Characterization of Laser-Cladded AISI 420 Martensitic Stainless Steel for Additive Manufacturing Applications

Laser cladding is an additive manufacturing (AM) process that uses lasers to melt and deposit metallic powders in layer by layer to coat a substrate or to build three dimensional object. However, the AM industry encounters problems in handling residual stresses in the cladded parts or coating that lead to high hardness and distortion. Also, anisotropic properties developed in the laser-cladded AM parts are a challenge to use them as a functional component. This study aims to understand those problems with the laser-cladding AM process using AISI 420 martensitic stainless steel (MSS) powder in a coaxial direct powder deposition method. Primarily, this study focuses on the effect of process parameters, microstructural evolution, and associated residual stress development in the single bead of laser-cladded 420 MSS. Subsequently, the study was expanded to analyze the mechanical behavior of additive manufactured 3D samples using systematic approaches with X-ray diffraction, scanning and transmission electron microscopy (SEM/TEM), electron backscattered diffraction (EBSD) and MTS mechanical testing frame. This study revealed that laser speed has the most significant effect on the microhardness, while the powder feed rate has the most significant effect on the bead geometry. A detailed TEM study discovered various morphologies of martensitic phases that explained the reason behind the development of residual stress throughout the three zones, such as bead zone (BZ), dilution zone (DZ), and heat affected zone (HAZ) in a single bead clad. A high profile tensile residual stress (310–486 MPa) was observed in the upper BZ, while compressive stress (420–1000 MPa) was seen in the rest of the BZ and the DZ. This laser-cladded stainless steel vi showed a ~16% increase in yield strength (YS ~ 521 MPa), ~ 63% increase in tensile strength (TS ~ 1774 MPa), and a ~ 22% increase in ductility in terms of percentage of area reduction when compared with a similar 420 commercial grade MSS (YS - 483 MPa, TS - 1087 MPa), in the rolling direction with pre-hardened condition. The study showed that a post-cladding heat treatment at 565 °C for an hour reduced the tensile residual stress substantially in a single bead clad. A similar heat treatment also improved the fracture mode of 3D AM sample from brittle to ductile fracture and changed the anisotropic properties of the as-cladded sample in the transverse direction. This indicated that for design purposes, a simple post-cladding heat treatment (at 565 °C for an hour) is very important to minimize the anisotropy in the mechanical properties of as-cladded transverse sample. Also, it showed that a parts building technique with 30° angle to the base improved the ultimate tensile strength and partially eliminated the directionality issue. These findings could be important information for the designers with respect to “design for AM strategies.” It is expected that the above findings will be useful for the laser-based additive manufacturing application of AISI 420 martensitic stainless steel in designing functional components. However, the ratio of the yield strength vs. tensile strength of as-cladded AM sample needs to be improved to use this AM alloy in potential automotive applications

Thermal and Microstructural Analysis of the A356 Alloy Subjected to High Pressure in the Squeeze Casting (SC) UMSA Technology Platform

The effect of cyclic pressure on the thermal and microstructural behaviour of the A356 alloy and the addition of commercial Al-Sr Master Alloy, Nano Alumina Master Alloy and a combination of both Master Alloys during the squeeze casting was investigated in this study. The results show that the α-dendrite growth temperature was increased and the Al-Si eutectic growth temperature was decreased substantially, resulting in a super refined, as-cast, equiaxed α-Al cells and ultra fine dendritic eutectic Si in the modified and unmodified A356 aluminum alloy. Moreover, very rapid spheroidization of the Si particles was observed within a very short Solution Treatment time (15min) of the squeeze cast unmodified and Sr modified A356 alloy. It is expected that the results of this study will lead to a number of further fundamental and applied research rendering new alloys and technology

Food safety of Bangladesh focusing on local production and distribution channel of goat meat from producers to consumers

2020東京農業大

Optimal loading analysis with penalty factors for generators using brute force method

