Combination Of Process Parameters Research Articles

Microstructure and Mechanical Properties of Cermet Composite Coating on TC4 Surface

In order to improve the surface quality and performance of TC4, the TC4-HfC-TaC metal-ceramic composite cladding coating was prepared on the surface of TC4 by using different process parameters by laser cladding technology. Through digital testing equipment, the macro morphology, molten pool size, dilution rate, Rockwell hardness (HRC), geometry, microstructure and other comprehensive properties of the coating are studied and analyzed. The results show that the performance characterization of TC4-HfC-TaC cladding layer is closely related to the melting state of refractory carbide ceramic phase HfC and TaC powder, when the laser beam energy density is not enough to completely melt the ceramic phase powder, with the increase of energy density, the coating surface tends to be flat, the molten pool size and dilution rate increase, the hardness gradually increases, the metallographic structure is refined uniformly, and the surface quality is improved. Excessive energy density of the laser beam can easily lead to excessive dilution rate of the cladding layer, reduced coating hardness, coarse metallographic structure, and reduced surface quality. Finally, the best combination of process parameters was screened out: laser power 3000 W, scanning speed 300 mm/min, spot size 2.5 × 11.5 mm, rectangular spot lap rate 40%, preset powder thickness 1.5 mm, at which time the coating hardness increased to 63.22 HRC. It is about 2.1 times the hardness of TC4 substrate, with the best performance, and the metallographic structure is densely arranged fine cell-like crystals, with outstanding microscopic characteristics, which realizes the purpose of preparing high-quality TC4-HfC-TaC cermet composite cladding coating on the surface of TC4 titanium alloy.

Process Parameter Optimization for CO2 Laser Polishing of Fused Silica Using the Taguchi Method.

Fused silica was polished to a high quality by a CO2 laser beam with a rapid scanning rate. The rapid scanning rate produced a line laser heat source, resulting in a "polishing line" during the polishing process. The Taguchi method was used to evaluate the comprehensive influence of polishing process parameters on the polishing qualities. Four factors, namely the length of laser reciprocating scanning (A), laser beam scanning speed (B), feed speed (C), and defocusing amount (D), were investigated in this study. The optimal process parameter combination (A1B1C1D1) was obtained. The surface roughness of fused silica was reduced from Ra = 0.157 μm to 0.005 μm. Through analysis of variance (ANOVA), it was found that laser beam scanning speed (B) had a significant influence on the polishing quality. The interaction of the two factors plays a decisive role in the determination of the best process parameters, and the influence of other multi-factor interaction can be ignored; the interaction between A × B is the largest, with a contribution of 42.69%.

Improvement of the Dimensional Accuracy of a Ti-6Al-4V Ripple Disc During Electric Hot Incremental Sheet Forming

The edge warpage of a Ti-6Al-4V ripple disc is a major forming defect during electric hot incremental forming, which can lead to a significant dimensional error. In this paper, a novel manufacturing method, namely the combination of electric hot incremental forming and electrically assisted sizing, has been proposed to improve the forming defect. The effect of process parameters on forming fracture was analysed in detail, and then an optimal combination of process parameters was obtained to ensure the successful forming of a Ti-6Al-4V ripple disc. On this basis, a sizing device and a sizing current were separately designed and analysed to eliminate the warpage defect of Ti-6Al-4V ripple discs. According to the experimental result, Ti-6Al-4V ripple discs can be satisfactorily fabricated through the method proposed.

Efficient low-carbon manufacturing for CFRP composite machining based on deep networks

ABSTRACT The drilling quality of carbon fibre reinforced polymer (CFRP) components is a key factor affecting the service life of the components, while energy saving and emission reduction in industrial production are crucial. In this study, drilling experiments were conducted on T300 plywood using a 55° coated tungsten steel drill bit, and CNN-LSTM neural network models were used to construct mapping relationships between process parameters (spindle speed, feed rate, and fibre lay-up sequence) and delamination factor and machine energy consumption. A new method of predicting the delamination factor by process parameters is proposed, and explored the optimal process parameter combinations that reduce the energy consumption of machine tools and minimise the delamination factor at the same time. The research results show that within the parameter settings, a spindle speed of 7000 r/min, a feed rate of 40 mm/min, and a lay-up sequence of [0°, 0°, −45°, 90°]6s ensure both low power consumption in the drilling process and the highest possible hole quality. This paper clearly demonstrates the feasibility of achieving low-power, high-quality drilling of CFRP through parameter optimisation, providing guidance to the manufacturing industry to improve the quality of CFRP hole-making while easing the pressure on carbon emissions.

