Search results for: hardness prediction

Authors: M. Karimpour, L. Hitihamillage, N. Elkhoury, S. Moridpour, R. Hesami

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Rail transport authorities around the world have been facing a significant challenge when predicting rail infrastructure maintenance work for a long period of time. Generally, maintenance monitoring and prediction is conducted manually. With the restrictions in economy, the rail transport authorities are in pursuit of improved modern methods, which can provide precise prediction of rail maintenance time and location. The expectation from such a method is to develop models to minimize the human error that is strongly related to manual prediction. Such models will help them in understanding how the track degradation occurs overtime under the change in different conditions (e.g. rail load, rail type, rail profile). They need a well-structured technique to identify the precise time that rail tracks fail in order to minimize the maintenance cost/time and secure the vehicles. The rail track characteristics that have been collected over the years will be used in developing rail track degradation prediction models. Since these data have been collected in large volumes and the data collection is done both electronically and manually, it is possible to have some errors. Sometimes these errors make it impossible to use them in prediction model development. This is one of the major drawbacks in rail track degradation prediction. An accurate model can play a key role in the estimation of the long-term behavior of rail tracks. Accurate models increase the track safety and decrease the cost of maintenance in long term. In this research, a short review of rail track degradation prediction models has been discussed before estimating rail track degradation for the curve sections of Melbourne tram track system using Adaptive Network-based Fuzzy Inference System (ANFIS) model.

Keywords: ANFIS, MGT, prediction modeling, rail track degradation

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Authors: Vijay Kr. Yadav, Nilam Rathi

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Accurate prediction of glucose level for diabetes mellitus is required to avoid affecting the functioning of major organs of human body. This study describes the fundamental assumptions and two different methodologies of the Blood glucose prediction. First is based on the back-propagation algorithm of Artificial Neural Network (ANN), and second is based on the Neuro-Fuzzy technique, called Fuzzy Inference System (FIS). Errors between proposed methods further discussed through various statistical methods such as mean square error (MSE), normalised mean absolute error (NMAE). The main objective of present study is to develop mathematical model for blood glucose prediction before 12 hours advanced using data set of three patients for 60 days. The comparative studies of the accuracy with other existing models are also made with same data set.

Keywords: back-propagation, diabetes mellitus, fuzzy inference system, neuro-fuzzy

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Authors: Ravinder Bahl, Jamini Sharma

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The paper demonstrates analysis of the cervical cancer based on a probabilistic model. It involves technique for classification and prediction by recognizing typical and diagnostically most important test features relating to cervical cancer. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases. The combination of the conventional statistical and machine learning tools is applied for the analysis. Experimental study with real data demonstrates the feasibility and potential of the proposed approach for the said cause.

Keywords: cervical cancer, recurrence, no recurrence, probabilistic, classification, prediction, machine learning

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Authors: Mohammad Mahdi Mousavi

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Bankruptcy prediction models have been implemented for continuous evaluation and monitoring of firms. With the huge number of bankruptcy models, an extensive number of studies have focused on answering the question that which of these models are superior in performance. In practice, one of the drawbacks of existing comparative studies is that the relative assessment of alternative bankruptcy models remains an exercise that is mono-criterion in nature. Further, a very restricted number of criteria and measure have been applied to compare the performance of competing bankruptcy prediction models. In this research, we overcome these methodological gaps through implementing an extensive range of criteria and measures for comparison between dynamic and static bankruptcy models, and through proposing a multi-criteria framework to compare the relative performance of bankruptcy models in forecasting firm distress for UK firms.

Keywords: bankruptcy prediction, data envelopment analysis, performance criteria, performance measures

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Authors: Feng Guolin, Gong Zhiqiang

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In order to study the spatial structure and dynamical mechanism of extreme precipitation in East Asia, a corresponding climate network is constructed by employing the method of event synchronization. It is found that the area of East Asian summer extreme precipitation can be separated into two regions: one with high area weighted connectivity receiving heavy precipitation mostly during the active phase of the East Asian Summer Monsoon (EASM), and another one with low area weighted connectivity receiving heavy precipitation during both the active and the retreat phase of the EASM. Besides，a way for the prediction of extreme precipitation is also developed by constructing a directed climate networks. The simulation accuracy in East Asia is 58% with a 0-day lead, and the prediction accuracy is 21% and average 12% with a 1-day and an n-day (2≤n≤10) lead, respectively. Compare to the normal EASM year, the prediction accuracy is lower in a weak year and higher in a strong year, which is relevant to the differences in correlations and extreme precipitation rates in different EASM situations. Recognizing and identifying these effects is good for understanding and predicting extreme precipitation in East Asia.