Optimal load dispatching is an important challenge for modern electric and computational engineering. Considering different linear and nonlinear constraints optimal load analysis is done to predict the utility and operating duration of the different power stations. This paper reports the optimal loading analysis method using the Brute Force method with and without considering the penalty factor of power line loss. In this work, two different algorithms are discussed with their mathematical explanation and analyzing feasibility. The algorithms are designed and analyzed in Matlab 2018a. Several conditions are examined by the proposed algorithms and the yields are explained with numerical and graphical presentation. The results prove the effectiveness of the proposed algorithms. Furthermore, the pros and cons of the proposed methods are also discussed in this work

Prostate cancer prediction using feedforward neural network trained with particle swarm optimizer

Prostate cancer has been one of the most commonly diagnosed cancers in men and one of the leading causes of death in the United States. Because of the complexity of the masses, radiologists are unable to diagnose prostate cancer properly. Many prostate cancer detection methods have been established in the recent past, but they have not effectively diagnosed cancer. It is worth noting that most current studies employ machine learning techniques, especially when creating prediction models from data. Despite its possible benefits compared to standard statistical analyses, these methods break down the problem statements into different parts and combine their results at the final stage. This makes complexity, and the prediction accuracy not consistently high. In this paper, the Feedforward Neural Networks (FNNs) is trained by using Particle Swarm Optimizer (PSO) and the FNNPSO framework is applied to the prediction of prostate cancer. PSO is one of the novel metaheuristics and frequently used for solving several complex problems. The experimental results are evaluated using the mean, best, worst, and standard deviation (Std.) values of the fitness function and compared with other learning algorithms for FNNs, including the Salp Swarm Algorithm (SSA) and Sine Cosine Algorithm (SCA). The experimental finding shows that the FNNPSO framework provides better results than the FNNSSA and FNNSCA in FNN training. Moreover, FNN trained with PSO is also shown to be better accurate than other trained methods to predict prostate cancer

Economic power dispatch solutions incorporating stochastic wind power generators by moth flow optimizer

Optimization encourages the economical and efficient operation of the electrical system. Most power system problems are nonlinear and nonconvex, and they frequently ask for the optimization of two or more diametrically opposed objectives. The numerical optimization revolution led to the introduction of numerous evolutionary algorithms (EAs). Most of these methods sidestep the problems of early convergence by searching the universe for the ideal. Because the field of EA is evolving, it may be necessary to reevaluate the usage of new algorithms to solve optimization problems involving power systems. The introduction of renewable energy sources into the smart grid of the present enables the emergence of novel optimization problems with an abundance of new variables. This study's primary purpose is to apply state-of-the-art variations of the differential evolution (DE) algorithm for single-objective optimization and selected evolutionary algorithms for multi-objective optimization issues in power systems. In this investigation, we employ the recently created metaheuristic algorithm known as the moth flow optimizer (MFO), which allows us to answer all five of the optimal power flow (OPF) difficulty objective functions: (1) reducing the cost of power generation (including stochastic solar and thermal power generation), (2) diminished power, (3) voltage variation, (4) emissions, and (5) reducing both the cost of power generating and emissions. Compared to the lowest figures provided by comparable approaches, MFO's cost of power production for IEEE-30 and IEEE-57 bus architectures is $888.7248 per hour and$ 31121.85 per hour, respectively. This results in hourly cost savings between 1.23 % and 1.92%. According to the facts, MFO is superior to the other algorithms and might be utilized to address the OPF problem

A simple design of a Matlab-Based function for topographical presentation of FNIRS Data

Functional Near-Infrared Spectroscopy (fNIRS) has aggrandized the domain of Neurophotonics and Imaging research to reach its apex. With enhanced spatial resolution with the pre-existing temporal resolution, fNIRS can be more promising for the functional analysis of the brain. Hardware integrated software for fNIRS analysis is affluent as well as limited for users. The analysis based on MATLAB is done with the Graphical User Interface (GUI) that are difficult to use because they involve numerous steps, coefficients, and related files. This is a simple MATLAB-based study that includes the generation of the brain activation patterns based on oxygenation and de-oxygenation of hemoglobin and enhancing spatial resolution for the better identification of brain functionality. Brain activation pattern based on the recorded fNIRS data is created in the form of a color-coded map. The map is registered to the brain surface image which provides better visuality of the activation scheme of the brain with an anatomical view. This research intends to encourage prolific researchers in this research area to conduct simplified and cost-effective analyses of the fNIRS study