Multi-objective Optimization of Injection Molding Process Based on One-Dimensional Convolutional Neural Network and the Non-dominated Sorting Genetic Algorithm II

<div>In the process of injection molding, the vacuum pump rear housing is prone to warping deformation and volume shrinkage, which affects its sealing performance. The main reason is the improper control of the injection process and the large flat structure of the vacuum pump rear housing, which does not meet its production and assembly requirements (the warpage deformation should be controlled within 1.1 mm and the volume shrinkage within 10%). To address this issue, this study initially utilized orthogonal experiments to obtain training samples and conducted a preliminary analysis using gray relational analysis. Subsequently, a predictive model was established based on a one-dimensional convolutional neural network (1D CNN). Input parameters from the injection molding process, including melt temperature, mold temperature, packing pressure, packing time, injection pressure, injection time, and cooling time, were used while warping deformation and volume shrinkage were considered as outputs. Global optimization was performed using the non-dominated sorting genetic algorithm II (NSGA-II), and the optimal combination of process parameters was evaluated using the criterion importance through intercriteria correlation—technique for order preference by similarity to ideal solution (CRITIC-TOPSIS). Moldflow analysis demonstrated that the obtained indicators outperformed the optimization results from orthogonal experiments, confirming the effectiveness of the injection molding process parameter optimization method based on 1D CNN-NSGA-II. In comparison to the pre-optimization results, product warping deformation decreased by 40.68%, and volume shrinkage reduced by 18.14%, and all of them meet the production requirements.</div>

Modeling of creep-fatigue features of hydrogen storage bed and its parameter optimizing based on finite element method and orthogonal experimental design with artificial neural networks

Regarding the creep-fatigue behavior of hydrogen storage bed (HSB) structures under high-temperature loading and internal pressure loading under repeated hydrogen absorption and desorption process, the heat transfer characteristics and creep-fatigue life during a complete service process in HSB were calculated using Ansys Workbench and Ncode software, and the reliability of the life results calculated by finite element method were validated using ASME code. Based on orthogonal experimental design (OED) theory, an orthogonal experimental table with 4 factors and 5 levels was compiled for the four process parameters: diameter of cooling tube, thickness of cylinder wall, spacing between inner and outer layers, and heat transfer coefficient of condensate. Moreover, an optimization method based on OED coupled with BP ANNs was proposed to find the best process parameters. And the importance of each parameter was analyzed based on range analysis and analysis of variance (ANOVA) to divide the process parameters into rough optimized parameters and precise optimized parameters in this method. In order to identify the optimal process parameter combination of HSB, the maximum life and corresponding process parameter combinations were identified based on OED and the combined method, respectively. The maximum life identified by the combined method proposed in this work is increased by 10.23 % than that identified by OED, which shows the superiority of this combined method.

Optimizing micro-EDM with carbon-coated electrodes: A multi-criteria approach

The pursuit of precision machining in engineering materials has ignited an imperative to advance electrical discharge machining (EDM) techniques. This study is a deep dive into the domain of micro-electrical discharge machining ([Formula: see text]EDM) with a specific focus on the utilization of carbon-coated tool electrodes. The primary objective is to elucidate the influence of carbon-coated electrodes on vital machining parameters, including machining depth (Z coordinate), tool wear rate (TWR) and overcut (OC) in [Formula: see text]EDM operations. Moreover, the investigation extends to scrutinizing surface characteristics generated by [Formula: see text]EDM with carbon-coated tool electrodes. To enhance performance, carbon-coated tungsten carbide tool electrodes are employed for machining titanium alloy (Ti–6Al–4V) workpieces. The optimization process seeks to identify the optimal combination of process parameters using a Taguchi-DEAR-based multi-response approach. Experimental findings reveal that integrating carbon-coated electrodes significantly improves the [Formula: see text]EDM process, leading to enhanced surface performance metrics. Notably, this study identifies specific parameter settings that effectively optimize machining quality indicators. By achieving a voltage of 160[Formula: see text]V, a capacitance of 10,000[Formula: see text]pF and a tool rotation of 600[Formula: see text]RPM, the research contributes valuable insights into the intricate realm of [Formula: see text]EDM, highlighting the potential for enhanced surface performance through strategic parameter manipulation.

