Optimization of multiple performance characteristics in turning using Taguchi’s quality loss function: An experimental investigation

Cutting force and chip reduction coefficient is the important index of machinability as it determines the power consumption and amount of energy invested in machining actions. It is primarily influenced by process parameters like cutting speed, feed and depth of cut. This paper presents the application of Taguchi’s parameter design to optimize the parameters for individual responses. For multi-response optimization, Taguchi’s quality loss function approach is proposed. In the present investigation, optimal values of cutting speed, feed and depth of cut are determined to minimize cutting force and chip reduction coefficient during orthogonal turning. The effectiveness of the proposed methodology is illustrated through an experimental investigation in turning mild steel workpiece using high speed steel tool

Design and Implementation of Deep Learning Model for Atrial Fibrillation Classification using ECG Signals

Electrocardiograms (ECGs), which are an essential diagnostic tool, are required to be performed in the normal course of clinical practise in order to evaluate cardiac arrhythmias. Convolutional neural network framework is suggested for use in this method, which makes use of deep learning to carry out automatic ECG arrhythmia diagnosis by classifying patient ECGs into the proper cardiac states. The prior training for this network was done using a standard signal data set. The primary objective of this approach is to provide a basic, reliable, and easily implemented deep learning algorithm for the categorization of the two separate cardiac category scenarios that have been selected. The findings demonstrated that a conventional back propagation neural network used in cascade with transferred deep learning classification was able to accomplish exceptionally high levels of performance. The primary objective of this research is to develop an efficient classification system that can forecast the severity of a patient's sleep apnea, as well as to improve classification accuracy and reduce the number of incorrect classifications

Experimental investigation on flank wear and tool life, cost analysis and mathematical model in turning hardened steel using coated carbide inserts

Turning hardened component with PCBN and ceramic inserts have been extensively used recently and replaces traditional grinding operation. The use of inexpensive multilayer coated carbide insert in hard turning is lacking and hence there is a need to investigate the potential and applicability of such tools in turning hardened steels. An attempt has been made in this paper to have a study on turning hardened AISI 4340 steel (47 ± 1 HRC) using coated carbide inserts (TiN/TiCN/Al2O3/ZrCN) under dry environment. The aim is to assess the tool life of inserts and evolution of flank wear with successive machining time. From experimental investigations, the gradual growth of flank wear for multilayer coated insert indicates steady machining without any premature tool failure by chipping or fracturing. Abrasion is found to be the dominant wear mechanisms in hard turning. Tool life of multilayer coated carbide inserts has been found to be 31 minute and machining cost per part is Rs.3.64 only under parametric conditions chosen i.e. v = 90 m/min, f = 0.05 mm/rev and d = 0.5 mm. The mathematical model shows high determination coefficient, R2 (99%) and fits the actual data well. The predicted flank wear has been found to lie very close to the experimental value at 95% confidence level. This shows the potential and effectiveness of multilayer coated carbide insert used in hard turning applications

Employee Attendance System using Face Recognition Using LBP Method

In the field of computer vision, the study of face recognition technologies is becoming an increasingly important area of research. This technology can be useful in a variety of settings, including those involving security and surveillance, biometrics, and the interaction between humans and computers. (HCI). Deep learning models, such as convolutional neural networks, have made it feasible to create face recognition systems that are both accurate and scalable. These systems are already in widespread use. These advancements are a direct consequence of the models that were utilised in the making of them. Despite this, there are still a lot of obstacles that need to be overcome, such as developing systems that are more open-minded and objective, as well as figuring out how to account for changes in posture, expression, and lighting. This article presents a comprehensive examination of the innovative techniques that are now being employed for face recognition. Deep learning is an example of a more contemporary methodology, whereas local binary patterns are an example of an earlier approach to machine learning. In addition, we analyse the numerous challenges and confinements posed by these methods and present some potential answers to the issues that have been raised. In addition to this, we investigate the moral and legal implications of employing facial recognition technology, as well as the preexisting datasets that are typically put to use for this kind of research.  The one of the section of this investigation will centre on the characteristics and characteristics that are crucial to the effective operation of a face recognition system. As research and development activities are carried on into the foreseeable future, there will be a great deal of fascinating progress made in this area. These advancements have the potential to significantly improve both safety and comfort in a variety of settings

On certain subclasses of analytic functions associated with the Carlson–Shaffer operator

The object of the present paper is to solve Fekete-Szego problem and determine the sharp upper bound to the second Hankel determinant for a certain class $R^{\lambda}(a,c,A,B)$ of analytic functions in the unit disk. We also investigate several majorization properties for functions belonging to a subclass $\widetilde {R}^{\lambda}(a,c, A,B)$ of $R^{\lambda}(a,c,A,B)$ and related function classes. Relevant connections of the main results obtained here with those given by earlier workers on the subject are pointed out

