A Methodology for Investigating and Modelling Laser Clad Bead Geometry and Process Parameter Relationships

The rapid growth in population increases water demand thus resulting in scarcity of water which is due to improper management rather than lack of resources. Reservoir is the most important source for surface water. So, reservoir storage plays a crucial role in efficient reservoir management. Artificial neural network (ANN) is capable of simulating reservoir storage capacity. So, in present work cascade forward back propagation and feed forward back propagation network artificial neural network (ANN) models with five different architecture in each by changing the number of hidden layer neurons were developed to predict Harangi reservoir storage, Karnataka, India. The first two years (2010–2012) data on daily basis was used for supervised training and remaining (2013–2014) data on daily basis was used in prediction. The predictive accuracy using the statistical parameters like correlation coefficient (R) and mean absolute percentage error (MAPE) were found within well acceptable limit. Results shows that, compared to the cascade forward back propagation network, feed forward back propagation network architecture of 6-5-1 ANN model with correlation coefficient (R) of 0.9882 and absolute mean percentage error (MAPE) of 5.225%. is best suited for present study.

Single point incremental forming (SPIF) is an advanced, flexible, and cost-effective approach for producing complicated sheet metal objects quickly. Because of its low equipment cost, the process is best suited toward low or medium quantity batch production as the conventional stamping method remains cost-effective only for mass manufacture. During SPIF formation of titanium grade 5 materials, a response surface methodology and artificial neural network (ANN) model was created to optimize and estimate wall angle (Ømax) and average surface roughness ( Ra). The ANN model is developed using feed forward back propagation networks. Various combinations of transfer functions and number of neurons were used to create the ANNs (3- n-1, 3- n-2). The confirmation runs have been used to ensure that the ANN anticipated and practical findings have been in agreement. The generated ANN model (3- n-1) was capable of accurately forecasting the results of the experiment, with an overall R-value and Mean Square Error (MSE) of 0.99987 and 0.010905 for Ra, and 0.99999 and 0.00700 for wall angle. The best 3- n-2 models has an average R-value of 0.99992 and MSE of 0.05532, respectively. As a consequence of rapid ANN modeling technique, it became discovered that technical effort inside the SPIF process could be decreased.

Laser cladding is a novel additive manufacturing and surface treatment process associated with many interactive process parameters. Using the response surface method with a central composite design, a structured design of experiments approach was chosen to examine the influence of selected process parameters on the bead geometry and hardness for single-track laser-cladded specimens using AISI 420 stainless steel powder on a AISI 1018 substrate. In the present study, robust predictive models for hardness, bead aspect ratio, and wetting angle with the substrate were determined using multiple regression analysis. The geometry and hardness relationships to the process inputs were evaluated using F-statistics from the analysis of variance and compared by utilizing perturbation plots, 3D surface mapping, and contour plots. The highly coupled non-linear relationships are evident from these analyses, but this study revealed that the laser speed has the most significant effect on the bead microhardness and the powder feed rate was found to be the most significant parameter for these bead geometry parameters. The perturbation plots confirmed this sensitivity of those process parameters. This research study gives a guideline for the selection of appropriate process parameters for the laser cladding process to achieve desired hardness and bead geometry.

<div class="section abstract"><div class="htmlview paragraph">Diesel engine is vital in the industry for its characteristics of low fuel consumption, high-torque, reliability, and durability. Existing diesel engine technology has reached the upper limit. It is difficult to break through the fuel consumption and emission of diesel engines. VVA (Variable Valve Actuation) is a new technology in the field of the diesel engines. In this paper, GT-Suite and ANN (artificial neural network) model are established based on engine experimental data and DoE simulation results. By inputting Intake Valve Opening crake angle (IVO), Intake Valve Angle Multiplier (IVAM) and Exhaust Valve Angle Multiplier (EVAM) into the ANN Model, and by using SA (simulated annealing algorithm), the optimized results of intake and exhaust valve lift under the target conditions are obtained. According to the optimized results, the fuel consumption of BSFC (brake specific fuel consumption) can be saved by 3.9%, 0.9%, and 7.3% at three different target working conditions, respectively (1000r/min with 50% load, 2000 r/min with 35% load, and 3000r/min with 20% load. Under the three conditions, the intake valve lifts are 1.10, 0.96, 1.10 times as the original respectively and the exhaust valve lifts are 0.98,0.94 and 1.10 times respectively. In addition, by adding the secondary opening strategy of the exhaust valve lift, internal exhaust gas recycling (IEGR) can be achieved. The exhaust gas temperature increased by 9.55% (66.64K) under low-speed working condition, and fuel consumption increased slightly by 6.15%, which is also important for exhaust thermal management and external emission control in the cold start phase of diesel engines. By changing the intake and exhaust valve control, there are obvious differences in the optimal valve lift under different working conditions. Therefore, the application of the VVA system in the diesel engine is of great significance.</div></div>