Experimental investigation of 3D taper profile machining of SS304 using WEDM

ABSTRACT Wire electrical discharge machining (WEDM) is an outstanding variety of EDM processes in which the tool is a wire electrode. The medical, electronics, tool, and die industries use stainless steel 304 (SS304). However, there are traditionally many complications that manufacturers face, while machining SS304. Also, the creation of 3D shapes using WEDM is challenging. Due to difficulties in normal machining and producing 3D profiles using conventional machining of SS304, this research aims to utilize the application of WEDM for creating complex shapes or products with top as square and bottom as rectangular shape to get a rectangular frustum. A ϕ 0.25 mm brass wire was used, and experiments were conducted as per L9 array. Further, the determination of the best process parameter (Ton, Toff, Voltage, current) combination for achieving the highest MRR, hardness, and minimal surface roughness (Ra) and kerf with analysis of machined surfaces using an SEM microstructure image was tried. The results indicated that MRR and Ra values varied from 7.05 to 47.29 mm3/min and 1.251 to 2.019 µm, respectively. Furthermore, the kerf ranged from 0.294 to 0.475 mm and hardness started at 164 to 233 HV. To optimize WEDM results Grey relational analysis (GRA) was utilized.

Mesoscale Simulation of Laser Powder Bed Fusion with an Increased Layer Thickness for AlSi10Mg Alloy

Low performance is considered one of the main drawbacks of laser powder bed fusion (LPBF) technology. In the present work, the effect of the AlSi10Mg powder layer thickness on the laser melting process was investigated to improve the LPBF building rate. A high-fidelity simulation of the melt pool formation was performed for different thicknesses of the powder bed using the Kintech Simulation Software for Additive Manufacturing (KiSSAM, version cd8e01d) developed by the authors. The powder bed after the recoating operation was obtained by the discrete element method. The laser energy deposition on the powder particles and the substrate was simulated by ray tracing. For the validation of the model, an experimental analysis of single tracks was performed on two types of substrates. The first substrate was manufactured directly with LPBF technology, while the second was cast. The simulation was carried out for various combinations of process parameters, predominantly with a high energy input, which provided a sufficient remelting depth. The calculations revealed the unstable keyhole mode appearance associated with the low absorptivity of the aluminum alloy at a scanning speed of 300 mm/s for all levels of the laser power (325–375 W). The results allowed formulating the criteria for the lack of fusion emerging during LPBF with an increased layer thickness. This work is expected to provide a scientific basis for the analysis of the maximum layer thickness via simulation to increase the performance of the technology.

A multi-objective optimization workflow of ring-rolling process parameters based on production energy and time

Our industrial era is characterized by a significant attention to environmental aspects and production sustainability. Under this point of view, the minimization of the process energy represents a milestone leading to a consistent reduction of pollution. However, to promptly react to the high production rate market request, companies employ time saving manufacturing strategies, often in contrast with this principle. Considering this, an optimization concerning the reduction of both production time and energy, results to be strategical, especially when energy-intensive processes, such as ring rolling, are considered. Ring rolling is a shaping process performed by a dedicated equipment, composed of a driving roll, an idle roll, and a couple of axial rolls, allowing to obtain seamless rings while simultaneously reducing their height and thickness, while enlarging diameter. The kinematics of each component of the equipment can be set independently. This leads to several process conditions influencing quality, energy, and times, and makes it difficult the selection of the most suitable configuration. In this paper, a multi-objective optimization workflow is presented. Starting from validated FEM simulation campaign results, the proposed methodology allowed to individuate the best process parameters combination for limiting energy and time, encouraging its application to a wider range of energy-consuming processes.

Study on Machinability Behaviour and Simultaneous Optimisation of Multiple Responses Using Taguchi‐Based Grey Relational Analysis in End Milling of Aluminum Hybrid Composites

The present work reveals the machinability characteristics of Al7068/Si3N4/BN hybrid composite materials. The composite materials were prepared through the liquid metallurgy technique with various weight fractions (0, 5, 10) of silicon nitride and hexagonal boron nitride particles. The experimental studies were performed on a CNC milling machine by varying cutting speed (600, 1200 and 1800 rpm), feed rate (150, 300 and 450 mm/min), and depth of cut (0.8, 1.2 and 1.6 mm). Taguchi’s plan of experiments (L27 orthogonal array design) and analysis of variance (ANOVA) were employed to investigate the effect of process parameters on surface roughness (Ra), cutting temperature, and material removal rate (MRR). Taguchi‐based grey relational analysis (GRA) was performed to identify the best process parameters combination for optimising the multiple responses simultaneously. ANOVA results revealed that the weight fraction of Si3N4 is the most contributing parameter for multiple response characteristics.

