Effect of wire coating on the wire EDM performance characteristics during the machining of Inconel 718

The current work aims to investigate the effect of wire electrode coating, wire EDM process parameters like pulse on time, pulse off time, servo voltage and wire feed rate, on the performance characteristics like cutting speed, surface roughness and flatness error. Inconel 718 superalloy is chosen as the work material, the wire electrodes considered for the study are uncoated brass and zinc coated brass wires having a diameter of 0.25 mm. Grey relational analysis is performed to optimize the process parameters for maximizing machining performance and the results are verified by conducting confirmation experiments. L18 orthogonal array was considered to perform the experiments. The effects of wire coating was analyzed separately on each response. Wire coating was found to have a significant effect in improving the cutting speed and surface quality. Optimized process parameters improved surface roughness and profile accuracy by 6% and 37% respectively with an overall improvement of 8.6% in grey relational grade

Surface integrity comparison of wire electric discharge machined Inconel 718 surfaces at different machining stabilities

Current study aims to investigate the effect of machining stability on the surface integrity of the wire electric discharge machined Inconel 718 superalloy. The wire electrode material used for machining is hard zinc coated brass. Experiments were conducted at various levels of machining stabilities, defined with respect to discharge energies and machining gap conditions. The topographical characteristics were analysed by contact surface profilometer and non-contact 3D profilometer. Scanning electron microscope (SEM) images were analyzed to observe and compare the surface defects like microvoids, micro-cracks, micro globules, micro-craters on the samples machined at different stability levels. The least stable machining condition, with highest discharge energy, lowest inter electrode gap and least pulse off time, produced the most uneven topography. Recast layer (RCL) thickness was analyzed by observing the polished cross-sectional view of machined specimens under SEM. The conditions that provided the least RCL thickness were the most stable machining conditions. Furthermore, the surface layer characteristics like elemental contamination and thermal softening effects were analyzed using EDS and micro hardness tester respectively. All these characteristics have close relationship with the degree of machining stability

Failure detection and control for wire EDM process using multiple sensors

Unstable machining conditions during wire EDM process can lead to process failures, which affects the efficiency and sustainability of process. The study aims to develop a sensor-based failure prediction and process control system. The monitoring system consisting of high sampling rate differential and current probes extracts voltage and current signals during spark machining. Relevant discharge characteristics like pulse proportions, pulse frequency, and discharge energy are extracted from the pulse train data. The proposed process control algorithm works in three stages: failure prediction, failure severity assessment, and process control. Failure conditions considered are wire breakage and spark absence, which are predicted based on the extracted discharge characteristics. Severity of failure is judged based on the spark discharge energy. The proposed process control algorithm retunes the process parameters by adjusting pulse on time, pulse off time, and servo voltage, based on the type of failure and its severity. The methodology is successful in preventing the potential failure situation by restoring the machining stability. The capability of the model is demonstrated by conducting confirmation experiments. Microstructural comparison of machined surfaces and worn wire surfaces also confirms the effectiveness of the proposed strategy to ensure failure free machining with better surface integrity

Sustainability improvement of WEDM process by analysing and classifying wire rupture using kernel-based naive Bayes classifier

The current work aims to improve the sustainability of wire electric discharge machining by predicting the wire breakages. Wire breakages are process interruptions which increase the machining time, energy wastage and material consumption. The study is a novel approach to predict process continuity by binomial classification of machining outcomes using kernel-based naive Bayes algorithm. The two classes are labelled as wire breakages and continuous machining. Training dataset consists of 31 experiments according to central composite design of response surface methodology, and wire breakage instances are recorded as response. The input dataset contains four machining parameters, namely pulse on time, pulse off time, servo voltage and wire feed rate, whereas mean gap voltage variation is derived from in-process data. The trained model was 96.7% accurate in wire breakage predictions. Further, nine confirmation tests were conducted to check model adequacy in real-world situations. The model predicted all instances of wire breakages accurately. The stages of wire wear up to wire rupture were studied by conducting microstructural analysis

Integrated use of residues from olive mill and winery for lipase production by solid state fermentation with Aspergillus sp