Keywords: synchronization, climate network, prediction, rainfall

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Authors: H. Unal, A. Mimaroglu, I. Ozsoy

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In this study, the mechanical properties of micro filled epoxy composites were investigated. The matrix material is epoxy. Micro fillers are Al2O3 and TiO2 added in 10-30 wt% by weight ratio. Test samples were prepared using an open mould type die. Tensile, three point bending and hardness tests were carried out. The tensile strength, elastic modulus, elongation at break, flexural strength, flexural modulus and the hardness of the composite materials were obtained and evaluated. It was seen from the results that the level of the mechanical properties of the epoxy composites is highly influenced by micro filler content.

Keywords: composites, epoxy, fillers, mechanical properties

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Authors: A. Jourani, C. Trevisiol, S. Bouvier

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The aim of this work is to study the coupled effect of microstructure and surface roughness on friction coefficient, wear resistance and wear mechanisms. Friction tests on 35NCD16 steel are performed under different normal loads (50-110 N) on a pin-on-plane configuration at cyclic sliding with abrasive silicon carbide grains ranging from 35 µm to 200 µm. To vary hardness and microstructure, the specimens are subjected to water quenching and tempering at various temperatures from 200°C to 600°C. The evolution of microstructures and wear mechanisms of worn surfaces are analyzed using scanning electron microscopy (SEM). For a given microstructure and hardness, the friction coefficient decreases with increasing of normal load and decreasing of the abrasive particle size. The wear rate increase with increasing of normal load and abrasive particle size. The results also reveal that there is a critical hardness Hcᵣᵢₜᵢcₐₗ around 430 Hv which maximizes the friction coefficient and wear rate. This corresponds to a microstructure transition from martensite laths to carbides and equiaxed grains, for a tempering around 400°C. Above Hcᵣᵢₜᵢcₐₗ the friction coefficient and the amount of material loss decrease with an increase of hardness and martensite volume fraction. This study also shows that the debris size and the space between the abrasive particles decrease with a reduction in the particle size. The coarsest abrasive grains lost their cutting edges, accompanied by particle damage and empty space due to the particle detachment from the resin matrix. The compact packing nature of finer abrasive papers implicates lower particle detachment and facilitates the clogging and the transition from abrasive to adhesive wear.

Keywords: martensite, microstructure, friction, wear, surface roughness

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Authors: Nabilah Filzah Mohd Radzuan, Zalinda Othman, Azuraliza Abu Bakar, Abdul Razak Hamdan

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Uncertain data is believed to be an important issue in building up a prediction model. The main objective in the time series uncertainty analysis is to formulate uncertain data in order to gain knowledge and fit low dimensional model prior to a prediction task. This paper discusses the performance of a number of techniques in dealing with uncertain data specifically those which solve uncertain data condition by minimizing the loss of compression properties.

Keywords: compression properties, uncertainty, uncertain time series, mining technique, weather prediction

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Authors: Ulas Vural, M. Ergun Okay, E. Mesut Yildiz

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Telecommunication service providers demand accurate and precise prediction of customer churn probabilities to increase the effectiveness of their customer relation services. The large amount of customer data owned by the service providers is suitable for analysis by machine learning methods. In this study, expenditure data of customers are analyzed by using an artificial neural network (ANN). The ANN model is applied to the data of customers with different billing duration. The proposed model successfully predicts the churn probabilities at 83% accuracy for only three months expenditure data and the prediction accuracy increases up to 89% when the nine month data is used. The experiments also show that the accuracy of ANN model increases on an extended feature set with information of the changes on the bill amounts.

