process is a thermochemical conversion process that utilizes heat to convert wet biomass feedstocks into hydrochar. Hydrochar with premium gasoline properties is used for fuel combustion for strength. The properties of fuel hydrochar, including C char (carbon content), HHV (higher heating value), and yield, are mainly based on the properties of the MSW. This study aimed to predict the properties of fuel hydrochar using a machine learning (ML) model. We employed an ensemble support vector machine (E-SVM) as the classifier, which was combined with the slime mode algorithm (SMA) for optimization and developed based on 281 data points. The model was primarily trained and tested on a fusion of three datasets: sewage sludge, leftovers, and cow dung. The proposed ESVM_SMA model achieved an excellent overall performance with an average R2 of 0.94 and RMSE of 2.62.

Model-driven engineering (MDE) paradigm considers models as central artifacts for software development lifecycle during which models evolve. Developing an e-health solution using MDE poses challenges of model version control, model differencing and model merging, which requires appropriate software configuration management (SCM). In this paper we focus on model-driven merging, which refers to combining two or more versions of a model into a single consolidated version. SCM for model-driven merging leverages evolution of valid configurations, which is a highly desired behavior. Our investigation is based on the features that are required for model-driven SCM realization. Initially, we identify these features using which the existing model-driven merging techniques are evaluated. It is observed that though various proposals are made by academia and research community, a standard model-driven SCM solution that can cater to the needs of industry is still absent. This is in contrary to the situation of traditional SCM systems where standard solutions exist. We also present the usefulness of each technique along with the tradeoffs involved. Finally, guidelines are provided to select techniques appropriate for given circumstances.

Skin cancer is measured as one of the fatal types of cancer diseases in humans, among numerous kinds of malignancy. Current diagnostic classifications are lacking in finding an effective treatment. The effective and early stage treatment of skin disease can increase the survival rate of patients. Substantial investigative work has been developed to improve computer aided diagnosis system to detect cancer at early stage. However, early detection of skin cancer still requires better accuracy through experiment on digital skin lesion images as a multiclass classification, rather than using biopsy methods. This paper presents an intelligent framework to detect and classify four types of skin cancer. Before classification, noise removal from skin lesion is performed by gaussian filter. Textural and colour features are extracted from skin lesion to detect and classify cancer into four types. Support vector Machine is trained to classify Melanoma, Nevus, Basal and Squamous skin cancer types. Extensive experiments are performed on standard benchmark skin cancer images dataset with an improvement in accuracy of 92.41% after comparison with the well-known methods.

Ni-based superalloy Inconel 625, despite its extensive applications (in aerospace, oil and gas, marine, chemical processing and alike industry) is categorized as difficult-to-cut material due to distinct thermal and physical characteristics. Incidentally, the use of novel multi radii insert (wiper) geometry, that is reported to provide extended machining capabilities for different materials and machining scenarios has not been found to be reported for this alloy. This work employs PVD coated carbide inserts with novel wiper edge geometry for face milling of Inconel 625 under dry cutting conditions. Cutting speed, feed per tooth and axial depth of cut have been taken as input variables and machining performance is evaluated in terms of tool life, tool wear analysis, material removed and surface integrity aspects (namely roughness and microhardness). Taguchi L8 array has been employed for experimentation phase followed by post experimental analysis. It is found that for tool life, axial depth of cut is the most significant factor with contribution of 45.43%. Maximum tool life of 42.8 min was achieved when machining was done employing lower values of feed (0.08 mm/tooth) and axial depth of cut (0.25 mm) with higher value of cutting speed (45 m/min). For the case of surface roughness, feed/tooth is found to be the most contributing factor (PCR 46.25%). Results are found to be correlating well with the cutting temperatures generated during the process. Experiments with minimum axial depth of cuts resulted in lesser temperatures and better output parameters in general. Review of the wear pattern via SEM analysis indicate adhesion, BUE, attrition and chipping to be the main wear mechanism in general except where higher axial depth of cut (0.5 mm) was employed that culminated in fracture. For the conditions where maximum tool life was obtained, a work-hardened layer was observed beneath the machined surface extending up to ∼400 μm depth. An indirect comparison of the results with other literature reported face milling scenarios of Inconel 625 (where other tool types are employed), seems to indicate the effectiveness of wiper inserts employed herein. The results are well explained and supported by the physical phenomenon involved.