The aim of this study is to develop an artificial neural network (ANN) model for spark assisted diamond face grinding (SADFG) of cobalt bonded tungsten carbide (WC-Co) composite to predict the material removal rate (MRR) and average surface roughness (Ra). The experiments were conducted on a self-developed face grinding setup, which is attached with EDM machine. A bronze metal bonded diamond wheel is used for experimentations. All the experiments were performed according to the central rotatable design. The current, pulse on-time, duty factor and wheel speed were taken as input process parameters and responses are measured in terms of MRR and Ra. Central rotatable design is used for experimentation. The obtained experimental data set was used to train the ANN model. The ANN architecture with back propagation algorithm has been used for modelling of process parameters of SADFG process. It has been found that the developed ANN model is capable to predict the MRR and Ra with absolute average percentage error of 10.40% and 6.81%, respectively. It has been also found that wheel speed at 1,300 RPM is suitable for achieving of the better surface finish while duty factor at 0.70 has been found more appropriate for higher MRR.

<div class="section abstract"><div class="htmlview paragraph">Growing concerns about the emissions of internal combustion engines have forced the adoption of aftertreatment devices to reduce the adverse impact of diesel engines on health and environment.</div><div class="htmlview paragraph">Diesel particulate filters are considered as an effective means to reduce the particle emissions and comply with the regulations. Research activity in this field focuses on filter configuration, materials and aging, on understanding the variation of soot layer properties during time, on defining of the optimal strategy of DPF management for on-board control applications.</div><div class="htmlview paragraph">A model was implemented in order to simulate the filtration and regeneration processes of a wall-flow particulate filter, taking into account the emission characteristic of the engine, whose architecture and operating conditions deeply affect the size distribution of soot particles. The model is based on a lumped parameter approach able to be used for on-board monitoring and control, in order to provide knowledge of DPF status versus time.</div><div class="htmlview paragraph">The filtration model is based on the ‘unit collector’ and fluid dynamic approaches to predict trapped mass and filter backpressure evolution during time. The model accounts for the size distribution of soot particles in the engine exhaust and for its impact on the DPF properties during loading process. Regeneration model is based on a O<sub>2</sub> non-catalytic oxidation approach of the trapped soot.</div><div class="htmlview paragraph">The model was applied to simulate the loading/regeneration processes of a wall-flow DPF under real engine operation condition. Experimental tests were carried out and measurements were used to validate the proposed model.</div></div>

The present dissertation “Laser Cladded Surface Hardening Coating with Gradient of Mechanical Properties” is devoted to the research of laser cladding process for obtaining high quality surface hardening coating, which can be used for hardening and refurbishment of both machine elements and pressure processing technological equipment. The aim of the research was to investigate the possibility of creating a laser cladded single layer surface hardening coating with a gradient of mechanical properties in the normal direction from the surface to the base material, which would allow increasing the crack resistance of the cladded coating and consequently extend the workability of product in manufacturing and refurbishment of machine elements and tools. The present dissertation contains an introduction, a literature analysis part, a description of research methods, a description of technical facilities and materials, and experimental-analytical parts. Chapter 1 covers literature analysis of laser cladding technologies and concepts for the development of advanced coatings with gradient of properties. The current situation in laser cladding of surface hardening and wear resistant coatings is analysed, including information on the parameters of the laser cladding process, as well as current information on achievable coating properties, coating quality characteristics and typical defects. Chapter 2 provides information on the research methodology, equipment, methods and materials. In this paper, metallography of laser cladded beads and coatings was investigated in detail, as well as the morphology, chemical composition, and mechanical and tribo-technical properties of coatings. Chapter 3 describes the experimental research on forming conditions of the laser cladded high-speed steel (AISI M2) single beads with a keyhole in penetration quality characteristics (shape geometry, porosity and amount of carbide-forming elements) in respect to the technological parameters (cladding speed and laser beam defocusing) of the process. In chapter 4 the experimental research of the influence of laser cladding process regimes on the quality characteristics (thickness, depth of penetration, amount of carbide forming elements in the facing layer), morphology and change in mechanical properties (microhardness) in coating-substrate system of high-speed steel (AISI M2) coatings with keyhole in penetration is reviewed. In chapter 5 experimental study of laser cladding process parameters (overlap ratio, cladding speed and laser beam defocusing direction) and anisotropic structure influence on surface properties (hardness and friction coefficient) of high-speed steel coatings after mechanical treatment is described. Chapter 6 describes the experimental study, in which comparative analysis of differently laser cladded high-speed steel coatings using conventional and the new laser cladding method is carried out. As a result of the research, regime frames of the new laser cladding method for deposition of a single bead and a coating with keyhole penetration in to substrate were determined. The results are summarized in figures, tables and graphs. On the basis of regression analysis empirical models of laser cladding process are obtained, which can be used to predict the values of the cladded bead and coating quality characteristics. In the dissertation a new laser cladding method was developed (Latvian patent application P–19–84, date of filing 30.12.2019.), which allows to create single-layer surface hardening coatings with improved properties in the coating and base material transition zone, which may have a positive influence on the residual stress redistribution, especially when using materials with different coefficients of thermal expansion. Thus, this method eliminates the need for interlayer application in the cladding of dissimilar steels. Consequently, it can contribute to increasing the fatigue resistance of items as well as to increasing the durability under thermocyclic conditions. Technological instruction for hardening and refurbishment of surfaces of pressure shaping equipment by laser cladding of coatings with keyhole in penetration was elaborated as the basis for the present dissertation. At the end of the dissertation information about the trial production of the refurbished parts with coatings obtained by new laser cladding method in Latvia and Belarus; and approbation of the results, as well as an insight regarding the development prospects of this method in further research is given.

