Search results for: hardness prediction

Authors: Amine Hamdi, Sidi Mohammed Merghache, Brahim Fernini

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During abrasive machining of hard steels by belt grinding, the finished surface texture is influenced by the pressure between the abrasive belt and the workpiece; this pressure is the force applied by the contact roller on the workpiece. Therefore, the contact roller has an important role and has a direct impact on process efficiency. The objective of this article is to study and compare the influence of different contact rollers on the belt ground surface texture. The quality of the surface texture is characterized by eight roughness parameters (Ra, Rz, Rp, Rv, Rsk, Rku, Rsm, and Rdq) and five parameters of the bearing area curve (Rpk, Rk, Rvk, Mr1, and Mr2). The results of the experimental tests indicate a better surface texture obtained by the PA 6 polyamide roller (hardness 60 Shore D) compared to that obtained with other rollers of the same hardness or of different hardness. Simultaneously, optimum medium pressure between the belt and the workpiece allows chip removal without fracturing the abrasive grains. This generates a good surface texture.

Keywords: belt grinding, contact roller, pressure, abrasive belt, surface texture

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Authors: Li Yu, Chenlu Jiao, Hongrun Xin, Yan Wang, Kaiyang Wang

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In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry.

Keywords: deep learning, convolutional neural network, LSTM, housing prediction

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Authors: Khiaira S. Hassan, Abbas S. Alwan, Muna K. Abbass

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This research aims to study the effect of the liquid nitriding and shot peening on the hardness, surface roughness, residual stress, microstructure and corrosion behavior of austenite stainless steel 316 L. Chemical surface heat treatment by liquid nitriding process was carried out at 500 °C for 1 h and followed by shot peening with using ball steel diameter of 1.25 mm in different exposure time of 10 and 20 min. Electrochemical corrosion test was applied in sea water (3.5% NaCl solution) by using potentostat instrument. The results showed that the nitride layer consists of a compound layer (white layer) and diffusion zone immediately below the alloy layer. It has been found that the mechanical treatment (shot peening) has led to the formation of compressive residual stresses in layer surface that increased the hardness of stainless steel surface. All surface treatment (nitriding and shot peening) processes have led to the formation of carbide of CrN in hard surface layer. It was shown that both processes caused an increase in surface hardness and roughness which increases with shot peening time. Also, the corrosion results showed that the liquid nitriding and shot peening processes increase the corrosion rate to values more than that of not treated stainless steel.

Keywords: stainless steel 316L, shot peening, nitriding, corrosion, hardness

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Authors: Josef Sepitka, Petr Vlcak, Tomas Horazdovsky, Vratislav Perina

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An ion implantation technique was used for designing the surface area of a titanium alloy and for irradiation-enhanced hardening of the surface. The Ti6Al4V alloy was treated by nitrogen ion implantation at fluences of 2·10¹⁷ and 4·10¹⁷ cm^-2 and at ion energy 90 keV. The depth distribution of the nitrogen was investigated by Rutherford Backscattering Spectroscopy. The gradient of mechanical properties was investigated by nanoindentation. The continuous measurement mode was used to obtain depth profiles of the indentation hardness and the reduced storage modulus of the modified surface area. The reduced storage modulus and the hardness increase with increasing fluence. Increased fluence shifts the peak of the mechanical properties as well as the peak of nitrogen concentration towards to the surface. This effect suggests a direct relationship between mechanical properties and nitrogen distribution.

Keywords: nitrogen ion implantation, titanium-based nanolayer, storage modulus, hardness, microstructure

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Authors: Alaeddine Kaouka

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The properties and characterization of Ti-6Al-4V alloys with different contents of Mo were investigated. Microstructure characterization and hardness are considered. The alloy structure was characterized by X-ray diffraction, SEM and optical microscopy. The results showed that the addition of Mo stabilized the β-phase in the treated solution condition. The Mo element added to titanium alloys changes the lattice parameters of phases. Microstructural observations indicate an obvious reduction in the prior grain size. The hardness has increased with the increase in β-phase stability, while Young’s modulus and ductility have decreased.