Keywords: customer relationship management, churn prediction, telecom industry, deep learning, artificial neural networks

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Authors: Shraddha Jain Sharma, Ratnalata Gupta

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Predicting stock movements in the financial market is both difficult and rewarding. Analysts and academics are increasingly using advanced approaches such as machine learning techniques to anticipate stock price patterns, thanks to the expanding capacity of computing and the recent advent of graphics processing units and tensor processing units. Stock market prediction is a type of time series prediction that is incredibly difficult to do since stock prices are influenced by a variety of financial, socioeconomic, and political factors. Furthermore, even minor mistakes in stock market price forecasts can result in significant losses for companies that employ the findings of stock market price prediction for financial analysis and investment. Soft computing techniques are increasingly being employed for stock market prediction due to their better accuracy than traditional statistical methodologies. The proposed research looks at the need for soft computing techniques in stock market prediction, the numerous soft computing approaches that are important to the field, past work in the area with their prominent features, and the significant problems or issue domain that the area involves. For constructing a predictive model, the major focus is on neural networks and fuzzy logic. The stock market is extremely unpredictable, and it is unquestionably tough to correctly predict based on certain characteristics. This study provides a complete overview of the numerous strategies investigated for high accuracy prediction, with a focus on the most important characteristics.

Keywords: stock market prediction, artificial intelligence, artificial neural networks, fuzzy logic, accuracy, deep learning, machine learning, stock price, trading volume

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Authors: S. Channgam, A. Sae-Tang, T. Termsaithong

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In this research, the occurrences of large size events in various system sizes of the Bak-Tang-Wiesenfeld sandpile model are considered. The system sizes (square lattice) of model considered here are 25×25, 50×50, 75×75 and 100×100. The cross-correlation between the ratio of sites containing 3 grain time series and the large size event time series for these 4 system sizes are also analyzed. Moreover, a prediction method of the large-size event for the 50×50 system size is also introduced. Lastly, it can be shown that this prediction method provides a slightly higher efficiency than random predictions.

Keywords: Bak-Tang-Wiesenfeld sandpile model, cross-correlation, avalanches, prediction method

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Authors: Yalçın Bozkurt

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Prediction models were developed for accurate prediction of bodyweight (BW) by using Digital Images of beef cattle body dimensions by Artificial Neural Networks (ANN). For this purpose, the animal data were collected at a private slaughter house and the digital images and the weights of each live animal were taken just before they were slaughtered and the body dimensions such as digital wither height (DJWH), digital body length (DJBL), digital body depth (DJBD), digital hip width (DJHW), digital hip height (DJHH) and digital pin bone length (DJPL) were determined from the images, using the data with 1069 observations for each traits. Then, prediction models were developed by ANN. Digital body measurements were analysed by ANN for body prediction and R2 values of DJBL, DJWH, DJHW, DJBD, DJHH and DJPL were approximately 94.32, 91.31, 80.70, 83.61, 89.45 and 70.56 % respectively. It can be concluded that in management situations where BW cannot be measured it can be predicted accurately by measuring DJBL and DJWH alone or both DJBD and even DJHH and different models may be needed to predict BW in different feeding and environmental conditions and breeds

Keywords: artificial neural networks, bodyweight, cattle, digital body measurements

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Authors: Abhijit Pattnayak, Abhijith N.V, Deepak Kumar, Jayant Jain, Vijay Chaudhry

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Hard and dense ceramic coatings deposited on the surface provide the ideal solution to the poor tribological properties exhibited by some popular stainless steels like EN-36, 17-4PH, etc. These steels are widely used in nuclear, fertilizer, food processing, and marine industries under extreme environmental conditions. The present study focuses on the development of Al₂O₃-CeO₂-rGO-based coatings on the surface of 17-4PH steel using High-Velocity Oxygen Flame (HVOF) thermal spray process. The coating is developed using an oxyacetylene flame. Further, we report the physical (Density, Surface roughness, Surface energetics), Metallurgical (Scanning electron microscopy, X-ray diffraction, Raman), Mechanical (Hardness(Vickers and Nano Hard-ness)), Tribological (Wear, Scratch hardness) and Chemical (corrosion) characterization of both As-sprayed coating and the Substrate (17-4 PH steel). The comparison of the properties will help us to understand the microstructure-property relationship of the coating and reveal the necessity and challenges of such coatings.

Keywords: thermal spray process, HVOF, ceramic coating, hardness, wear, corrosion

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Authors: S. S. Sharma, P. R. Prabhu, K. Jagannath, Achutha Kini U., Gowri Shankar M. C.