PLA-Based Composite Panels Prepared via Multi-Material Fused Filament Fabrication and Associated Investigation of Process Parameters on Flexural Properties of the Fabricated Composite.

This study prepares composite panels with three Polylactic acid (PLA)-based materials via the multi-material fused filament fabrication method. The influences of four processing parameters on the mechanical properties of 3D-printed samples are investigated employing the Taguchi method. These parameters include the relative volume ratio, material printing order, filling pattern, and filling density. A "larger is better" signal-to-noise analysis is performed to identify the optimal combination of printing parameters that yield maximum bending strength and bending modulus of elasticity. The results reveal that the optimal combination of printing parameters that maximizes the bending strength involves a volume ratio of 1:1:2, a material sequence of PLA/foam-agent-modified eco-friendly PLA (ePLA-LW)/glass fiber-reinforced eco-friendly PLA (ePLA-GF), a Gyroid filling pattern, and a filling density of 80%, and the optimal combination of printing parameters for maximum bending modulus involves a volume ratio of 1:2:1 with a material sequence of PLA/ePLA-LW/ePLA-GF, a Grid filling pattern, and 80% filling density. The Taguchi prediction method is utilized to determine an optimal combination of processing parameters for achieving optimal flexural performances, and predicted outcomes are validated through related experiments. The experimental values of strength and modulus are 43.91 MPa and 1.23 GPa, respectively, both very close to the predicted values of 46.87 MPa and 1.2 GPa for strength and modulus. The Taguchi experiments indicate that the material sequence is the most crucial factor influencing the flexural strength of the composite panels. The experiment result demonstrates that the flexural strength and modulus of the first material sequence are 67.72 MPa and 1.53 GPa, while the flexural strength and modulus of the third material sequence are reduced to 27.09 MPa and 0.72 GPa, respectively, only 42% and 47% of the first material sequence. The above findings provide an important reference for improving the performance of multi-material 3D-printed products.

Optimization of duplex heat treatment settings with the MOORA-PCA technique to customize the mechanical and wear response of Ti6Al4V alloy

The impact of duplex heat treatment settings was examined to optimize the mechanical and wear responses of Ti6Al4V alloys. A multi-objective optimization using ratio analysis (MOORA) coupled with PCA was used to optimize the different outcomes. Heat treatment aims to reduce the wear rate, frictional coefficient, and improve tensile strength and microhardness. Variables like solution treatment temperature, duration, aging temperature, and time are considered for optimization. The optimization results reveal that the optimal circumstances are A3B1C2D3. The appropriate process parameter combination was finally evaluated by a confirmation test, which shows that the MOORA-PCA technique worked well together to find the best heat treatment settings. All of the heat-treated samples demonstrated better mechanical and wear characteristics. However, compared to other heat-treated samples, samples that underwent heat treatment in optimal settings have superior features. Compared to the heat-treated samples at trial 9 and the optimum conditions, the samples at trial 8 settings showed 5.4% and 12.3% lower microhardness, respectively. The alloy subjected to optimal conditions for heat treatment has a tensile strength of 1154.87 MPa, which is 12.95% and 6.09% greater than the specimen heat-treated at trials 8 and 9, respectively. Compared to the as-received alloy, the sample that underwent heat treatment at optimal settings exhibited a wear rate of approximately 40% lower than the as-received alloy, 3% lower than the heat treatment settings used in trial 8, and nearly 26% lower than the heat treatment settings used in trial 9. The bi-model microstructure developed under optimal circumstances is correlated with increased mechanical and wear properties.

Analysis and Optimization of Process Parameters of Powder Mixed Near Dry- EDM by using Grey Relational Optimization

The hybrid Electric Discharge Machine (PM-EDM) is the latest advancement in the removal of hard materials from workpieces while enhancing their performance and characteristics. This research project is focused on optimizing key process parameters, including material removal rate, tool wear, residual stresses, and surface finish. The approach utilized in this study involves employing the standard deviation-based objective weighting method in conjunction with GRA (Grey Relational Analysis) optimization to improve the hybrid PM-EDM process when applied to EN-31 material. Experimental runs were conducted to assess the impact of various input factors such as pulse on time, duty cycle, discharge current, and concentration of metallic powder. The GRA (Grey Relational Analysis)-based Taguchi method was employed for this purpose, and the experimental design followed an L-27 orthogonal array within the framework, facilitated by the use of minitab-20 software. A total of 27 experiments were conducted, encompassing diverse combinations of process parameters. Subsequently, an ANOVA (Analysis of Variance) was performed to scrutinize the impact of various inputs of discharge current, pulse on time, duty cycle, and powder concentration on MRR (Material Removal), TWR (Tool Wear), Ra (surface finish), and Rs (Residual stress).