Keywords: characterization, mechanical properties, molybdenum, titanium alloy

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Authors: Dimitrios Triantakonstantis, Demetris Stathakis

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Urban areas have been expanded throughout the globe. Monitoring and modeling urban growth have become a necessity for a sustainable urban planning and decision making. Urban prediction models are important tools for analyzing the causes and consequences of urban land use dynamics. The objective of this research paper is to analyze and model the urban change, which has been occurred from 1990 to 2000 using CORINE land cover maps. The model was developed using drivers of urban changes (such as road distance, slope, etc.) under an Artificial Neural Network modeling approach. Validation was achieved using a prediction map for 2006 which was compared with a real map of Urban Atlas of 2006. The accuracy produced a Kappa index of agreement of 0,639 and a value of Cramer's V of 0,648. These encouraging results indicate the importance of the developed urban growth prediction model which using a set of available common biophysical drivers could serve as a management tool for the assessment of urban change.

Keywords: artificial neural networks, CORINE, urban atlas, urban growth prediction

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Authors: Reham K. El Sawah, N. S. M. El-Tayeb

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This study aims to produce a polymer matrix composite reinforced with Al₂O₃ nanoparticles in order to enhance the mechanical properties of PMMA. The composite was fabricated via Friction stir processing to ensure homogenous dispersion of Al₂O₃ nanoparticles in the polymer, and the processing was submerged to prevent the sputtering of nanoparticles. The surface quality, microstructure, impact energy and hardness of the prepared samples were investigated. Good surface quality and dispersion of nanoparticles were attained through employing sufficient processing conditions. The experimental results indicated that as the percentage of nanoparticles increased, the impact energy and hardness increased, reaching 2 kJ/m2 and 14.7 HV at a nanoparticle concentration of 25%, which means that the toughness and the hardness of the polymer-ceramic produced composite is higher than unprocessed PMMA by 66% and 33% respectively.

Keywords: friction stir processing, polymer matrix nanocomposite, mechanical properties, microstructure

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Authors: Abdull Halim

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The Standard Penetration Test (SPT-N) is the most common in situ test for soil investigations. The Shearing Seismic Standard Penetration Test (SSPT-N), on the other hand, is a new method using shearing wave with propagation exponent equation between the shearing wave, Vs., and hardness, N values without any need for borehole data. Due to the fast and accurate results that can be obtained, the SSPT has found many applications such as in the field rectification buried pipe line, the acid tank settlement and foundation design analyses, and the quality control assessment. Many geotechnical regimes and properties have attempted to correlate both the SSPT and the SPT-N values. Various foundation design methods have been developed based on the outcomes of these tests. Hence, it is pertinent to correlate these tests so that either one of the test can be used in the absence of the other, especially for preliminary evaluation and design purposes. The primary purpose of this study was to investigate the relationship between the SSPT-N and SPT-N values for different types of cohesive soil in Peninsular Malaysia. Data were collected from four different sites, and the correlations were established between the hardness N values, principal stress-strain Mohr circle curve, cohesion, friction angle and vertical effective stress. A positive exponent relationship was found between the shearing wave, sVs., and the hardness N values of the soil. In general, the SSPT-N value was slightly lower than the SPT-N value due to the upper limit boundary of the soil layer.

Keywords: InsituSoil determination; shearing wave; hardness; correlation, SSPT-N, SPT-N

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Authors: Jeet Vijay Sah, Alphonsa Joseph, Pravin Kumari Dwivedi, Ghanshyam Jhala, Subroto Mukherjee

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AISI 304 is known for its corrosion resistance which comes from Cr that forms passive Cr₂O₃ on the surface. But its poor hardness makes it unsuitable for applications where the steel also requires high wear resistance. This can be improved by surface hardening using nitrocarburizing processes, which form ε-Fe2-3N, γ’-Fe4N, nitrides, and carbides of Cr and Fe on the surface and subsurface. These formed phases give the surface greater hardness, but the corrosion resistance drops because of the lack of Cr2O3 passivation as a result. To overcome this problem, plasma nitrocarburizing processes are being developed where the process temperatures are kept below 723 K to avoid Cr-N precipitation. In the presented work, low-temperature pulsed-DC plasma nitrocarburizing utilizing a discharge of N₂-H₂-C₂H₂ at 500 Pa with varying N₂:H₂ ratios was conducted on AISI 304 samples at 673 K. The process durations were also varied, and the samples were characterized by microindentation using Vicker’s hardness tester, corrosion resistances were established from electrochemical impedance studies, and corrosion potentials and corrosion currents were obtained by potentiodynamic polarization testing. XRD revealed S-phase, which is a supersaturated solid solution of N and C in the γ phase. The S-phase was observed to be composed of the expanded phases of γ; γN, γC, and γ’N and ε’N phases. Significant improvement in surface hardness was achieved after every process, which is attributed to the S-phase. Corrosion resistance was also found to improve after the processes. The samples were also characterized by XPS, SEM, and GDOES.