Osteoporosis is a worldwide disease depicted by the reduced bone mass, an adequate supply of minerals are needed to support bone remodelingand their deficiency causes bone-related diseases, osteoporosis in particular, and have osteo-protective effects. The aim of recent researchwas to quantify the micro (Mn, Fe, Cu, and Zn) and macroelements (Mg, K, and Ca) in the peels powder of some common fruits (pomegranate, orange, lemon, mango, and grapefruit) by flame atomic absorption spectrometer (FAAS). The extraction of micro and macroelements in peels powder was done by using dilute acids in ultrasonic bath. Apple leaves were used as standard reference material (SRM, NIST 1515) to optimize the ultrasound assisted extraction (UAE) method at varied operating parameters. Maximum response was obtained for extracting of minerals in 500 mg SRM at 60 °C temperature, setting a vortexing time of 5 min.while using 5.0 mL extracting agent HNO3 (0.5 M)-H2O2 (10 %) at90 % sonication amplitude of ultrasound bath for 6 min.While analyzing the SRM, the percentage recovery wasobtained in ranged between 96.8-102.7 % to assure the accuracy whereasrepeatability (n = 10) study in terms of % RSDyielding≤ 2.29well support the precision of proposed method and limits of quantitation (µg/g) were 0.034, 0.061, 0.065, 0.057, 0.017, 0.175 and 0.053 for Mn, Fe, Cu, Zn, Mg, K and Ca respectively. The proposed UAE method was reliable,efficient, and advantageous over the conventionally employed acid digestion method with regards to less consumption of reagents and short analysis time for the determination of micro and macroelements in fruit peels powder.

The novel tool geometry has been fabricated by a tool manufacturer which is termed as “Z Geometry” having a combination of positive and negative rake angles on a single insert. The term Z-geometry was given to the insert due to its appearance like alphabet “Z” character in its side view. With this special geometry, productivity of Ti-6Al-4V alloy can be increased at elevated feed rate. With this Z geometry, a special type of wiper insert having a flat flank face for robust rubbing action was provided to reduce the surface roughness. The combination of inserts, i.e., Z-geometry and wiper insert, is termed as hybrid geometry. This paper involves the evaluation of tool life, material removed, temperature of the machining zone, and surface roughness during the dry milling of Ti-6Al-4V alloy using hybrid geometry tooling at higher feed rates compared to the literature. Feed rate at four levels (0.30, 0.60, 0.90, 1.20 mm/rev) and depth of cut at two levels (0.50, 0.75 mm) were employed while number of wiper inserts were varied from one to two. Maximum material of 108 cm3 was removed in 20 min at a feed rate of 0.60 mm/rev and 0.50-mm depth of cut. Variation in surface roughness was within 0.20 to 0.50 μm over the course of experimentation irrespective of the variables and tool condition.

Bioimplants demand unique surface integrity (SI) requirements wherein the primary target is to have minimum surface roughness and maximum microhardness to improve their corrosion and wear resistance characteristics. This demands a more meticulous approach while machining biocompatible materials such as AISI 316L than may be the case when the alloy is to be machined for other applications. Various machinability studies have been conducted on AISI 316L targeting the aforementioned aspects. However, in view of the range of parameters that could be investigated and the availability of various parametric optimization algorithms, it is felt that research gaps still exist. This paper reports on the improvement of surface integrity aspects of AISI 316L in the context of bioimplant applications. The grey relational analysis (GRA) approach has been used to first optimize the influence of various milling parameters: cutting environment (wet and dry conditions), the cutting speed (CS), the feed rate (FR), and the axial depth of cut (Ap) for the aspects of surface roughness (Ra), and microhardness with biocompatibility requirements in mind. The experimentation work involves two phases: Taguchi L18 array was used for phase I experimentation followed by multi-attribute GRA-based optimization. Phase II experimentation explored the possibility of further increase in microhardness by machining with worn tools taking GRA-identified optimized parameter levels as a baseline and then increasing the cutting speed. It has been found that the use of worn tools at GRA-optimized parameters results in further improvement in SI aspects in general. An extent of 37% improvement in terms of the maximum value of microhardness (301 HV at 15-μm depth) has been reported compared with GRA-optimized value (218 HV) when worn tools at higher cutting speeds are employed. This is accompanied by a machined hardened layer extending up to a depth of 222 μm and an associated Ra of 0.85 μm. Microstructure analysis shows more machined-affected zones with worn tools thus supporting the findings.

