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1 – 10 of 344Muhammad Arif Mahmood, Chioibasu Diana, Uzair Sajjad, Sabin Mihai, Ion Tiseanu and Andrei C. Popescu
Porosity is a commonly analyzed defect in the laser-based additive manufacturing processes owing to the enormous thermal gradient caused by repeated melting and solidification…
Abstract
Purpose
Porosity is a commonly analyzed defect in the laser-based additive manufacturing processes owing to the enormous thermal gradient caused by repeated melting and solidification. Currently, the porosity estimation is limited to powder bed fusion. The porosity estimation needs to be explored in the laser melting deposition (LMD) process, particularly analytical models that provide cost- and time-effective solutions compared to finite element analysis. For this purpose, this study aims to formulate two mathematical models for deposited layer dimensions and corresponding porosity in the LMD process.
Design/methodology/approach
In this study, analytical models have been proposed. Initially, deposited layer dimensions, including layer height, width and depth, were calculated based on the operating parameters. These outputs were introduced in the second model to estimate the part porosity. The models were validated with experimental data for Ti6Al4V depositions on Ti6Al4V substrate. A calibration curve (CC) was also developed for Ti6Al4V material and characterized using X-ray computed tomography. The models were also validated with the experimental results adopted from literature. The validated models were linked with the deep neural network (DNN) for its training and testing using a total of 6,703 computations with 1,500 iterations. Here, laser power, laser scanning speed and powder feeding rate were selected inputs, whereas porosity was set as an output.
Findings
The computations indicate that owing to the simultaneous inclusion of powder particulates, the powder elements use a substantial percentage of the laser beam energy for their melting, resulting in laser beam energy attenuation and reducing thermal value at the substrate. The primary operating parameters are directly correlated with the number of layers and total height in CC. Through X-ray computed tomography analyses, the number of layers showed a straightforward correlation with mean sphericity, while a converse relation was identified with the number, mean volume and mean diameter of pores. DNN and analytical models showed 2%–3% and 7%–9% mean absolute deviations, respectively, compared to the experimental results.
Originality/value
This research provides a unique solution for LMD porosity estimation by linking the developed analytical computational models with artificial neural networking. The presented framework predicts the porosity in the LMD-ed parts efficiently.
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Elavaar Kuzhali S. and Pushpa M.K.
COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150…
Abstract
Purpose
COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.
Design/methodology/approach
The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.
Findings
From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.
Originality/value
This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.
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Naveenkumar R., Shanmugam S. and Veerappan AR
The purpose of this paper is to understand the effect of basin water depth towards the cumulative distillate yield of the traditional and developed single basin double slope solar…
Abstract
Purpose
The purpose of this paper is to understand the effect of basin water depth towards the cumulative distillate yield of the traditional and developed single basin double slope solar still (DSSS).
Design/methodology/approach
Modified single basin DSSS integrated with solar operated vacuum fan and external water cooled condenser was fabricated using aluminium material. During sunny season, experimental investigations have been performed in both conventional and modified DSSS at a basin water depth of 3, 6, 9 and 12 cm. Production rate and cumulative distillate yield obtained in traditional and developed DSSS at different water depths were compared and best water depth to attain the maximum productivity and cumulative distillate yield was found out.
Findings
Results indicated that both traditional and modified double SS produced maximum yield at the minimum water depth of 3 cm. Cumulative distillate yield of the developed SS was 16.39%, 18.86%, 15.22% and 17.07% higher than traditional at water depths of 3, 6, 9 and 12 cm, respectively. Cumulative distillate yield of the developed SS at 3 cm water depth was 73.17% higher than that of the traditional SS at 12 cm depth.
Originality/value
Performance evaluation of DSSS at various water depths by integrating the combined solar operated Vacuum fan and external Condenser.
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Md Mehedi Hasan Rubel, Syed Rashedul Islam, Abeer Alassod, Amjad Farooq, Xiaolin Shen, Taosif Ahmed, Mohammad Mamunur Rashid and Afshan Zareen
The main purpose of this study was to prepare the cotton fibers and cellulose powder by a layer of nano-crystalline-titanium dioxide (TiO2) using the sol-gel sono-synthesis method…
Abstract
Purpose
The main purpose of this study was to prepare the cotton fibers and cellulose powder by a layer of nano-crystalline-titanium dioxide (TiO2) using the sol-gel sono-synthesis method to clean the wastewater containing reactive dye. Moreover, TiO2 nano-materials are remarkable due to their photoactive properties and valuable applications in wastewater treatment.