Two phase olive mill waste (TPOMW) is presently the major waste produced by the olive mill industry. This waste has potential to be used as substrate for solid state fermentation (SSF) despite of its high concentration of phenolic compounds and low nitrogen content. In this work, it is demonstrated that mixtures of TPOMW with winery wastes support the production of lipase by Aspergillus spp. By agar plate screening, Aspergillus niger MUM 03.58, Aspergillus ibericus MUM 03.49 and Aspergillus uvarum MUM 08.01 were chosen for lipase production by SSF. Plackett-Burman experimental design was employed to evaluate the effect of substrate composition and time on lipase production. The highest amounts of lipase were produced by A. ibericus on a mixture of TPOMW, urea and exhausted grape mark (EGM). Urea was found to be the most influent factor for the lipase production. Further optimization of lipase production by A. ibericus using a full factorial design (32) conducted to optimal conditions of substrate composition (0.073 g urea/g and 25% of EGM) achieving 18.67 U/g of lipolytic activity.Jose Manuel Salgado is grateful for Postdoctoral fellowship (EX-2010-0402) of Education Ministry of Spanish Government. Luis Abrunhosa was supported by the grant SFRH/BPD/43922/2008 from Fundacao para a Ciencia e Tecnologia-FCT, Portugal. Authors thank Fundacao para a Ciencia e a Tecnologia (FCT) for financial support through the project FCT Pest-OE/EQB/LA0023/2011

Machine learning based classification and analysis of wire-EDM discharge pulses

Wire electrical discharge machining (wire-EDM) process is having immense potential over conventional machining methods due to its non-contact nature of material removal. However, frequent and unanticipated machining failures like wire breakages negatively affect the productivity, sustainability and efficiency of the process. In this context, there is a wide scope to improve the process efficiency through online condition monitoring. A prominent aspect of EDM condition monitoring is discharge pulse discrimination. The threshold based methods which are currently being used has low accuracy and is reliant on operator’s experience. In this study, a machine learning (ML) based pulse classification based on the extracted discharge characteristics is proposed. The features are extracted from the raw voltage and current senor signals collected from the machining zone during the wire EDM operation. Among the various ML models, Artificial Neural Network (ANN) classifier is found to have the maximum prediction accuracy of 98 %. Also, the effects of different discharge pulses on the productivity, surface finish and machining failures are investigated. The short circuit and arc discharges are found to cause wire breakage failure if they predominate the pulse cycle by more than 80 %. Also, short and arc sparks increase the surface roughness significantly, by up to 70 %

Heart Disease Prediction using Machine Learning

The Web Article Summarizer is an advanced NLP-based application that leverages state-of-the-art transformer models, including BART for abstractive summarization and BERT for contextual understanding, combined in a dual-encoder architecture to generate accurate and coherent summaries from lengthy articles. Built using the Flask framework, the system features a scalable RESTful API that enables seamless integration with web and mobile platforms, while its multi-stage preprocessing pipeline ensures optimal text normalization and feature extraction. Evaluated using ROUGE metrics, the solution demonstrates superior performance in maintaining content integrity and domain-specific terminology handling compared to traditional methods. Designed for researchers, students, and professionals, this tool addresses information overload challenges by delivering concise yet comprehensive summaries, with future enhancements planned for multilingual support via mBART, cloud deployment for real-time processing, and JWT authentication for secure API access, making it a versatile and powerful solution for automated text summarization across diverse applications

GreenPHABLETTM video for effective information dissemination on hermetic groundnut storage technology

Information and communication technologies (ICT) tools can facilitate dissemination of need based and farmer centric information at an affordable cost to India's rural population. One of the major constraints of groundnut production is aflatoxin accumulation and insect infestation during storage. In our studies conducted at ICRISAT, the Purdue Improved Crop Storage (PICS) hermetic storage technology proved effective against insect infestation and aflatoxin accumulation during storage. To facilitate visual learning of the use of hermetic storage, a five minute GreenPHABLETTM video (GPV) in the local language was developed at ICRISAT. A 3-month long experiment was conducted in collaboration with an NGO Samatha of Penugonda in Anantapur district of Andhra Pradesh, India to assess the dissemination potential of GPV. A survey conducted among 30 farmers who received the video, revealed that about 80% of farmers received the video from a fellow farmer and only 20 per cent farmers received from the extension agents. Majority of the farmers received the video on their mobile phones through "Share It" (73.3%) and 13.3 per cent received via "Bluetooth", further 10 per cent reported through "WhatsApp" and while only 3.3% received it through the computer by USB Copy. After three months, 300 farmers from 40 villages received the GPV, while our 30 respondents shared the GPV with 150 farmers and screened the GPV to 200 farmers. The experiment shows that GPV can be an effective tool for spreading information about the groundnut hermetic storage technology and other agricultural innovations