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In present global scenario, aluminum alloys are coining the attention of many innovators as competing structural materials for automotive and space applications. Comparing to other challenging alloys, especially, 7xxx series aluminum alloys have been studied seriously because of their benefits such as moderate strength; better deforming characteristics, excellent chemical decay resistance, and affordable cost. 7075 Al-alloys have been used in the transportation industry for the fabrication of several types of automobile parts, such as wheel covers, panels and structures. It is expected that substitution of such aluminum alloys for steels will result in great improvements in energy economy, durability and recyclability. However, it is necessary to improve the strength and the formability levels at low temperatures in aluminium alloys for still better applications. Aluminum–Zinc–Magnesium with or without other wetting agent denoted as 7XXX series alloys are medium strength heat treatable alloys. Cu, Mn and Si are the other solute elements which contribute for the improvement in mechanical properties achievable by selecting and tailoring the suitable heat treatment process. On subjecting to suitable treatments like age hardening or cold deformation assisted heat treatments, known as low temperature thermomechanical treatments (LTMT) the challenging properties might be incorporated. T6 is the age hardening or precipitation hardening process with artificial aging cycle whereas T8 comprises of LTMT treatment aged artificially with X% cold deformation. When the cold deformation is provided after solution treatment, there is increase in hardness related properties such as wear resistance, yield and ultimate strength, toughness with the expense of ductility. During precipitation hardening both hardness and strength of the samples are increasing. Decreasing peak hardness value with increasing aging temperature is the well-known behavior of age hardenable alloys. The peak hardness value is further increasing when room temperature deformation is positively supported with age hardening known as thermomechanical treatment. Considering these aspects, it is intended to perform heat treatment and evaluate hardness, tensile strength, wear resistance and distribution pattern of reinforcement in the matrix. 2 to 2.5 and 3 to 3.5 times increase in hardness is reported in age hardening and LTMT treatments respectively as compared to as-cast composite. There was better distribution of reinforcements in the matrix, nearly two fold increase in strength levels and upto 5 times increase in wear resistance are also observed in the present study.

Keywords: reinforcement, precipitation, thermomechanical, dislocation, strain hardening

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Authors: M. K. Manoj, V. Pancholi, S. K. Nath

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Microalloyed dual phase steels have been prepared by intercritical austenitisation (ICA) treatment of normalized steel at different temperature and time. Water quenching wad carried to obtain different martensite volume fraction (MVF) in DP steels. DP steels and normalized steels have been characterized by optical and scanning electron microscopy, Vickers hardness measurements and tensile properties determination. The effect of MVF and martensite morphology on mechanical properties and fracture behavior of microalloyed dual phase steels have been explained in the present work.

Keywords: dual phase steel, martensite morphology, hardness, tensile strength

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Authors: Abhishek Kumar

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X70 pipeline steel was electrochemically charged with hydrogen for different durations in order to find crack nucleation and propagation sites. After 3 hours charging, suitable regions for crack initiation and propagation were found. These regions were studied by OM, SEM, EDS and later Vicker hardness test was done. The results brought out that HIC cracks nucleated from regions rich of inclusions and further propagated through the segregation area of some elements, such as manganese, carbon, silicon and sulfur. It is worth-mentioning that all these potential sites for crack nucleation and propagation appeared at the centre of cross section of the specimens. Additionally, cracked area has harder phase than the non-cracked area which was confirmed by hardness test.

Keywords: X70 steel, morphology of inclusions, SEM/EDS/OM, simulation, statistical data

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Authors: Naman Solanki, Souraj Mondal

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With the world moving towards online communication, the video datastore has exploded in the past few years. Consequently, it has become crucial to analyse participant’s engagement levels in online communication videos. Engagement prediction of people in videos can be useful in many domains, like education, client meetings, dating, etc. Video-level or frame-level prediction of engagement for a user involves the development of robust models that can capture facial micro-emotions efficiently. For the development of an engagement prediction model, it is necessary to have a widely-accepted standard dataset for engagement analysis. DAiSEE is one of the datasets which consist of in-the-wild data and has a gold standard annotation for engagement prediction. Earlier research done using the DAiSEE dataset involved training and testing standard models like CNN-based models, but the results were not satisfactory according to industry standards. In this paper, a multi-level classification approach has been introduced to create a more robust model for engagement analysis using the DAiSEE dataset. This approach has recorded testing accuracies of 0.638, 0.7728, 0.8195, and 0.866 for predicting boredom level, engagement level, confusion level, and frustration level, respectively.