The quality of weld joint is directly influenced by the input process parameters influenced by various process parameters during welding. The weld quality can be decided by bead geometry viz., depth of penetration and bead width. Inadequate depth of penetration will lead to failure of the welded structure. This paper presents the development of mathematical model for the prediction of depth of penetration of weld bead geometry in pulsed gas metal arc welding process in double pulse mode. The model is based on experimental data .In this investigation four input process parameters wire feed rate, ratio of wire feed rate to travel speed, amplitude and double pulse frequency are considered. The experiments were conducted on 6mm thickness plate of 5083 H111 aluminum alloy using fractional factorial design of experiments. The mathematical model for depth of penetration is developed using multiple nonlinear regression. The developed model is then compared with experimental results and it is found that the results obtained from mathematical model are accurate. The obtained results help in selecting quickly the process parameters to achieve the desired quality.

Statistical analysis and multi-objective process optimization of laser cladding TiC-Inconel718 composite coating

<div class="section abstract"><div class="htmlview paragraph">Additive Manufacturing (AM) techniques, particularly Fusion Deposition Modeling (FDM), have received considerable interest due to their capacity to create complex structures using a diverse array of materials. The objective of this study is to improve the process control and efficiency of Fused Deposition Modeling (FDM) for Thermoplastic Polyurethane (TPU) material by creating a predictive model using an Adaptive Neuro-Fuzzy Inference System (ANFIS). The study investigates the impact of FDM process parameters, including layer height, nozzle temperature, and printing speed, on key printing attributes such as tensile strength, flexibility, and surface quality. Several experimental trials are performed to gather data on these parameters and their corresponding printing attributes. The ANFIS predictive model is built using the collected dataset to forecast printing characteristics by analyzing input process parameters. The ANFIS model utilizes the learning capabilities of neural networks and fuzzy logic systems to analyze the intricate relationships within the FDM process. This model allows for precise predictions of printing outcomes. The model shows its ability to precisely forecast printing attributes, enabling the determination of ideal process parameter configurations for enhanced FDM performance with TPU material. The proposed Adaptive Neuro-Fuzzy Inference System (ANFIS) predictive model presents a methodical strategy for optimizing Fused Deposition Modeling (FDM) parameters. This model serves as a valuable tool for manufacturers to improve productivity and product quality in additive manufacturing operations using Thermoplastic Polyurethane (TPU) material. This research enhances the comprehension of FDM processes and provides practical recommendations for optimizing AM operations in diverse industrial applications.</div></div>

In this paper, artificial neural network (ANN) model was used for predicting colour properties of 100 % cotton fabrics, including colour yield (in terms of K/S value) and CIE L, a, and b values, under the influence of laser engraving process with various combination of laser processing parameters. Variables examined in the ANN model included fibre composition, fabric density (warp and weft direction), mass of fabric, fabric thickness and linear density of yarn (warp and weft direction). The ANN model was compared with a linear regression model where the ANN model produced superior results in prediction of colour properties of laser engraved 100 % cotton fabrics. The relative importance of the examined factors influencing colour properties was also investigated. The analysis revealed that laser processing parameters played an important role in affecting the colour properties of the treated 100 % cotton fabrics.