This paper presents a new immunity-based approach to measure the displacement of nodes (or points of interest) in successive-yetsparse images, in order to measure the settlement of deep-foundations under heavy loads. It is developed as a close-range photogrammetric technique to search the nodes, similar to immune-cells searching bacteria in their vicinity. The immunological concepts of chemotaxis, biased random walk, diffusion and inflammation are implemented herein to accomplish the task. The technique greatly reduces the errors and is also validated on site of a mega project to record the settlements in deep foundations. The accuracy levels are found to be conforming to ASTM’s standards for static load testing, including both compression and lateral-load tests. Unlike other techniques, such as particle image velocimetry, this approach is able to handle successive-images with varying ΔT between them. Furthermore, the technique is completely autonomous and does not require any embedded instrumentation.

Wire electric discharge machining (WEDM) is a thermoelectrical process wherein workpiece surface integrity still remains a concern due to process-generated thermal damage. Recent developments in WEDM, such as the use of novel coated wires and multi-pass strategy, present opportunity for reducing this thermal damage and improve surface integrity. Available literature shows that only uncoated wires have been used with multi-pass cutting strategy. Present work reports on the integrated benefits of coated wires with multi-pass cutting technique while employing three coated wires, namely brass core Zn coated, Broncocut Type X (Cu core-ZnCu50 coating) and Topas Plus X (Cu core-double Zn-rich layer coating), to machine superalloy Ti-6Al-4 V in view of its extensive aerospace applications. Five output responses involving workpiece surface roughness, over cut, cutting speed, material removal rate and recast layer thickness have been assessed. Each experiment constituted one rough pass followed by two trim cuts with each cut taken at offset/input parametric settings appropriate for the individual cut type. For comparison purposes, baseline experiment was conducted with uncoated brass wire using the same input parameters employed for rough and rough plus trim cuts for other coated wires considering the fact that rough cut is intended for high material removal, while trim cuts focusing on better surface integrity. Results show that Topas Plus X wire outperformed other wire types investigated in this work. Up to 8% reduction in Ra value, up to 31% increase in cutting speed, up to 26% higher material removal rate (in rough cut) and up to 40% lesser recast layer thickness have been obtained with Topas X wire. Though coated wires resulted in much better performance otherwise, however, higher “overcut” was observed for all coated wires when compared with the simple brass wire. Also, trim cuts that succeeded the rough passes produced crack-free surfaces.

A review of the available literature indicates that the development of metal-reinforced castings present intriguing prospects but carry inherent challenges owing to differences in thermal coefficients, chemical affinities, diffusion issues and the varying nature of intermetallic compounds. It is supported that pressure application during solidification may favorably influence the dynamics of the aforementioned issues; nevertheless, not only certain limitations have been cited, but also some pressure and process regimes have not yet been investigated and optimized. This work employs the pressure-assisted approach for bimetallic steel-reinforced aluminum composite castings at a low-pressure regime and thoroughly investigates the role of three process parameters, namely pouring temperature (800–900 °C), pressure (10–20 bars) and holding time (10–20 s), for producing sound interfaces. The Taguchi L9 orthogonal array has been employed as the Design of the Experiment, while dominant factors have been determined via analysis of variance and the grey relational analysis multi-objective optimization technique. Supplementary analysis through optical micrographs, scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) has been utilized to quantify interfacial layer thicknesses and to study microstructural and compositional aspects of the interface. Nano-indentation tests under static and dynamic loading have also been performed for mechanical strength characterization. It has been found that uniform interfaces with verifiable diffusion are obtainable, with the pouring temperature being the most influential parameter (percentage contribution 92.84%) in this pressure regime. The experiments performed at optimum conditions of pouring temperature, applied pressure and holding time produced a ~328% thicker interface layer, 19.42% better nano-hardness and a 19.10% improved cooling rate as compared to the minimum input values of the said parameters.