Keywords: computer vision, engagement prediction, deep learning, multi-level classification

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Authors: Bin Li, Mei Liu

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Arrival time information is a crucial component of advanced public transport system (APTS). The advertisement of arrival time at stops can help reduce the waiting time and anxiety of passengers, and improve the quality of service. In this research, an experiment was conducted to compare the performance on prediction accuracy and precision between the link-based and the path-based historical travel time based model with the automatic vehicle location (AVL) data collected from an actual bus route. The research results show that the path-based model is superior to the link-based model, and achieves the best improvement on peak hours.

Keywords: bus transit, arrival time prediction, link-based, path-based

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Authors: Sohyoung Won, Heebal Kim, Dajeong Lim

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Genomic prediction is an effective way to measure the abilities of livestock for breeding based on genomic estimated breeding values, statistically predicted values from genotype data using best linear unbiased prediction (BLUP). Using haplotypes, clusters of linked single nucleotide polymorphisms (SNPs), as markers instead of individual SNPs can improve the reliability of genomic prediction since the probability of a quantitative trait loci to be in strong linkage disequilibrium (LD) with markers is higher. To efficiently use haplotypes in genomic prediction, finding optimal ways to define haplotypes is needed. In this study, 770K SNP chip data was collected from Hanwoo (Korean cattle) population consisted of 2506 cattle. Haplotypes were first defined in three different ways using 770K SNP chip data: haplotypes were defined based on 1) length of haplotypes (bp), 2) the number of SNPs, and 3) k-medoids clustering by LD. To compare the methods in parallel, haplotypes defined by all methods were set to have comparable sizes; in each method, haplotypes defined to have an average number of 5, 10, 20 or 50 SNPs were tested respectively. A modified GBLUP method using haplotype alleles as predictor variables was implemented for testing the prediction reliability of each haplotype set. Also, conventional genomic BLUP (GBLUP) method, which uses individual SNPs were tested to evaluate the performance of the haplotype sets on genomic prediction. Carcass weight was used as the phenotype for testing. As a result, using haplotypes defined by all three methods showed increased reliability compared to conventional GBLUP. There were not many differences in the reliability between different haplotype defining methods. The reliability of genomic prediction was highest when the average number of SNPs per haplotype was 20 in all three methods, implying that haplotypes including around 20 SNPs can be optimal to use as markers for genomic prediction. When the number of alleles generated by each haplotype defining methods was compared, clustering by LD generated the least number of alleles. Using haplotype alleles for genomic prediction showed better performance, suggesting improved accuracy in genomic selection. The number of predictor variables was decreased when the LD-based method was used while all three haplotype defining methods showed similar performances. This suggests that defining haplotypes based on LD can reduce computational costs and allows efficient prediction. Finding optimal ways to define haplotypes and using the haplotype alleles as markers can provide improved performance and efficiency in genomic prediction.

Keywords: best linear unbiased predictor, genomic prediction, haplotype, linkage disequilibrium

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Authors: Bikis Muhammed, Sehra Sedigh Sarvestani, Ali R. Hurson, Lasanthi Gamage

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Vehicular traffic events have overly complex spatial correlations and temporal interdependencies and are also influenced by environmental events such as weather conditions. To capture these spatial and temporal interdependencies and make more realistic vehicular traffic predictions, graph neural networks (GNN) based traffic prediction models have been extensively utilized due to their capability of capturing non-Euclidean spatial correlation very effectively. However, most of the already existing GNN-based traffic prediction models have some limitations during learning complex and dynamic spatial and temporal patterns due to the following missing factors. First, most GNN-based traffic prediction models have used static distance or sometimes haversine distance mechanisms between spatially separated traffic observations to estimate spatial correlation. Secondly, most GNN-based traffic prediction models have not incorporated environmental events that have a major impact on the normal traffic states. Finally, most of the GNN-based models did not use an attention mechanism to focus on only important traffic observations. The objective of this paper is to study and make real-time vehicular traffic predictions while incorporating the effect of weather conditions. To fill the previously mentioned gaps, our prediction model uses a real-time driving distance between sensors to build a distance matrix or spatial adjacency matrix and capture spatial correlation. In addition, our prediction model considers the effect of six types of weather conditions and has an attention mechanism in both spatial and temporal data aggregation. Our prediction model efficiently captures the spatial and temporal correlation between traffic events, and it relies on the graph attention network (GAT) and Bidirectional bidirectional long short-term memory (Bi-LSTM) plus attention layers and is called GAT-BILSTMA.

