Prediction of Diffraction Wave for a Blunt Ship with Forward Speed

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Influence of Bluntness Effect on Green Water Flow in a Regular Wave

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Optimizing end milling parameters for custom 450 stainless steel using ant lion optimization and TOPSIS analysis

The current research examines the effectiveness of cryogenically treated (CT) tungsten carbide cutting inserts on Custom450 stainless steel using multi-objective soft computing approaches. The Taguchi-based L27 orthogonal array was employed in the experiments. During milling operations, cutting force, surface roughness, and cutting temperature were measured at different spindle speeds (rpm), feed rates (mm/min), and constant depths of cut (mm). The surface roughness and chip morphology of the Custom 450 stainless steel machined by cryo-treated (CT) and untreated (UT) cutting tool inserts were compared across various responses to cutting temperature and force. This paper also carried out multi-objective optimization, employing algorithm techniques such as Grasshopper Optimization Algorithm (GHO), Grey Wolf Optimization(GWO), Harmony Search Algorithm(HAS), and Ant line Optimization (ALO). The Multi-objective Taguchi approach and TOPSIS were first used to optimize the machining process parameters (spindle speed, feed rate, and cryogenic treatment) with different performance characteristics. Second, to relate the machining process parameters with the performance characteristics (cutting force, cutting temperature, and surface roughness), a mathematical model was developed using response surface analysis. The created mathematical response model was validated using ANOVA. The results showed that in IGD values of GHO, GWO, HSA and ALO module had 2.5765, 2.4706, 2.3647 and 2.5882 respectively, ALO has the best performance indicator. A Friedman’s test was also conducted, revealing higher resolution with the ALO method than with the HSA, GWO, and GHO methods. The results of the scanning test show that the ALO approach is workable

Effect of deposition temperature on the tribo-mechanical properties of nitrogen doped DLC thin film

The tribomechanical characteristics of diamond-like carbon (DLC) coatings are notably superior to other hard coatings, making them highly desirable for industrial applications. This study focuses on the synthesis of nitrogen-doped DLC (N-DLC) films through chemical vapor deposition (CVD) methods, with an emphasis on varying the deposition temperature. Comprehensive characterization techniques such as atomic force microscopy (AFM), scanning electron microscopy (SEM), and nanoindentation were employed to investigate the morphological and mechanical attributes of these coatings. The thickness of the films, measured using a Dektak profilometer, demonstrated an increase from 1.9 to 2.8 µm as the deposition temperature rose. Nanoindentation testing revealed that the film deposited at 900°C exhibited the highest hardness (H) and modulus of elasticity (E), measuring 21.95 and 208.3 GPa, respectively. Conversely, the film deposited at 1,000°C showed the lowest values, with H and E at 14.23a and 141.9 GPa, respectively. The H/E ratio of the coatings initially rose from 0.096 to 0.106 as the deposition temperature increased from 800°C to 900°C. However, for deposition temperatures exceeding 900°C the H/E ratio began to decline

An efficient chaos-based image encryption technique using bitplane decay and genetic operators

Social networks have greatly expanded in the last ten years the need for sharing multimedia data. However, on open networks such as the Internet, where security is frequently compromised, it is simple for eavesdroppers to approach the actual contents without much difficulty. Researchers have created a variety of encryption methods to strengthen the security of this transmission and make it difficult for eavesdroppers to get genuine data. However, these conventional approaches increase computing costs and communication overhead and do not offer protection against fresh threats. The problems with current algorithms encourage academics to further investigate the subject and suggest new algorithms that are more effective than current methods, that reduce overhead, and which are equipped with features needed by next-generation multimedia networks. In this paper, a genetic operator-based encryption method for multimedia security is proposed. It has been noted that the proposed algorithm produces improved key strength results. The investigations using attacks on data loss, differential assaults, statistical attacks, and brute force attacks show that the encryption technique suggested has improved security performance. It focuses on two techniques, bitplane slicing and followed by block segmentation and scrambling. The suggested method first divides the plaintext picture into several blocks, which is then followed by block swapping done by the genetic operator used to combine the genetic information of two different images to generate new offspring. The key stream is produced from an iterative chaotic map with infinite collapse (ICMIC). Based on a close-loop modulation coupling (CMC) approach, a three-dimensional hyperchaotic ICMIC modulation map is proposed. By using a hybrid model of multidirectional circular permutation with this map, a brand-new colour image encryption algorithm is created. In this approach, a multidirectional circular permutation is used to disrupt the image's pixel placements, and genetic operations are used to replace the pixel values. According to simulation findings and security research, the technique can fend off brute-force, statistical, differential, known-plaintext, and chosen-plaintext assaults, and has a strong key sensitivity.Web of Science2220art. no. 804