Design/methodology/approach
In this research, TiO2 was synthesized and deposited effectively on cotton fibers and cellulose powder using ultrasound-assisted coating. Further, tetra butyl titanate was used as a precursor to the synthesis of TiO2 nanoparticles. Reactive dye (red 195) was used in this study. X-ray Diffraction, scanning electron microscopy and Fourier transform infrared spectroscopy were performed to prove the aptitude for the formation of crystal TiO2 on the cotton fibers and cellulose powder along with TiO2 nanoparticles as well as to analyze the chemical structure. Decoloration of the wastewater was investigated through ultraviolet (UV-Visible) light at 30 min.
Findings
The experimental results revealed that the decolorization was completed at 2.0 min with the cellulose nano TiO2 treatment whereas cotton nano TiO2 treated solution contained reactive dyestuffs even after the treatment of 2 min. This was the fastest method up to now than all reported methods for sustainable decolorization of wastewater by absorption. Furthermore, this study explored that the cellulose TiO2 nano-composite was more effective than the cotton TiO2 nano-composite of decoloration wastewater for the eco-friendly remedy.
Research limitations/implications
Cotton fibers and cellulose powder with nano-TiO2, and only reactive dye (red 195) were tested.
Practical implications
With reactive dye-containing wastewater, it seems to be easier to get rid of the dye than to retain it, especially from dyeing of yarn, fabric, apparel, and as well as other sectors where dyestuffs are used.
Social implications
This research would help to reduce pollution in the environment as well as save energy and cost.
Originality/value
Decoloration of wastewater treatment is an essential new track with nano-crystalline TiO2 to fast and efficient cleaning of reactive dyes containing wastewater used as a raw material.
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Subbarama Kousik Suraparaju, Arjun Singh K., Vijesh Jayan and Sendhil Kumar Natarajan
The utilisation of renewable energy sources for generating electricity and potable water is one of the most sustainable approaches in the current scenario. Therefore, the current…
Abstract
Purpose
The utilisation of renewable energy sources for generating electricity and potable water is one of the most sustainable approaches in the current scenario. Therefore, the current research aims to design and develop a novel co-generation system to address the electricity and potable water needs of rural areas.
Design/methodology/approach
The cogeneration system mainly consists of a solar parabolic dish concentrator (SPDC) system with a concentrated photo-voltaic module at the receiver for electricity generation. It is further integrated with a low-temperature thermal desalination (LTTD) system for generating potable water. Also, a novel corn cob filtration system is introduced for the pre-treatment to reduce the salt content in seawater before circulating it into the receiver of the SPDC system. The designed novel co-generation system has been numerically and experimentally tested to analyse the performance at Karaikal, U.T. of Puducherry, India.
Findings
Because of the pre-treatment with a corn cob, the scale formation in the pipes of the SPDC system is significantly reduced, which enhances the efficiency of the system. It is observed that the conductivity, pH and TDS of seawater are reduced significantly after the pre-treatment by the corncob filtration system. Also, the integrated system is capable of generating 6–8 litres of potable water per day.
Originality/value
The integration of the corncob filtration system reduced the scaling formation compared to the general circulation of water in the hoses. Also, the integrated SPDC and LTTD systems are comparatively economical to generate higher yields of clean water than solar stills.
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Guodong Sa, Haodong Bai, Zhenyu Liu, Xiaojian Liu and Jianrong Tan
The assembly simulation in tolerance analysis is one of the most important steps for the tolerance design of mechanical products. However, most assembly simulation methods are…
Abstract
Purpose
The assembly simulation in tolerance analysis is one of the most important steps for the tolerance design of mechanical products. However, most assembly simulation methods are based on the rigid body assumption, and those assembly simulation methods considering deformation have a poor efficiency. This paper aims to propose a novel efficient and precise tolerance analysis method based on stable contact to improve the efficiency and reliability of assembly deformation simulation.