Keywords: deep learning, real time prediction, GAT, Bi-LSTM, attention

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Authors: Mohammad Zavid Parvez, Manoranjan Paul

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Epilepsy is a common neurological disorders characterized by sudden recurrent seizures. Electroencephalogram (EEG) is widely used to diagnose possible epileptic seizure. Many research works have been devoted to predict epileptic seizure by analyzing EEG signal. Seizure prediction by analyzing EEG signals are challenging task due to variations of brain signals of different patients. In this paper, we propose a new approach for feature extraction based on phase correlation in EEG signals. In phase correlation, we calculate relative change between two consecutive segments of an EEG signal and then combine the changes with neighboring signals to extract features. These features are then used to classify preictal/ictal and interictal EEG signals for seizure prediction. Experiment results show that the proposed method carries good prediction rate with greater consistence for the benchmark data set in different brain locations compared to the existing state-of-the-art methods.

Keywords: EEG, epilepsy, phase correlation, seizure

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Authors: Pallavi Mane, Nikita Mankar, Shraddha Mazire, Rasika Pashankar

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Content sharing sites are very useful in sharing information and images. However, with the increasing demand of content sharing sites privacy and security concern have also increased. There is need to develop a tool for controlling user access to their shared content. Therefore, we are developing an Adaptive Privacy Policy Prediction (A3P) system which is helpful for users to create privacy settings for their images. We propose the two-level framework which assigns the best available privacy policy for the users images according to users available histories on the site.

Keywords: online information services, prediction, security and protection, web based services

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Authors: Sam Khozama, Ali M. Mayya

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Breast cancer is one of the most common types in women. Early prediction of breast cancer helps physicians detect cancer in its early stages. Big cancer data needs a very powerful tool to analyze and extract predictions. Machine learning and deep learning are two of the most efficient tools for predicting cancer based on textual data. In this study, we developed a fusion model of two machine learning and deep learning models. To obtain the final prediction, Long-Short Term Memory (LSTM) and ensemble learning with hyper parameters optimization are used, and score-level fusion is used. Experiments are done on the Breast Cancer Surveillance Consortium (BCSC) dataset after balancing and grouping the class categories. Five different training scenarios are used, and the tests show that the designed fusion model improved the performance by 3.3% compared to the individual models.

Keywords: machine learning, deep learning, cancer prediction, breast cancer, LSTM, fusion

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Authors: Sara Khamseh, Kambiz Javanruee, Hamid Khorsand

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Plenty of metallic materials are used for biomedical applications like hip joints and screws. Besides, it is reported that metal platforms such as stainless steel show significant deterioration because of wear and friction. The surface of metal substrates has been coated with a variety of multicomponent coatings to prevail these problems. The carbon-based multicomponent coatings such as metal-added amorphous carbon and diamond coatings are crucially important because of their remarkable tribological performance and chemical stability. In the current study, H-D contained Nb: (a-C) multicomponent coatings (H-D: hexagonal diamond, a-C: amorphous carbon) coated on A 304 steel substrates using an unbalanced magnetron (UBM) sputtering system. The effects of Nb and H-D content and ID/IG ratio on microstructure, mechanical and tribological characteristics of (Nb: H-D: a-C) composite coatings were investigated. The results of Raman spectroscopy represented that a-C phase with a Graphite-like structure (GLC with high value of sp2 carbon bonding) is formed, and its domain size increased with increasing Nb content of the coatings. Moreover, the Nb played a catalyst for the formation of the H-D phase. The nanoindentation hardness value of the coatings ranged between ~17 to ~35 GPa and (Nb: H-D: a-C) composite coatings with more H-D content represented higher hardness and plasticity index. It seems that the existence of extra-hard H-D particles straightly increased hardness. The tribological performance of the coatings was evaluated using the pin-on-disc method under the wet environment of SBF (Simulated Body Fluid). The COF value of the (Nb: H-D: a-C) coatings decreased with an increasing ID/IG ratio. The lower coefficient of friction is a result of the lamelliform array of graphitic domains. Also, the wear rate of the coatings decreased with increasing H-D content of the coatings. Based on the literature, a-C coatings with high hardness and H3/E2 ratio represent lower wear rates and better tribological performance. According to the nanoindentation analysis, hardness and H3/E2 ratio of (Nb: H-D: a-C) multicomponent coatings increased with increasing H-D content, which in turn decreased the wear rate of the coatings. The mechanical and tribological potency of (Nb: H-D: a-C) composite coatings on A 304 steel substrates paved the way for the development of innovative advanced coatings to ameliorate the performance of A 304 steel for biomedical applications.