The Role of Applied Behaviour Analysing in Zoo Management System for Animals

This study examines the use of Applied Behaviour Analysis (ABA) in animal management systems in zoos with an emphasis on how well it enhances animal wellbeing. The goals of the study were comparing ABA-based approaches to conventional zoo management techniques and evaluating the effect of ABA on stress reduction in captive chimpanzees. Over the course of a year, information was gathered from a chosen group of chimpanzees, including baseline and post-ABA stress levels. Following the use of ABA techniques, the data from Table 1 show a consistent trend among the chimpanzees, suggesting a considerable reduction in stress levels. Strong evidence for the effectiveness of ABA in lowering stress levels in the chimpanzees was given by statistical analysis of the data (Table 2). With a high t-statistic of 8.25, the mean baseline stress level of 7.7 substantially decreased to 3.5 post-ABA treatments (p 0.05), emphasizing the significance of this reduction. With regard to reducing stress, Objective 2 compared ABA-Based and Traditional groups. Compared to the Traditional group, the ABA-Based method had a reduced baseline stress level, according to the findings in Table 3. The ABA-Based group showed a significant reduction in stress levels following ABA sessions, while the Traditional group also showed a reduction in stress levels, albeit to a lesser extent. Both strategies significantly reduced stress, according to statistical analysis (Table 4), with the ABA-Based strategy having a greater degree of statistical significance (p 0.05). This study concludes by offering empirical proof that Applied Behaviour Analysis is a useful method for enhancing animal wellbeing in the context of zoo management. The findings imply that when compared to conventional management techniques, ABA therapies can significantly lower stress levels in caged animals

Machine-learning-assisted prediction of maximum metal recovery from spent zinc-manganese batteries

Spent zinc-manganese batteries contain heavy toxic metals that pose a serious threat to the environment. Recovering these metals is vital not only for industrial use but also for saving the environment. Recycling metal from spent batteries is a complex task. In this study, machine-learning-based predictive models are developed for predicting metal recovery from spent zinc-manganese batteries by studying the energy substrates concentration, pH control of bioleaching media, incubating temperature and pulp density. The main objective of this study is to make a detailed comparison among five machine learning models, namely, linear regression, random forest regression, AdaBoost regression, gradient boosting regression and XG boost regression. All the machine learning models are tuned for optimal hyperparameters. The results from each of the machine learning models are compared using several statistical metrics such as R-2, mean squared error (MSE), mean absolute error (MAE), maximum error and median error. The XG Boost regression model is observed to be the most effective among the tested algorithms.Web of Science105art. no. 103

Accurate estimation of tensile strength of 3D printed parts using machine learning algorithms

Manufacturing processes need optimization. Three-dimensional (3D) printing is not an exception. Consequently, 3D printing process parameters must be accurately calibrated to fabricate objects with desired properties irrespective of their field of application. One of the desired properties of a 3D printed object is its tensile strength. Without predictive models, optimizing the 3D printing process for achieving the desired tensile strength can be a tedious and expensive exercise. This study compares the effectiveness of the following five predictive models (i.e., machine learning algorithms) used to estimate the tensile strength of 3D printed objects: (1) linear regression, (2) random forest regression, (3) AdaBoost regression, (4) gradient boosting regression, and (5) XGBoost regression. First, all the machine learning models are tuned for optimal hyperparameters, which control the learning process of the algorithms. Then, the results from each machine learning model are compared using several statistical metrics such as R-2, mean squared error (MSE), mean absolute error (MAE), maximum error, and median error. The XGBoost regression model is the most effective among the tested algorithms. It is observed that the five tested algorithms can be ranked as XG boost > gradient boost > AdaBoost > random forest > linear regression.Web of Science106art. no. 115