Design/methodology/approach
The proposed method comprehensively considers the initial rigid assembly state, the assembly deformation and the stability examination of assembly simulation to improve the reliability of tolerance analysis results. The assembly deformation of mating surfaces was first calculated based on the boundary element method with optimal initial assembly state, then the stability of assembly simulation results was assessed by the density-based spatial clustering of applications with noise algorithm to improve the reliability of tolerance analysis. Finally, combining the small displacement torsor theory, the tolerance scheme was statistically analyzed based on sufficient samples.
Findings
A case study of a guide rail model demonstrated the efficiency and effectiveness of the proposed method.
Research limitations/implications
The present study only considered the form error when generating the skin model shape, and the waviness and the roughness of the matching surface were not considered.
Originality/value
To the best of the authors’ knowledge, the proposed method is original in the assembly simulation considering stable contact, which can effectively ensure the reliability of the assembly simulation while taking into account the computational efficiency.
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Muhammad Adnan Hasnain, Hassaan Malik, Muhammad Mujtaba Asad and Fahad Sherwani
The purpose of the study is to classify the radiographic images into three categories such as fillings, cavity and implant to identify dental diseases because dental disease is a…
Abstract
Purpose
The purpose of the study is to classify the radiographic images into three categories such as fillings, cavity and implant to identify dental diseases because dental disease is a very common dental health problem for all people. The detection of dental issues and the selection of the most suitable method of treatment are both determined by the results of a radiological examination. Dental x-rays provide important information about the insides of teeth and their surrounding cells, which helps dentists detect dental issues that are not immediately visible. The analysis of dental x-rays, which is typically done by dentists, is a time-consuming process that can become an error-prone technique due to the wide variations in the structure of teeth and the dentist's lack of expertise. The workload of a dental professional and the chance of misinterpretation can be decreased by the availability of such a system, which can interpret the result of an x-ray automatically.
Design/methodology/approach
This study uses deep learning (DL) models to identify dental diseases in order to tackle this issue. Four different DL models, such as ResNet-101, Xception, DenseNet-201 and EfficientNet-B0, were evaluated in order to determine which one would be the most useful for the detection of dental diseases (such as fillings, cavity and implant).
Findings
Loss and accuracy curves have been used to analyze the model. However, the EfficientNet-B0 model performed better compared to Xception, DenseNet-201 and ResNet-101. The accuracy, recall, F1-score and AUC values for this model were 98.91, 98.91, 98.74 and 99.98%, respectively. The accuracy rates for the Xception, ResNet-101 and DenseNet-201 are 96.74, 93.48 and 95.65%, respectively.
Practical implications
The present study can benefit dentists from using the DL model to more accurately diagnose dental problems.
Originality/value
This study is conducted to evaluate dental diseases using Convolutional neural network (CNN) techniques to assist dentists in selecting the most effective technique for a particular clinical condition.
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Natthawut Daoset, Samroeng Inglam, Sujin Wanchat and Nattapon Chantarapanich
This paper aims to investigate the influence of post-curing temperature, post-curing time and gamma ray irradiation dose upon the tensile and compressive mechanical properties of…
Abstract
Purpose
This paper aims to investigate the influence of post-curing temperature, post-curing time and gamma ray irradiation dose upon the tensile and compressive mechanical properties of the medical graded vat photopolymerization parts.
Design/methodology/approach
Medical graded vat photopolymerization specimens, made from photopolymer resin, were fabricated using bottom-up vat photopolymerization machine. Tensile and compressive tests were conducted to assess the mechanical properties. The specimens were categorized into uncured and post-curing groups. Temperature post-processing and/or gamma irradiation exposure were for post-curing specimens. The post-curing parameters considered included temperature levels of 50°C, 60°C and 70°C, with 1, 2, 3 and 4 h periods. For the gamma irradiation, the exposure doses were 25, 50, 75 and 100 kGy.