Keywords: AISI 304, surface engineering, nitrocarburizing, S-phase

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Authors: P. S. Jagadeesh Kumar, S. Meenakshi Sundaram, Wenli Hu, Yang Yung

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In 3D video games, the dominance of production is unceasingly growing with a protruding level of affordability in terms of budget. Afterward, the automation of speech-lip synchronization technique is customarily onerous and has advanced a critical research subject in virtual reality based 3D video games. This paper presents one of these automatic tools, precisely riveted on the synchronization of the speech and the lip movement of the game characters. A robust and precise speech recognition segment that systematized with Algebraic Code Excited Linear Prediction method is developed which unconventionally delivers lip sync results. The Algebraic Code Excited Linear Prediction algorithm is constructed on that used in code-excited linear prediction, but Algebraic Code Excited Linear Prediction codebooks have an explicit algebraic structure levied upon them. This affords a quicker substitute to the software enactments of lip sync algorithms and thus advances the superiority of service factors abridged production cost.

Keywords: algebraic code excited linear prediction, speech-lip synchronization, video games, virtual reality

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Authors: Philip Baillie, Stuart W. Campbell, Alexander M. Galloway, Stephen R. Cater, Norman A. McPherson

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This study compared the mechanical and microstructural properties produced during friction stir welding(FSW) of S275 structural steel in air and underwater. Post weld tests assessed the tensile strength, micro-hardness, distortion, Charpy impact toughness and fatigue performance in each case. The study showed that there was no significant difference in the strength, hardness or fatigue life of the air and underwater specimens. However, Charpy impact toughness was shown to decrease for the underwater specimens and was attributed to a lower degree of recrystallization caused by the higher rate of heat loss experienced when welding underwater. Reduced angular and longitudinal distortion was observed in the underwater welded plate compared to the plate welded in air.

Keywords: Charpy impact toughness, distortion, fatigue, friction stir welding(FSW), micro-hardness, underwater

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Authors: Pradeep Singh, Shrish Verma

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Software fault prediction models are created by using the source code, processed metrics from the same or previous version of code and related fault data. Some company do not store and keep track of all artifacts which are required for software fault prediction. To construct fault prediction model for such company, the training data from the other projects can be one potential solution. The earlier we predict the fault the less cost it requires to correct. The training data consists of metrics data and related fault data at function/module level. This paper investigates fault predictions at early stage using the cross-project data focusing on the design metrics. In this study, empirical analysis is carried out to validate design metrics for cross project fault prediction. The machine learning techniques used for evaluation is Naïve Bayes. The design phase metrics of other projects can be used as initial guideline for the projects where no previous fault data is available. We analyze seven data sets from NASA Metrics Data Program which offer design as well as code metrics. Overall, the results of cross project is comparable to the within company data learning.

Keywords: software metrics, fault prediction, cross project, within project.

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Authors: Haozhe Xiang

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With the rise of the Internet of Things era, intelligent products are gradually integrating into people's lives. Pedestrian trajectory prediction has become a key issue, which is crucial for the motion path planning of intelligent agents such as autonomous vehicles, robots, and drones. In the current technological context, deep learning technology is becoming increasingly sophisticated and gradually replacing traditional models. The pedestrian trajectory prediction algorithm combining neural networks and attention mechanisms has significantly improved prediction accuracy. Based on in-depth research on deep learning and pedestrian trajectory prediction algorithms, this article focuses on physical environment modeling and learning of historical trajectory time dependence. At the same time, social interaction between pedestrians and scene interaction between pedestrians and the environment were handled. An improved pedestrian trajectory prediction algorithm is proposed by analyzing the existing model architecture. With the help of these improvements, acceptable predicted trajectories were successfully obtained. Experiments on public datasets have demonstrated the algorithm's effectiveness and achieved acceptable results.