Thermo-mechanical behavior of aluminum matrix nano-composite automobile disc brake rotor using finite element method

Analysis of mechanical and thermal behaviors during braking has become an increasingly important issue in many transport sectors for different modes of transportation. Brake failure generated during braking is a complex phenomenon confronting automobile manufacturers and designers. During braking, kinetic energy is transferred to thermal energy, resulting in the intense heating of disc brake rotors that increases proportionally with vehicle speed, mass, and braking frequency. It is essential to look into and improve strategies to make versatile, thermally resistant, lightweight, high-performance discs. As a result, this study uses the finite element method to conduct a thermo-mechanical analysis of aluminum alloy and aluminum matrix nano-composite disc brake rotors to address the abovementioned issues. The FEA method is used for the thermo-mechanical analysis of AMNCs for vented disc brake rotor during emergency braking at 70 km/h. From the results obtained, aluminum base metal matrix nano-composites have an excellent strength-to-weight ratio when used as disc brake rotor materials, significantly improving the discs' thermal and mechanical performance. From the result of transient thermal analysis, the maximum value of heat flux obtained for aluminum alloy disc is about 8 W/mm(2), whereas for AMNCs, the value is increased to 16.28 W/mm(2). The result from static analysis shows that the maximum deformation observed is 0.19 mm for aluminum alloy disc and 0.05 mm for AMNCs disc. In addition, the maximum von Mises stress value of AMNC disc is about 184 MPa. The maximum von Mises stress value of aluminum alloy disc is about 180 MPa. Therefore, according to the results, the proposed aluminum base metal matrix nano-composites are valid for replacing existing materials for disc brake rotor applications.Web of Science1517art. no. 607

Machining of Custom-450 grade stainless steel using TiAlSiN-coated tungsten carbide tool inserts

Turning operations using single-point cutting tools have been one of the earliest and most used methods for cutting metal. It has been widely studied for cutting forces and workpiece surface roughness to affect turning operations. When cutting metal, the cutting tool needs to be tougher than the workpiece so it can resist high temperatures and wear while the operation is conducted. The me chanical qualities of martensitic stainless steel (MSS) grade Custom-450 can be significantly enhanced by heat treatment processes, which also provide it with an outstanding corrosion-resistance material. It has excellent resistance to rusting and pitting in a saltwater environment. Nuclear power reactors, screens for the pulp and paper sector, chemical processing, and power generation are just a few in dustries that require Custom-450 grade steel. To increase the workpiece’s machinability, dimensional precision, and appealing surface finish, the cutting tool industries have recently demonstrated a great interest in developing hard coatings and cutting tool technology. In the present study, Custom-450 grade stainless steel was used for machining (turning operation), using a tungsten carbide tool insert coated with TiAlSiN using the physical vapor deposition (PVD) method. The machining parameters such as the speed, feed, and depth of cut (DOC) were varied Surface roughness and various forces (cutting force, thrust force, and feed force) were evaluated by varying these three parameters. The depth of cut is the main factor affecting the surface roughness. More plastic deformation may lead to a rougher surface as a result. The tungsten carbide insert wear decreased with an increase in the cutting speed. An increase in feed considerably accelerates the tool wear of the inserts. As the depth of cut grows, the likelihood of tool wear also increases. The depth of cut, however, has a greater effect on tool wear than anything else. Therefore, the surface roughness in the sample is reduced as the cutting speed is increased.Web of Science114art. no. 103