Moth Flame Optimization Algorithm including Renewable Energy for Minimization of Generation & Emission Costs in Optimal Power Flow

Optimal power flow is an approach for enhancing power system performance, scheduling, and energy management. Because of its adaptability in a variety of settings, optimum power flow is becoming increasingly vital. The demand for optimization is driven by the need for cost-effective, efficient, and optimum solutions. Optimization is useful in a variety of fields, including science, economics, and engineering. This problem must be overcome to achieve the goals while keeping the system stable. Moth Flame Optimization (MFO), a recently developed metaheuristic algorithm, will be used to solve objective functions of the OPF issue for combined cost and emission reduction in IEEE 57-bus systems with thermal and stochastic wind-solar-small hydropower producing systems. According to the data, the MFO generated the best results across all simulated research conditions. MFO, for example, offers a total cost and emission of power generation of 248.4547 $/h for IEEE 57-bus systems, providing a 1.5 percent cost savings per hour above the worst values obtained when comparing approaches. According to the statistics, MFO beats the other algorithms and is a viable solution to the OPF proble

Automatic brain tumor detection using feature selection and machine learning from MRI Images

A brain tumor is a group of defective cells in the brain. It happens when a cell in the brain develops a dysfunctional structure. Nowadays it becoming a crucial factor of death for a large number of people. Among all the varieties of tumors, the seriousness of a brain tumor is high. Therefore, instant detection and proper care to be done to save a life from brain tumors. Microscopic examination can separate the tumor cells from healthy cells. They are typically less well separated than normal cells. In modern imaging technology, the detection and classification of brain tumors is a primary concern. For a clinical supervisor or radiologist, it is time-consuming and frustrating work. The accuracy of recognition and classification of tumors executed by radiologists or clinical experts is depended on their experience only. Therefore, accurate identification and classification of brain tumors can be determined by image processing techniques. This research suggests a machine learning module to detect brain tumors using magnetic resonance imaging (MRI) of brain tumors. The method consists of pre-processing of nearly raw raster data (NRRD) of the MRI images, feature extraction, feature selection, and the classification learner to evaluate and construct the final model. The classification learner is designed with a support vector machine (SVM) classifier. The classification method performs well with weighted sensitivity, specificity, precision, and accuracy of 98.81%, 98.88%, 98.82%, and 98.81% respectively. The findings may infer a remarkable step for detecting the presence of tumors in neuro-medicine diagnosis

Success history moth flow optimization for multi-goal generation dispatching with nonlinear cost functions

Combined Economic Emission Dispatch (CEED) is resolved by combining Success History Moth Flow Optimization (SHMFO) and valve-point loading of thermal generators. This SHMFO the valve-point loading problem is a multi-objective nonlinear optimization problem including generator capacity limits and power balance. The valve-point loading causes oscillations in the input-output characteristics of generating units, hence rendering the CEED problem an imperfect optimization problem. As a benchmark test system for validating the efficacy of SHMFO, IEEE 30-bus systems are studied. Comparing the SHMFO method to other optimization strategies revealed its superiority and proved its capacity to resolve the CEED issue. The OPF is framed as a single or multiobjective problem with restrictions on generator capability, line capacity, bus voltage, and power flow balance to minimize fuel cost, emission, transmission loss, voltage deviation, etc. The numerical findings indicate that the SHMFO algorithm can provide cost-efficiency, diversity, and convergence in a single run. SHMFO performs better than the other algorithms and is an excellent choice for addressing the OPF problem, as shown by the results. On non-dominated solutions, a method adapted from the Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) is used to establish the Best Compromise Solution (BCS)