Keywords: deep learning, graph convolutional network, attention mechanism, LSTM

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Authors: Neriman Ozada, Ebrahim Karamian, Amirsalar Khandan, Sina Ghafoorpoor Yazdi

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Natural hydroxyapatite, NHA, coatings on the surface of 316 L stainless steel implants has been widely employed in order to achieve better osteoconductivity. For coating, the plasma spraying method is generally used because they ensure adhesion between the coating and the 316 L stainless steel (SS) surface. Some compounds such as zircon (ZrSiO4) is employed as an additive in an attempt to improve HA’s mechanical properties such as wear resistance and hardness. In this study wear resistance has been carried out in different chemical compositions of coating. Therefore, nanocomposites based on NHA containing of 0 wt.%, 5 wt.%, 10 wt.%, and 15 wt.% of zircon were used as a coating on the SS implants. The samples consisted of NHA, derived from calf heated at 850 °C for 3 h. The composite mixture was coated on SS by plasma spray method. The results were estimated using the scanning electron microscopy (SEM), X-ray diffraction (XRD) techniques were utilized to characterize the shape and size of NHA powder. Disc wear test and Vickers hardness were utilized to characterize the coated nanocomposite samples. The prepared NHA powder had nano-scale morphological structure with the mean crystallite size of 30-50 nm in diameter. The wear resistance are almost 320, 380, 415, and 395 m/g and hardness are approximately 376, 391, 420, 410 VHN in ceramic composite materials containing ZrSiO4. The results have been shown that the best wear resistance and hardness occurred in the sample coated by NHA/ZrSiO4 containing of 10 wt.% of zircon.

Keywords: zircon, 316 L stainless steel, wear resistance, orthopedic applications, plasma spray

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Authors: Dalin Si, Azizan Aziz, Bertrand Lasternas

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To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control.

Keywords: building energy prediction, data mining, demand response, electricity market

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Authors: Yong-Il Lee, Do-Yeon Hwang, Won-Seog Jeong, Duckshin Park

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Recently, because of the high-speed trains forced ventilation, it is important to control the ventilation. The ventilation is for controlling various contaminants, temperature, and humidity. The high-speed train route is straight to a destination having a high speed. And there are many mountainous areas in Korea. So, tunnel rate is higher then other country. KTX HVAC block off the outdoor air, when entering tunnel. So the high tunnel rate is an effect of ventilation in the KTX cabin. It is important to reduction rate in CO2 concentration prediction. To meet the air quality of the public transport vehicles recommend standards, the KTX cabin of CO2 concentration should be managed. In this study, the concentration change was predicted by CO2 prediction simulation in route to be opened.

Keywords: CO2 prediction, KTX, ventilation, infrastructure and transportation engineering

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Authors: Katawut Kaewbanjong

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We analyzed a volume of data and found significant user satisfaction in software project factors. A statistical significance analysis (logistic regression) and collinearity analysis determined the significance factors from a group of 71 pre-defined factors from 191 software projects in ISBSG Release 12. The eight prediction models used for testing the prediction potential of these factors were Neural network, k-NN, Naïve Bayes, Random forest, Decision tree, Gradient boosted tree, linear regression and logistic regression prediction model. Fifteen pre-defined factors were truly significant in predicting user satisfaction, and they provided 82.71% prediction accuracy when used with a neural network prediction model. These factors were client-server, personnel changes, total defects delivered, project inactive time, industry sector, application type, development type, how methodology was acquired, development techniques, decision making process, intended market, size estimate approach, size estimate method, cost recording method, and effort estimate method. These findings may benefit software development managers considerably.

Keywords: prediction model, statistical analysis, software project, user satisfaction factor

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Authors: K. Petcharaporn, S. Kumchoo

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The acidity (citric acid) is one of the chemical contents that can refer to the internal quality and the maturity index of tomato. The titratable acidity (%TA) can be predicted by a non-destructive method prediction by using the transmittance short wavelength (SW-NIR). Spectroscopy in the wavelength range between 665-955 nm. The set of 167 tomato samples divided into groups of 117 tomatoes sample for training set and 50 tomatoes sample for test set were used to establish the calibration model to predict and measure %TA by partial least squares regression (PLSR) technique. The spectra were pretreated with MSC pretreatment and it gave the optimal result for calibration model as (R = 0.92, RMSEC = 0.03%) and this model obtained high accuracy result to use for %TA prediction in test set as (R = 0.81, RMSEP = 0.05%). From the result of prediction in test set shown that the transmittance SW-NIR spectroscopy technique can be used for a non-destructive method for %TA prediction of tomatoes.