Keywords: COF, mechanical properties, (Nb: H-D: a-C) coatings, wear rate

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Authors: Nagini Sabbineni, Rajini T. V. Kanth, B. V. Kiranmayee

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India’s economy primarily depends on agriculture yield growth and their allied agro industry products. The agriculture yield prediction is the toughest task for agricultural departments across the globe. The agriculture yield depends on various factors. Particularly countries like India, majority of agriculture growth depends on rain water, which is highly unpredictable. Agriculture growth depends on different parameters, namely Water, Nitrogen, Weather, Soil characteristics, Crop rotation, Soil moisture, Surface temperature and Rain water etc. In our paper, lot of Explorative Data Analysis is done and various predictive models were designed. Further various regression models like Linear, Multiple Linear, Non-linear models are tested for the effective prediction or the forecast of the agriculture yield for various crops in Andhra Pradesh and Telangana states.

Keywords: agriculture yield growth, agriculture yield prediction, explorative data analysis, predictive models, regression models

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Authors: Ahmad Saleem

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Ethereum is the second largest crypto currency in blockchain ecosystem. Along with standard transactions, it supports smart contracts and NFT’s. Current research trends are focused on analyzing the overall structure of the network its growth and behavior. Ethereum addresses are anonymous and can be created on fly. The nature of Ethereum network and addresses make it hard to predict their behavior. The activity period of an ethereum address is not much analyzed. Using machine learning we can make early prediction about the disposability of the address. In this paper we analyzed the lifetime of the addresses. We also identified and predicted the disposable addresses using machine learning models and compared the results.

Keywords: blockchain, Ethereum, cryptocurrency, prediction

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Authors: Josef Brychta, Jiri Kratochvil, Marek Pagac

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The group of progressive cutting materials can include non-traditional, emerging and less-used materials that can be an efficient use of cutting their lead to a quantum leap in the field of machining. This is essentially a “superhard” materials (STM) based on polycrystalline diamond (PCD) and polycrystalline cubic boron nitride (PCBN) cutting performance ceramics and development is constantly "perfecting" fine coated cemented carbides. The latter cutting materials are broken down by two parameters, toughness and hardness. A variation of alloying elements is always possible to improve only one of each parameter. Reducing the size of the core on the other hand doing achieves "contradictory" properties, namely to increase both hardness and toughness.

Keywords: grained cutting materials difficult to machine materials, optimum utilization, mechanic, manufacturing

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Authors: M. Dezyani, R. Ezzati belvirdi, M. Shakerian, H. Mirzaei