Optimisation of process parameters for improving surface quality in laser powder bed fusion

Surface quality is one of the critical factors that affect the performance of a laser powder bed fusion part. Optimising process parameters in process design is an important way to improve surface quality. So far, a number of optimisation methods have been presented within academia. Each of these methods can work well in its specific context. But they were established on a few special surfaces and may not be capable to produce satisfying results for an arbitrary part. Besides, they do not consider the simultaneous improvement of the quality of multiple critical surfaces of a part. In this paper, an approach for optimising process parameters to improve the surface quality of laser powder bed fusion parts is proposed. Firstly, Taguchi optimisation is performed to generate a small number of alternative combinations of the process parameters to be optimised. Then, actual build and measurement experiments are conducted to obtain the quality indicator values of a certain number of critical surfaces under each alternative combination. After that, a flexible three-way technique for order of preference by similarity to ideal solution is used to determine the optimal combination of process parameters from the generated alternatives. Finally, a case study is presented to demonstrate the proposed approach. The demonstration results show that the proposed approach only needs a small amount of experimental data and takes into account the simultaneous improvement of the quality of multiple critical surfaces of an arbitrary part.

Metal-composite hybrid joint adhesion and testing optimization for electric vehicle applications

ABSTRACT Lightweight is a critical feature in automotive applications, especially in electric vehicle applications due to the heavyweight of batteries, thus contributing to improve vehicle efficiency. Multi-material solutions were studied in this paper as a strategy for lightweighting, focusing on the improvement of metal-composite joints obtained by overmolding through the comparison of different laser treated textures and processing parameters. In addition, testing methods to assess the adhesion of these joints were analysed. Firstly, steel surface treatment was carried out by pulsed laser radiation producing different patterns. Secondly, over injection of the composite material based on polyamide 6.6 with 40% recycled carbon fiber was carried out with different processing parameters. The mechanical performance of the joints was assessed through shear-strength analysis, and the morphological analysis was carried out. As outcome, the optimal combination of surface treatment and processing parameters was selected. Finally, a finite element analysis was carried out to evaluate comparatively the suitability of a testing method proposed as an alternative to the standard single lap shear tests, with a configuration that facilitates the specimens manufacturing and test execution. As a result, the adequacy of the method for the assessment of the shear strength of this type of joints was demonstrated.

Reinforcement learning as data-driven optimization technique for GMAW process

Welding optimization is a significant task that contributes to enhancing the final welding quality. However, the selection of an optimal combination of various process parameters poses different challenges. The welding geometry and quality are influenced differently by several process parameters, with some exhibiting opposite effects. Consequently, multiple experiments are typically required to obtain an optimal welding procedure specification (WPS), resulting in the waste of material and costs. To address this challenge, we developed a machine learning model that correlates the process parameters with the final bead geometry, utilizing experimental data. Additionally, we employed a reinforcement learning algorithm, namely stochastic policy optimization (SPO), with the aim to solve different optimization tasks. The first task is a setpoint‐based optimization problem that aims to find the process parameters that minimize the amount of deposited material while achieving the desired minimum level of penetration depth. The second task is an optimization problem without setpoint in which the agent aims to maximize the penetration depth and reduce the bead area. The proposed artificial intelligence-based method offers a viable means of reducing the number of experiments necessary to develop a WPS, consequently reducing costs and emissions. Notably, the proposed approach achieves better results with respect to other state-of-art metaheuristic data-driven optimization methods such as genetic algorithm. In particular, the setpoint‐based optimization problem is solved in 8 min and with a final mean percentage absolute error (MPAE) of 2.48% with respect to the 42 min and the final 3.42% of the genetic algorithm. The second optimization problem is also solved in less time, 30 s with respect to 6 min of GA, with a higher final reward of 5.8 from the proposed SPO algorithm with respect to the 3.6 obtained from GA.

Experimental Study on Chemical-Mechanical Synergistic Preparation for Cemented Carbide Insert Cutting Edge.