Keywords: tomato, quality, prediction, transmittance, titratable acidity, citric acid

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Authors: Venkat S. Somayajula

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Ground surface temperature history prediction model plays a vital role in determining standards for international nuclear waste management. International standards for borehole based nuclear waste disposal require paleoclimate cycle predictions on scale of a million forward years for the place of waste disposal. This research focuses on developing a paleoclimate cycle prediction model using Bayesian long-short term memory (LSTM) neural architecture operated on accumulated borehole temperature history data. Bayesian models have been previously used for paleoclimate cycle prediction based on Monte-Carlo weight method, but due to limitations pertaining model coupling with certain other prediction networks, Bayesian models in past couldn’t accommodate prediction cycle’s over 1000 years. LSTM has provided frontier to couple developed models with other prediction networks with ease. Paleoclimate cycle developed using this process will be trained on existing borehole data and then will be coupled to surface temperature history prediction networks which give endpoints for backpropagation of LSTM network and optimize the cycle of prediction for larger prediction time scales. Trained LSTM will be tested on past data for validation and then propagated for forward prediction of temperatures at borehole locations. This research will be beneficial for study pertaining to nuclear waste management, anthropological cycle predictions and geophysical features

Keywords: Bayesian long-short term memory neural network, borehole temperature, ground surface temperature history, paleoclimate cycle

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Authors: Ratchanon Boonachathong, Bordin Kaewnok, Suksun Amornraksa

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Rigid polyurethane foam (RPUF) used for imitation wood is typically prepared by using 1-Dichloro-1-fluoroethane (HCFC-141b) as a blowing agent. However, this chemical is a hydrofluorocarbon which severely causes ozone depletion to the atmosphere. In this work, a more environmental-friendly RPUF was prepared by using distilled water and cyclopentane (CP) as alternative blowing agent. Several properties of the prepared RPUF were investigated and measured such as density (kg/m³), surface hardness (shore D), and glass transition temperature (°C). It was found that when the amount of the blowing agents decreased, the foam density is increased as well as the surface hardness and glass transition temperature. The results showed that the proper amount of water and cylopentane blowing agent is around 0.3–1.2% and 0.5-1.3% respectively. And the new RPUF produced has a good potential to substitute for a conventional RPUF.

Keywords: blowing agent, cyclopentane co-blown, imitation wood, rigid polyurethane foam, surface hardness

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Authors: Soha A. Bahanshal, Byung G. Kim

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Identification of patients at high risk for hospital readmission is of crucial importance for quality health care and cost reduction. Predicting hospital readmissions among diabetic patients has been of great interest to many researchers and health decision makers. We build a prediction model to predict hospital readmission for diabetic patients within 30 days of discharge. The core of the prediction model is a modified k Nearest Neighbor called Hybrid Fuzzy Weighted k Nearest Neighbor algorithm. The prediction is performed on a patient dataset which consists of more than 70,000 patients with 50 attributes. We applied data preprocessing using different techniques in order to handle data imbalance and to fuzzify the data to suit the prediction algorithm. The model so far achieved classification accuracy of 80% compared to other models that only use k Nearest Neighbor.

Keywords: machine learning, prediction, classification, hybrid fuzzy weighted k-nearest neighbor, diabetic hospital readmission

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Authors: M. Unaldi, R. Kus

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Brake pads are important parts of the braking system and they are made of different materials. Use of asbestos fibre can cause health risks. The goal of this study is to determine the effect of ecological brake pad samples which are produced under different compaction pressure values and mixture ratios by using miscanthus as reinforcement component on the density, hardness, wear rate and compression strength properties, and friction coefficients changes of ecological brake pad samples. Miscanthus powder, cashew powder, alumina powder, phenolic resin powder, and calcite powder mixtures were used to produce ecological brake pad samples. The physical properties of the brake pad samples produced under different mixture ratios and compaction pressures values were determined to assign their effects on them by using Taguchi experimental design. Mixture ratios and compaction pressures values were chosen as the factors with three-levels. Experiments are conducted to L₉(3⁴) Taguchi orthogonal array design. The results showed that hardness value is very much affected both compaction pressure values and mixture ratios than the other physical properties. When reinforcing component ratio within the mixture and compaction pressure value is increased, hardness and compression strength values of the all samples are also increased. All test results taking into account, the ideal compaction value for used components and mixture ratios were determined as 200 MPa.