Cold metal transfer (CMT)–based wire-arc additive manufacturing (WAAM) is increasingly popular for the production of large and complex metallic components due to its high deposition rate, low heat input, and excellent material efficiency. The accurate prediction of the bead geometry is of great importance to enhance the stability of the process and its dimensional accuracy. Besides the wire feed speed (WFS) and travel speed (TS), the interlayer temperature is another key factor in determining the bead geometry because of the heat accumulation in the multilayer deposition. In this paper, considering the varying interlayer temperature, WFS, and TS as inputs, an artificial neural network (ANN) is developed to predict the bead width, height, and contact angle; then, by connecting the ANN model with a bead geometric model, a combined model is established to improve the ANN model. Based on experimental test data, with random combinations of input parameters, the combined model is demonstrated to be able to accurately predict the bead geometry (mean error < 5.1%). The general effect of interlayer temperature on the bead geometry was also investigated by experiment.

Laboratory experiments were used to estimate the hydraulic performance of emitters, i.e., the emitter flow variation (qvar) and manufacturer’s coefficient of variation (CVm), by measuring the discharge of different labyrinth-channel emitters at different operating pressures (P) and water temperatures (T). Considering the importance of the structural parameters of the labyrinth-channel emitters in drip irrigation design, which has been experimentally confirmed, artificial neural network (ANN) and gene expression programming (GEP) models were developed to predict qvar and CVm. The ANN and GEP models were trained and tested using structural parameters (including the number, height (H), and spacing of trapezoidal units and the flow path width and length) of different labyrinth-channel emitters, P and T as the input variables, and qvar and CVm as the outputs. Statistical criteria, including the coefficients of correlation (r), relative root-mean-square error (RRMSE), and mean absolute error (MAE), were used to examine the accuracy of the developed models. The ANN models exhibited good correlation with experimental values, with high r values 0.995 and 0.969 for qvar and 0.997 and 0.947 for CVm in the training and testing processes, respectively. The ANN models had lower RRMSE and MAE values than the GEP models. Furthermore, H was the dominant variable for obtaining the most accurate prediction model. The results confirm that the ANN models are superior to the GEP models for the prediction of the hydraulic performance of emitters.

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Nd: YAG Laser Welding Process (LWP) is the most efficient method commonly used for the joining of different kinds of materials, whether it can be a sheet or plate. LWP in general, uses high power density, frequency and travel speed or feed rate as primary process parameters in order to perform a joining process across the metals. This paper investigates the effect of free vibrational characteristics for Stainless Steel 304 (SS 304) joints which are reinforced with Al&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;3&lt;/sub&gt; micro particles processed by dual side (welding performed on the top and bottom surface of sheets) Nd: YAG LWP. The inclusion of micro particles was inserted directly across the weld pool region, by fabricating drills with a constant gap between each drilled holes. Totally 12 samples were fabricated with different laser power, travel speed and by keeping the frequency level as constant for all the experiments. Vibration tests were performed on the fabricated samples made with and without incorporation of micro particles using accelerometer, data acquisition card, and the plots were obtained with the assist of DEWE software. The vibration equipment is used on the SS 304 joints in order to process the vibrational signals. Each sample has been tested with ten trials and the average values are drawn in order to reduce or minimize the measuring errors like (measuring error, fitting error, environmental impacts, etc.). The results revealed that vibrational characteristics are linked to factors like reinforcement of powders, selection of welding process and process parameters. It is also understood that the frequency and damping factor for a material can be varied with process parameters and inclusion of micro particles in the solidification process. Thus this paper will assist any designer to choose the process parameter ranges, reinforcement of powder particle methodology and their impact on the material behavior of fabricated joints.&lt;/div&gt;&lt;/div&gt;

ABSTRACTIn this research paper, the development of an artificial neural network (ANN) model for the designing of a compact dual-band hybrid fractal antenna (CDHFA) that covers Wireless Medical Telemetry Service and industrial, scientific and medical (ISM) bands are described. The projected CDHFA is designed by using 1.6mm thick, FR4 substrate whose permittivity is 4.4 and obtained by the combination of different fractal geometries. The dimensions of the projected CDHFA are interlinked with each other. A set of 80 CDHFAs with different dimensions is designed to obtain a data dictionary for the implementation of ANN. From the experimental results, it is depicted that the resonant frequencies are 1.4290 and 2.4380 GHz with S(1,1) values −12.4 and −15.72 dB, respectively. A comparison between feed forward back propagation network, generalised regression neural network and desired outputs in terms of average absolute error is also done. The simulation, experimental and ANN results are well matched that indicates the effectiveness and accuracy of the developed ANN model for biomedical applications.