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The effect of Microfiltration (MF) on proteolysis, hardness, and flavor of Feta cheese during 6 mo of aging was determined. Raw skim milk was microfiltered two-fold in two cheese making trials. In trial 1, four vats of cheese were made in 1 d using unconcentrated milk (1X), 1.26X, 1.51X, and 1.82X Concentration Factors (CF). Casein-(CN)-to-fat ratio was constant among treatments. Proteolysis during cheese aging decreased with increasing CF due to either limitation of substrate availability for chymosin due to low moisture in the nonfat substance (MNFS), inhibition of chymosin activity by high molecular weight milk serum proteins, such as α2-macroglobulin, retained in the cheese or low residual chymosin in the cheese. Hardness of fresh cheese increased, and cheese flavor intensity decreased with increasing CF. In trial 2, the 1X and 1.8X CF were compared directly. Changes made in the cheese making procedure for the 1.8X CF (more chymosin and less cooking) increased the MNFS and made proteolysis during aging more comparable for the 1X and 1.8X cheeses. The significant difference in cheese hardness due to CF in trial 1 was eliminated in trial 2. In a triangle test, panelists could not differentiate between the 1X and 1.8X cheeses. Therefore, increasing chymosin and making the composition of the two cheeses more similar allowed production of aged Fetta cheese from milk concentrated up to 1.8X by MF that was not perceived as different from aged feta cheese produced without MF.

Keywords: feta cheese, microfiltration, concentration factor, proteolysis

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Authors: Pooja Verma, Rajnesh Tyagi, Sunil Mohan

Abstract:

Al-Si alloys are widely used in various components such as liner-less engine blocks, piston, compressor bodies and pumps for automobile sector and aerospace industries due to their excellent combination of properties like low thermal expansion coefficient, low density, excellent wear resistance, high corrosion resistance, excellent cast ability, and high hardness. The low density and high hardness of primary Si phase results in significant reduction in density and improvement in wear resistance of hypereutectic Al-Si alloys. Keeping in view of the industrial importance of the alloys, hypereutectic Al-Si alloys containing 14, 16, 18 and 20 wt. % of Si were prepared in a resistance furnace using adequate amount of deoxidizer and degasser and their erosion behavior was evaluated by conducting tests at impingement angles of 30°, 60°, and 90° with an erodent discharge rate of 7.5 Hz, pressure 1 bar using erosion test rig. Microstructures of the cast alloys were examined using Optical microscopy (OM) and scanning electron microscopy (SEM) and the presence of Si particles was confirmed by x-ray diffractometer (XRD). The mechanical properties and hardness were measured using uniaxial tension tests at a strain rate of 10-3/s and Vickers hardness tester. Microstructures of the alloys and X-ray examination revealed the presence of primary and eutectic Si particles in the shape of cuboids or polyhedral and finer needles. Yield strength (YS), ultimate tensile strength (UTS), and uniform elongation of the hypereutectic Al-Si alloys were observed to increase with increasing content of Si. The optimal strength and ductility was observed for Al-20 wt. % Si alloy which is significantly higher than the Al-14 wt. % Si alloy. The increased hardness and the strength of the alloys with increasing amount of Si has been attributed presence of Si in the solid solution which creates strain, and this strain interacts with dislocations resulting in solid-solution strengthening. The interactions between distributed primary Si particles and dislocations also provide Orowan strengthening leading to increased strength. The steady state erosion rate was found to decrease with increasing angle of impact as well as Si content for all the alloys except at 900 where it was observed to increase with the increase in the Si content. The minimum erosion rate is observed in Al-20 wt. % Si alloy at 300 and 600 impingement angles because of its higher hardness in comparison to other alloys. However, at 90° impingement angle the wear rate for Al-20 wt. % Si alloy is found to be the minimum due to deformation, subsequent cracking and chipping off material.

Keywords: Al-Si alloy, erosion wear, cast alloys, dislocation, strengthening

Procedia PDF Downloads 37

Authors: Ji Chan Kim, Seok Hong Min, Tae Kwon Ha

Abstract:

The effect of Yttrium addition on the microstructure and mechanical properties of Sn-Zn eutectic alloy, which has been attracting intensive focus as a Pb-free solder material, was investigated in this study. Phase equilibrium has been calculated by using FactSage® to evaluate the composition and fraction of equilibrium intermetallic compounds and construct a phase diagram. In the case of Sn-8.8 Zn eutectic alloy, the as-cast microstructure was typical lamellar. With addition of 0.25 wt. %Y, a large amount of pro-eutectic phases have been observed and various YZnx intermetallic compounds were expected to successively form during cooling. Hardness of Sn-8.8 Zn alloy was not affected by Y-addition and both alloys could be rolled by 90% at room temperature.

Keywords: lead-free solder, zn-al-sn alloy, phase equilibrium, rolling, microstructure, hardness

Procedia PDF Downloads 278