Findings
Post-curing improved the mechanical properties of medical graded vat photopolymerization parts for both tensile and compressive specimens. Post-curing temperature greater than 50°C or a prolonged post-curing period of more than 1 h made insignificant changes or deterioration in mechanical properties. The optimal post-curing condition was therefore a 50°C post-curing temperature with 1 h post-curing time. Exposure to gamma ray improved the compressive mechanical properties, but deteriorated tensile mechanical properties. Higher gamma irradiation doses could decrease the mechanical properties and also make the part more brittle, especially for doses more than 25 kGy.
Originality/value
The obtained results would be beneficial to the medical device manufacturer who fabricated the invasive temporary contact personalized surgical instruments by vat photopolymerization technique. In addition, it also raised awareness in excessive gamma sterilization in the medical graded vat photopolymerization parts.
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Ronnarit Khuengpukheiw, Anurat Wisitsoraat and Charnnarong Saikaew
This paper aims to compare the wear behavior, surface roughness, friction coefficient and volume loss of high-velocity oxy-fuel (HVOF) sprayed WC–Co and WC–Cr3C2–Ni coatings on…
Abstract
Purpose
This paper aims to compare the wear behavior, surface roughness, friction coefficient and volume loss of high-velocity oxy-fuel (HVOF) sprayed WC–Co and WC–Cr3C2–Ni coatings on AISI 1095 steel with spraying times of 10 and 15 s.
Design/methodology/approach
In this study, the pin-on-disc testing technique was used to evaluate the wear characteristics at a speed of 0.24 m/s, load of 40 N and test time of 60 min under dry conditions at room temperature. The wear characteristics were examined and analyzed by scanning electron microscopy and energy dispersive X-ray spectroscopy. The surface roughness of a coated surface was measured, and microhardness measurements were performed on the cross-sectioned and polished surfaces of the coating.
Findings
Spraying time and powder material affected the hardness of HVOF coatings due to differences in the porosity of the coated layers. The average hardness of the WC–Cr3C2–Ni coating with a spaying time of 15 s was approximately 14% higher than that of the WC–Cr3C2–Ni coating with a spraying time of 10 s. Under an applied load of 40 N, the WC–Co coating with a spraying time of 15 s had the lowest variation in the friction coefficient compared with the other coatings. The WC–Co coating with a spraying time of 10 s had the lowest average and variation in volume loss compared to the other coatings. The WC–Cr3C2–Ni coating with a spraying time of 10 s exhibited the highest average volume loss. The wear features changed slightly with the spraying time owing to variations in the hardness and friction coefficient.
Originality/value
This study investigated tribological performance of WC–Co; WC-Cr3C2-Ni coatings with spraying times of 10 and 15 s using pin-on-disc tribometer by rotating the relatively soft pin (C45 steel) against hard coated substrate (disc).
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Mohammad Mehralian, Ahmadreza Fallahfaragheh and Mohammad Khajeh Mehrizi
This study aims to investigation of the guar gum-manganese dioxide (GG/MnO2) nanocomposite (NC) synthesized using an environment-friendly method and the degradation of reactive…
Abstract
Purpose
This study aims to investigation of the guar gum-manganese dioxide (GG/MnO2) nanocomposite (NC) synthesized using an environment-friendly method and the degradation of reactive yellow (RY 145) dye in the UV system.
Design/methodology/approach
Characterization of the GG/MnO2 NCs were conducted using field emission scanning electron microscopy, X-ray diffraction and Fourier-transform infrared spectroscopy. Experiments were conducted using a 1 L glass reactor coupled with Ultraviolet (UV-C) blue light bulb of wavelength 250 nm and power of 8 W.
Findings
The NC (2.25 g/L) displayed high RY 145 dye degradation (81%) with 10 mg/L of concentration at pH 3. The coefficient of determination (R2 0.99) also depicted that the model fits the experimental data. The analysis of variance (ANOVA) showed that the F-values of 464.75, 276.04 and 5.15 are related to the dose of GG/MnO2 NCs, initial concentration of RY 145 dye and solution pH, respectively.
Practical implications
The GG/MnO2 NCs followed by photo oxidation process (UV-process) could be used to degrade the RY 145 dye from synthetic wastewater.
Originality/value
There are two main innovations. One is that the novel process is performed successfully for RY 145 dye degradation. The other is that the optimized conditions are obtained by Box–Behnken design. Also, the effects of different variables on the RY 145 dye removal efficiency were investigated.
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