Keywords: brake pad, eco-friendly materials, hardness, Miscanthus, Taguchi method

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Authors: M. Gangadharappa, M. Ravi Kumar, H. N. Reddappa

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The mechanical properties and morphological analysis of Al6061-Red mud particulate composites were investigated. The compositions of the composite include a matrix of Al6061 and the red mud particles of 53-75 micron size as reinforcement ranging from 0% to 12% at an interval of 2%. Stir casting technique was used to fabricate Al6061-Red mud composites. Density measurement, estimation of percentage porosity, tensile properties, fracture toughness, hardness value, impact energy, percentage elongation and percentage reduction in area. Further, the microstructures and SEM examinations were investigated to characterize the composites produced. The result shows that a uniform dispersion of the red mud particles along the grain boundaries of the Al6061 alloy. The tensile strength and hardness values increases with the addition of Red mud particles, but there is a slight decrease in the impact energy values, values of percentage elongation and percentage reduction in area as the reinforcement increases. From these results of investigation, we concluded that the red mud, an industrial waste can be used to enhance the properties of Al6061 alloy for engineering applications.

Keywords: Al6061, red mud, tensile strength, hardness and microstructures

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Authors: Stepan Kuchar, Martin Golasowski, Radim Vavrik, Michal Podhoranyi, Boris Sir, Jan Martinovic

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The main goal of this article is to describe the online flood monitoring and prediction system Floreon+ primarily developed for the Moravian-Silesian region in the Czech Republic and the basic process it uses for running automatic rainfall-runoff and hydrodynamic simulations along with their calibration and uncertainty modeling. It takes a long time to execute such process sequentially, which is not acceptable in the online scenario, so the use of high-performance computing environment is proposed for all parts of the process to shorten their duration. Finally, a case study on the Ostravice river catchment is presented that shows actual durations and their gain from the parallel implementation.

Keywords: flood prediction process, high performance computing, online flood prediction system, parallelization

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Authors: Abbas Al-Refaie

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This paper aims at improving the performance of the tableting process using statistical quality control and fuzzy goal programming. The tableting process was studied. Statistical control tools were used to characterize the existing process for three critical responses including the averages of a tablet’s weight, hardness, and thickness. At initial process factor settings, the estimated process capability index values for the tablet’s averages of weight, hardness, and thickness were 0.58, 3.36, and 0.88, respectively. The L₉ array was utilized to provide experimentation design. Fuzzy goal programming was then employed to find the combination of optimal factor settings. Optimization results showed that the process capability index values for a tablet’s averages of weight, hardness, and thickness were improved to 1.03, 4.42, and 1.42, respectively. Such improvements resulted in significant savings in quality and production costs.

Keywords: fuzzy goal programming, control charts, process capability, tablet optimization

Procedia PDF Downloads 239

Authors: Suriya

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Rapid socio-economic development and urbanization have led to an increasingly serious air pollution problem in Ulaanbaatar (UB), the capital of Mongolia. PM₂.₅ pollution has become the most pressing aspect of UB air pollution. Therefore, monitoring and predicting PM₂.₅ concentration in UB is of great significance for the health of the local people and environmental management. As of yet, very few studies have used models to predict PM₂.₅ concentrations in UB. Using data from 0:00 on June 1, 2018, to 23:00 on April 30, 2020, we proposed two deep learning models based on Bayesian-optimized LSTM (Bayes-LSTM) and CNN-LSTM. We utilized hourly observed data, including Himawari8 (H8) aerosol optical depth (AOD), meteorology, and PM₂.₅ concentration, as input for the prediction of PM₂.₅ concentrations. The correlation strengths between meteorology, AOD, and PM₂.₅ were analyzed using the gray correlation analysis method; the comparison of the performance improvement of the model by using the AOD input value was tested, and the performance of these models was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The prediction accuracies of Bayes-LSTM and CNN-LSTM deep learning models were both improved when AOD was included as an input parameter. Improvement of the prediction accuracy of the CNN-LSTM model was particularly enhanced in the non-heating season; in the heating season, the prediction accuracy of the Bayes-LSTM model slightly improved, while the prediction accuracy of the CNN-LSTM model slightly decreased. We propose two novel deep learning models for PM₂.₅ concentration prediction in UB, Bayes-LSTM, and CNN-LSTM deep learning models. Pioneering the use of AOD data from H8 and demonstrating the inclusion of AOD input data improves the performance of our two proposed deep learning models.