Role of Krimi (Pathogen) in Aupasarkika Yakrit Vikara (Infective Liver diseases) - A Narrative Review

Infective liver diseases are more than 18% of total Chronic Liver Diseases and been ranked as the fifth most common cause of death worldwide. Krimi can create systemic infection like - Jvara, Vibarnata (skin rash), Shula (Pain), Bhaktadwesa (anorexia), Krimija Pandu (Anaemia) etc. to organ specific disorders like - Krimi Danta, Krimi Karna, Asadhya Pratisyaya, Krimi Granthi, Krimija Hrudroga, Krimija Shira Roga But Krimijayakrit Vikara/Roga is not found in classical literature. A considerable number of Infectious liver disease patients came to various Ayurveda Hospital and expert Ayurveda physicians either feed up with conventional treatment or cannot bear the expenses of conventional therapy. Therefore, it is an attempt to establish the role of Krimi in Aupasarkika Yakrit Vikara through reviewing Ayurveda and modern literature with some experience-based inputs. Virus, bacteria, protozoa can be Adrisya Krimi (not visible in necked eye), Anu (minute) and Suksma and Nematodes and fungus are Drisya (Visible) Krimis. Purisaja and Raktaja Krimi can produce Krimija Yakrit Roga as near to Liver and through gut - liver axis and rich circulation of liver. Jvara (Fever), Shula (abdominal pain), Mandagni (low digestive power), Pita Netrata (Jaundice) are cardinal symptom of Krimija Yakrit Roga. In three dosas, Kapha Dosa is more aggravated in Krimija Yakrit Roga. Rakta Vaha Srotas and Purisha Vaha are mostly affected Srotas in Krimija Yakrit Roga. Sahaja Krimi or Avaikarik Krimi are said to be gut microbiota which are more than 100 trillion microorganisms in the gut show high metabolic activity and are continuously helping with the host immune system through gut-liver axis. Krimija Yakrit Roga can be a type of Yakrit Roga which clinically manifested as Yakritdalludara (Hepato megaly) and Yakrit Kshyaya (Cirrhosis of Liver). Successful diagnosis of type of Krimi can be possible though careful history and appropriate microbial and radiological studies for proper treatment of liver diseases

A response surface methodology and desirability approach for predictive modeling and optimization of cutting temperature in machining hardened steel

This paper presents an experimental investigation on cutting temperature during hard turning of EN 24 steel (50 HRC) using TiN coated carbide insert under dry environment. The prediction model is developed using response surface methodology and optimization of process parameter is performed by desirability approach. A stiff rise in cutting temperature is noticed when feed and cutting speed are elevated. The effect of depth of cut on cutting temperature is not that much significant compared with cutting speed and feed as observed from main effects plot. The response surface second order model presented high correlation coefficient (R2 = 0.992) explaining 99.2 % of the variability in the cutting temperature which indicates the goodness of fit for the model to the actual data and high statistical significance of the model. The experimental and predicted values are very close to each other. The calculated error for cutting temperature lies between 1.88-3.19 % during confirmation trial. Therefore, the developed second order model correlates the relationship of the cutting temperature with the process parameters with good degree of approximation. The optimal combination for process parameter is depth of cut at 0.2mm, feed of 0.1597 mm/rev and cutting speed of 70m/min. Based on these combination, the value of cutting temperature is 302.950C whose desirability is one

Fragility curves for special truss moment frame with single and multiple vierendeel special segment

[EN] Special Truss Moment frame (STMF) is an open web truss moment frame, which dissipates the input seismic energy through a well-defined ductile special segment located near the mid-span of truss while other members of truss outside the special segment and columns are designed to remain elastic. In this paper, the performance and the fragility curve of STMFs consisting single and multiple vierendeel panels in the special segment are investigated. The seismic response of nine-story having the length to depth ratio of special segment 2.5 is considered to develop the fragility curve. The seismic response of each building was recorded by performing nonlinear incremental dynamic analyses. Each archetype modelled in nonlinear analysis program PERFORM-3D to carry out IDA under a suit of forty-four real Far Field ground motion records. Fragility curves were developed for these structures and the probability of exceedance at immediate occupancy (IO) level, Life safety (LS) level and Collapse performance (CP) level was assessed for two level of hazards, DBE level (10% probability of exceedance in 50 years) and MCE level (2% probability of exceedance in 50 years). For DBE level earthquake intensity, the probability of exceedance for the CP performance level of STMF building for both structure is marginal while at MCE level the probability of exceedance at CP performance level is 71% and 45% for single and multiple panels respectively.Kumar, R.; Sahoo, D.; Gupta, A. (2018). Fragility curves for special truss moment frame with single and multiple vierendeel special segment. En Proceedings of the 12th International Conference on Advances in Steel-Concrete Composite Structures. ASCCS 2018. Editorial Universitat Politècnica de València. 739-746. https://doi.org/10.4995/ASCCS2018.2018.7248OCS73974