Typical edge defects in the edge region of a new cemented carbide insert without edge preparation include burrs, poor surface quality, micro-breakages, and irregularities along the edge. To address the problems in new cemented carbide inserts without edge preparations, a chemical-mechanical synergistic preparation (CMSP) method for the cemented carbide insert cutting edge was proposed. Firstly, the CMSP device for the insert cutting edge was constructed. Then, the polishing slurry of the CMSP for the insert cutting edge was optimized using the Taguchi method combined with a grey relation analysis and fuzzy inference. Finally, orthogonal experiments, the Taguchi method, and analysis of variance (ANOVA) were used to investigate the effect of the polishing plate's rotational speed, swing angle, and input frequency of the controller on the edge preparation process, and the parameters were optimized. The results showed that the best parameter combination for the polishing slurry for the cemented carbide inserts was the mass concentration of the abrasive particle of 10 wt%, the mass concentration of the oxidant of 10 wt%, the mass concentration of the dispersant of 2 wt%, and the pH of 8. The CMSP process parameter combination for the linear edge had the polishing plate's rotational speed of 90 rpm, the swing angle of 6°, and the input frequency of the controller of 5000 Hz. The optimum CMSP process parameter combination for the circular edge had the polishing plate's rotational speed of 90 rpm, the swing angle of 6°, and the input frequency of the controller of 7000 Hz. The polishing plate's rotational speed had the most significant impact on the edge preparation process, followed by the swing angle, and the effect of the input frequency of the controller was the smallest. This study demonstrated that CMSP is a potential way to treat the cemented carbide insert cutting edge in a tool enterprise.

INFLUENCE OF FUSED DEPOSITION MODELING REGIME PARAMETERS ON MANUFACTURING TIME OF UAV PARTS

Purpose. Investigate the regularities of FDM process parameters influence on the manufacturing time of parts.
 Research methods. The samples for research were produced by the FDM method on a Profi+midi 3D printer. Slic3rPE software were used for CAD model slicing and G-code preparation. The samples were printed using ABS+ filament. The STATISTICA software package was used for statistical processing of the results.
 Results. The influence of different combinations of FDM process parameters (printing speed, layer height, infill geometry, infill density) on the manufacturing time of products was examined. The main factors affecting the manufacturing time were established: printing speed, layer height, infill density and the interaction of these factors
 Scientific novelty. Analysis of the manufacturing time dependence on the layer height and the infill density showed that when selecting a layer height of 0.15 mm and 25 % infill and a layer height of 0.3 mm and 100% infill, the manufacturing time turned out to be nearly identical. It can be concluded that for a lower layer height, increasing the infill density significantly increased the manufacturing time, while setting a higher layer and increasing the infill density led to a small increase in the manufacturing time. It was found that for a layer height of 0.15 mm at a printing speed of 40 mm/s, the manufacturing time significantly increased compared to a printing speed of 80 mm/s, while when selecting a layer height of 0.3 mm, the manufacturing time is less and a decrease in the printing speed led to a small increase of manufacturing time compared to a layer height of 0.15 mm. It was established that an increase in printing speed led to a decrease in manufacturing time and an increase in infill density led to an increase in manufacturing time. Thus, the manufacturing time at a printing speed of 40 mm/s and infill density of 25 % differed slightly from a printing speed of 80 mm/s and infill density of 100 %.
 Practical value. A regression equation was obtained which allowed predicting the influence of the FDM process parameters on the manufacturing time of parts.

Parametric Modeling for Optimization of Deposition Rate in Wire Arc Additive Manufacturing of AISI 410 Stainless Steel Using Bead-on-Plate Specimens

The deposition rate is a critical parameter that has a significant influence in Wire Arc Additive Manufacturing (WAAM) of critical aerospace components made of AISI 410 Stainless steel. In this work, Taguchi parametric modeling for wire arc additive manufacturing is carried out using the input parameters of the welding current (WC), the travel speed of the nozzle (TSN), and the distance between the nozzle and the workpiece (DNW). Each factor is varied at 3 levels and the experiments are carried out based on Taguchi L9 orthogonal array. The response variable considered is the deposition rate (DR). Based on the results from experiments, the optimal combination of process parameters obtained is 140 A of welding current, 3mm/s of the travel speed of the nozzle, and 15 mm of distance between the nozzle and workpiece. The optimum deposition rate achieved is 8.545 g/s. The percentage of contribution of various factors is found to be TSN at 43.65% and WC at 16.6% and DNW at 4.9%.