Keywords: deep learning, AOD, PM2.5, prediction, Ulaanbaatar

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Authors: A. Majidian

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The life prediction of thermal power plant components is necessary to prevent the unexpected outages, optimize maintenance tasks in periodic overhauls and plan inspection tasks with their schedules. One of the main critical components in a power plant is condenser because its failure can affect many other components which are positioned in downstream of condenser. This paper deals with factors affecting life of condenser. Failure rates dependency vs. these factors has been investigated using Artificial Neural Network (ANN) and fuzzy logic algorithms. These algorithms have shown their capabilities as dynamic tools to evaluate life prediction of power plant equipments.

Keywords: life prediction, condenser tube, neural network, fuzzy logic

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Authors: Young Sik Kim, Tae Kwon Ha

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Effect of Zn addition on the microstructure and mechanical properties of Mg-Zn alloys with Zn contents from 6 to 10 weight percent was investigated in this study. Through calculation of phase equilibria of Mg-Zn alloys, carried out by using FactSage® and FTLite database, solution treatment temperature was decided as temperatures from 300 to 400oC, where supersaturated solid solution can be obtained. Solid solution treatment of Mg-Zn alloys was successfully conducted at 380oC and supersaturated microstructure with all beta phase resolved into matrix was obtained. After solution treatment, hot rolling was successfully conducted by reduction of 60%. Compression and tension tests were carried out at room temperature on the samples as-cast, solution treated, hot-rolled and recrystallized after rolling. After solid solution treatment, each alloy was annealed at temperatures of 180 and 200oC for time intervals from 1 min to 48 hrs and hardness of each condition was measured by micro-Vickers method. Peak aging conditions were deduced as at the temperature of 200oC for 10 hrs. By addition of Zn by 10 weight percent, hardness and strength were enhanced.

Keywords: Mg-Zn alloy, heat treatment, microstructure, mechanical properties, hardness

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Authors: Liqiang Gong, Hanguang Fu

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In order to further improve the wear resistance of Hypereutectic High Chromium Cast iron (HHCCI), the effects of different Cu contents on the microstructure and properties of HHCCI were systematically studied. It was found that with the increase of Cu content, the carbide size was refined, and the increase of Cu content led to the increase of austenite and the decrease of hardness in as-cast HHCCI. After heat treatment at 1050 °C, the hardness of HHCCI increased significantly compared with as-cast. And with the increase of Cu content, the hardness of HHCCI increased first and then decreased, and the hardness was the highest when 0.5 wt.% Cu was added. The increase of copper content promotes the precipitation of secondary carbides and makes the interface between α-Fe and M23C6-type secondary carbides a semi-coherent boundary. With the increase of Cu content, the wear loss of HHCCI decreased after heat treatment at 1050 °C, and the wear resistance improved. When the Cu content increased to 1.0 wt.%, the wear resistance of HHCCI was the best, which was 2.6 times that of copper-free HHCCI. The continued increase of copper content has no obvious effect on the wear resistance of HHCCI. In addition, a small amount of Cu tends to adsorb on the (0001) preferential growth surface of M₇C₃-type carbides, thereby refining the carbides. From the First-principles calculations, the solid solution strengthening effect of Cu on the matrix and the adsorption and refinement of carbides were revealed, and the influence mechanism on the wear resistance of HHCCI was characterized.

Keywords: hypereutectic high chromium cast iron, cu alloying, carbides, wear resistance, first-principles calculations

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Authors: Dong Bok Lee, Min Jung Kim

Abstract:

The commercially available titanium alloy, Ti-6Al-4V, was oxynitrided in the deoxygenated nitrogen gas at high temperatures followed by cooling in oxygen-containing nitrogen in order to analyze the influence of oxynitriding parameters on the phase modification, hardness, and the microstructural evolution of the oxynitrided coating. The surface microhardness of the oxynitrided alloy increased due to the strengthening effect of the formed titanium oxynitrides, TiN_xO_y. The maximum microhardness was obtained, when TiN_xO_y had near equiatomic composition of nitrogen and oxygen. It could be attained under the optimum oxygen partial pressure and temperature-time condition.

Keywords: titanium alloy, oxynitriding, gas diffusion, surface treatment

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