Review of the available literature indicates that hard part turning requires tailored cutting edge for specific tool workpiece combination. Incidentally, much work could not be found that evaluates the role of wiper insert’s cutting edge while hard turning. The objective of presented work is to evaluate the effect of wiper inserts’ micro geometry for critical hardness regime of the material. The work employs two distinct edge geometries namely chamfer (0.15 mm × 25°) and chamfer plus hone (0.15 mm × 25°–25 μm) for wiper inserts in finish hard turning of AISI D2 steel without cutting fluid. Taguchi L18 array is used for tool wear/life, material removed and surface roughness and comparison has been done with a conventional insert (chamfered plus honed edge geometry). Three levels of feed rate and depth of cut have been used while machining workpieces of two different hardness (55HRC and 60HRC). Contrary to the influence of edge preparation typically reported for conventional inserts, here, it is found that its role is somewhat suppressed in the presence of wiper macro-geometry for both the output parameters namely tool wear/life and surface roughness. More specifically, workpiece hardness is found to play most significant role (with PCR of 70.11%) for tool life whereas for surface roughness, insert type (wiper inserts are seen to clearly outperform conventional tools) and feed rate are found to be more dominant. Interestingly, maximum wear scar has been observed at the minor cutting edge with “Notching” being the prevalent wear mode. Oxidation and seizure pull-out seem to be principal wear mechanisms. Microhardness is seen to be increased beneath the machined surface for both new and worn tools up to a depth of 200 μm. At highest feed (0.281 mm/rev) and depth of cut (0.20 mm) combination, material pull-out was revealed as the major microstructural damage.

The development of cladded materials to augment the industrial requirements is grown because of having combination of properties in end use applications. However, the real challenge regarding the application of these materials is its machining with reasonable surface finish and accuracy owing to the heterogeneous nature. Therefore, oftenly, thermal cutting process like gas cutting and plasma arc cutting are used, but these processes not only offer poor surface quality but also necessitate additional finishing operations. Wire electric discharge machining (WEDM) process is a viable alternate but the roughness generated on the two surfaces is different as the said process is thermoelectric in nature and both layers of material have different thermoelectric properties. In the current research the cutting performance of WEDM of stainless clad steel has been evaluated with a focus not only to reduce the surface roughness of individual layer but also to minimize the difference as well. The influence of uncommon parameters, such as layer thickness of specific layer, workpiece orientation, pressure ratio, and wire diameter are primarily investigated. Wire diameter and workpiece orientation have proved to be the significant control factors affecting the surface roughness of both the layers, whereas pressure ratio affects the surface roughness of one layer (mild steel). Optimal settings of control factors have been developed using grey relational analysis. The results of confirmatory experiment validates the model as not only the surface roughness of both layers have been decreased, but also the difference comes to be minimal of only about 0.02 μm.

The present work aims to investigate the performance of recently designed chamfered inserts in high-feed turning of Ti-6Al-4V which is considered as the mainstream alloy for aerospace applications. So far, extensive experimental data has been published while turning this alloy using square-, round-, and rhombic-shaped inserts despite certain limitations associated with each insert design. Recently, a novel chamfer insert has been developed with an intention of combining the geometric benefits of aforementioned individual inserts into a single tool. The present work evaluates wear behavior and machining performance of this newly designed insert while turning Ti-6Al-4V without coolant under constant depth of cut of 1 mm at four levels of feed rate (0.14, 0.337, 0.45, 0.562 mm/rev) and three levels of cutting speed (55, 85, 110 m/min). For output responses of tool life and workpiece surface roughness, the novel chamfer inserts not only outperformed the conventional shaped tooling but more interestingly, they were seen to compete with much expensive PCD inserts. Flank wear was observed as the principle wear mode without any signs of crater formation. Chipping was evident at the lowest cutting speed (55 m/min) and low feed rate combinations (0.14 and 0.337 mm/rev). On the other hand, fracture was encountered when operated either at an intermediate cutting speed (85 m/min) with highest feed rate (0.562 mm/rev) or highest cutting speed (110 m/min) and medium to high feed rates (values ranging from 0.337 to 0.562 mm/rev). Workpiece surface roughness (Ra) values were within the range of 0.51 to 3.91 μm over the investigated regime of operating parameters. Strain-hardened layer that deepened up to 150 μm from the machined surface was observed without any microstructural damage.