Development and validation of a repharsed phase- HPLC method for simultaneous determination of rosiglitazone and glimepiride in combined dosage forms and human plasma

<p>Abstract</p> <p>Background</p> <p>Rosiglitazone (ROZ) and glimepiride (GLM) are antidiabetic agents used in the treatment of type 2 diabetes mellitus. A survey of the literature reveals that only one spectrophotometric method has been reported for the simultaneous determination of ROS and GLM in pharmaceutical preparations. However the reported method suffers from the low sensitivity, for this reason, our target was to develop a simple sensitive HPLC method for the simultaneous determination of ROZ and GLM in their combined dosage forms and plasma.</p> <p>Results</p> <p>A simple reversed phase high performance liquid chromatographic (RP-HPLC) method was developed and validated for the simultaneous determination of Rosiglitazone (ROS) and Glimepiride (GLM) in combined dosage forms and human plasma. The separation was achieved using a 150 mm × 4.6 mm i.d., 5 μm particle size Symmetry^®C18 column. Mobile phase containing a mixture of acetonitrile and 0.02 M phosphate buffer of pH 5 (60: 40, V/V) was pumped at a flow rate of 1 mL/min. UV detection was performed at 235 nm using nicardipine as an internal standard. The method was validated for accuracy, precision, specificity, linearity, and sensitivity. The developed and validated method was successfully used for quantitative analysis of Avandaryl™ tablets. The chromatographic analysis time was approximately 7 min per sample with complete resolution of ROS (t_R= 3.7 min.), GLM (t_R= 4.66 min.), and nicardipine (t_R, 6.37 min). Validation studieswas performed according to ICH Guidelines revealed that the proposed method is specific, rapid, reliable and reproducible. The calibration plots were linear over the concentration ranges 0.10-25 μg/mL and 0.125-12.5 μg/mL with LOD of 0.04 μg/mL for both compounds and limits of quantification 0.13 and 0.11 μg/mL for ROS and GLM respectively.</p> <p>Conclusion</p> <p>The suggested method was successfully applied for the simultaneous analysis of the studied drugs in their co-formulated tablets and human plasma. The mean percentage recoveries in Avandaryl™ tablets were 100.88 ± 1.14 and 100.31 ± 1.93 for ROS and GLM respectively. Statistical comparison of the results with those of the reference method revealed good agreement and proved that there were no significant difference in the accuracy and precision between the two methods respectively. The interference likely to be introduced from some co-administered drugs such as glibenclamide, gliclazide, metformine, pioglitazone and nateglinide was investigated.</p

Managing fruit rot diseases of Vaccinium corymbosum

Blueberry is an important perennial fruit crop with expanding consumption and production worldwide. Consumer demand for blueberries has grown due to the desirable flavor and numerous health benefits, and fresh market production in the U.S. has risen in turn. U.S. imports have also increased to satisfy year-round consumer demand for fresh blueberries. Pre- and post-harvest fruit diseases such as anthracnose (caused by Colletotrichum spp.) and botrytis fruit rot (caused by Botrytis spp.) have a significant impact on fruit quality and consumer acceptance. These are also among the most difficult diseases to control in the blueberry cropping system. These latent pathogens can cause significant losses both in the field, and especially during transport and marketplace storage. Although both diseases result in rotted fruit, the biology and infection strategies of the causal pathogens are very different, and the management strategies differ. Innovations for management, such as improved molecular detection assays for fungicide resistance, postharvest imaging, breeding resistant cultivars, and biopesticides have been developed for improved fruit quality. Development and integration of new strategies is critical for the long-term success of the blueberry industry