Optimization of Bead Morphology for GMAW-Based Wire-Arc Additive Manufacturing of 2.25 Cr-1.0 Mo Steel Using Metal-Cored Wires

Determining optimal bead central angle by applying machine learning to wire arc additive manufacturing (WAAM)

Wire arc additive manufacturing (WAAM) is being extensively used in various industrial fields. In WAAM, if a bead is deposited without considering the central angle, its shape may collapse with increasing number of layers. To address this problem, a new method for optimizing the bead geometry using a support vector machine (SVM) classifier was established in this study. The ranges of the optimal deposition conditions were determined using the SVM classifier and verified by experiments. Geometric data of deposited beads were extracted using a laser profiler, and an SVM binary classifier was used to predict suitable ranges of the deposition conditions. Data were extracted through 20 single-layer basic experiments, classification was performed based on 4°, and the appropriateness of SVM classification was found through 8 single-layer and 3 multi-layer verification experiments. The results showed that the SVM classifier successfully selected the ranges of the optimal deposition conditions. Verification experiments revealed that the results in all cases were appropriately classified based on the boundary of the classification line. Moreover, the SVM classifier was efficient even when a small amount of input data was available. The contribution of this study is that the developed method can help build desired bead geometries in scenarios where deposition is required in the WAAM process, such as re-manufacturing. Thus, this method can be used in real-world industrial applications through further research on the bead shape with multi-layer deposition.

Fabrication of Multi-Walled Structure through Parametric Study of Bead Geometries of GMAW-Based WAAM Process of SS309L

In the present study, an attempt is made to investigate and optimize the bead geometries of bead width (BW) and bead height (BH) of SS-309L using an SS316L substrate by employing a gas metal arc welding (GMAW)-based wire-arc additive manufacturing (WAAM) process. The Box-Behnken design approach was used to conduct the trials of single-layer depositions with input variables of travel speed (TS), voltage (V), and gas mixture ratio (GMR). The developed multi-variable regression models were tested for feasibility using ANOVA and residual plots. The data obtained indicated that V had the most significant impact on BW, followed by TS and GMR. For BH, TS had the most significant impact, followed by GMR and V. The results of single-response optimization using a passing vehicle search (PVS) algorithm showed a maximum BH of 9.48 mm and a minimum BW of 5.90 mm. To tackle the contradictory situation, a multi-objective PVS algorithm was employed, which produced non-dominated solutions. A multi-layered structure was successfully fabricated at the optimal parametric settings of TS at 20 mm/s, of voltage at 22 V, and of GMR at 3. For multi-layer structures, fusion among the layers was observed to be good, and they were found to be free from the disbonding of layers. This revealed the suitability of the PVS algorithm for generating suitable optimal WAAM variables. We consider the current work highly beneficial for users fabricating multi-layer structures.

Implementation of Passing Vehicle Search Algorithm for Optimization of WEDM Process of Nickel-Based Superalloy Waspaloy

Nickel-based superalloys find their main use in missile engines, atomic devices, investigational aircraft, aerospace engineering, industrial applications, and automotive gas turbines, spacecraft petrochemical tools, steam power, submarines, and broader heating applications. These superalloys impose certain difficulties during the process fabrication owing to their levels of higher hardness. In the current study, the precise machining of Waspaloy was attempted through the wire electrical discharge machining (WEDM) technique. A multi-objective optimization has been performed, and the influence of multi-walled carbon nanotubes (MWCNTs) has been assessed using the passing vehicle search (PVS) algorithm. The effects of machining variables like current, T_off, and T_on were studied using the output measures of material removal rate (MRR), recast layer thickness (RLT), and surface roughness (SR). The Box-Behnken design was applied to generate the experimental matrix. Empirical models were generated which show the interrelationship among the process variables and output measures. The analysis of variance (ANOVA) method was used to check the adequacy, and suitability of the models and to understand the significance of the parameters. The PVS technique was executed for the optimization of MRR, SR, and RLT. Pareto fronts were derived which gives a choice to the user to select any point on the front as per the requirement. To enhance the machining performance, MWCNTs mixed dielectric fluid was utilized, and the effect of these MWCNTs was also analyzed on the surface defects. The use of MWCNTs at 1 g/L enhanced the performance of MRR, SR, and RLT by 65.70%, 50.68%, and 40.96%, respectively. Also, the addition of MWCNTs has shown that the machined surface largely reduces the surface defects.

Research on Injury Causes and Prevention Effect of College Rowing Athletes Based on Multiple Regression and Residual Algorithm

Rowing competition in colleges and universities is an international competition, and it is also a favorite competition for college students. However, in the course of rowing competition, the stability of athletes' injuries often occurs, which is difficult to solve effectively. Aiming at the problem that the loss of athletes in rowing competition in colleges and universities cannot be accurately prevented, this paper puts forward a multiple regression prevention effect model and makes a comprehensive analysis combined with complex reasons. Through the integration of multiple regression and residual analysis, we can better find out the influencing factors, aiming at finding out the causes of athletes' injuries and putting forward corresponding countermeasures. First of all, analyze the causes of loss, establish a framework of injury prevention for college rowers, and the overall diagnosis framework is reasonable. Then, according to the “University Rowing Prevention and Control Standards” divided into various prevention measures, through the comprehensive prevention and control measure mechanism to get the cause of injury, finally, the optimal combination of various control measures forms a control system. The results of MATLAB show that the combination of multiple regression and residual analysis can improve the accuracy of athletes' injury prevention and treatment, make the accuracy more than 90%, shorten the diagnosis time less than 10 minutes, and meet the requirements of athletes' injury diagnosis under normal rowing competition.

Construction and Validation of Simulation Models of Samples Made from 316L Steel by Applying Additive Technique

The main aim of the study includes research concerning the strength of samples printed out of 316L steel in the form of laminates and the creation of reflective simulation models with regard to the results obtained during the research. In addition, the tests addressed the effect of the arrangement of the printed layers on the final strength of the object. Static tensile tests allowed the material constants of 316L steel in the form of dimensionally printed laminate to be determined. Tests were conducted on samples with different printed angles. The tests also covered the impact of the printing envelope on samples with the printing angles. Based on the determined material constants, simulation models for calculations using the finite element method were created. Furthermore, the study includes analytical and simulation calculations of plain laminate in order to verify the accuracy of the Composite Layup module in Abaqus CAE software. The study was summarized by compiling and commenting on the results obtained from the conducted research. Tests showed that there is a possibility of simulating the strength of the printouts from 316L steel using the FEM calculations. It was shown that the FEM model results are similar to those obtained in the tests. The calculated errors were from 3.6 to 14.4%. The linear model describes well the first part of the stress-strain curve, but in further research, it is strongly recommended that a proper and checked nonlinear anisotropic one is presented.

Parametric Study and Investigations of Bead Geometries of GMAW-Based Wire–Arc Additive Manufacturing of 316L Stainless Steels

Appropriate selection of wire–arc additive manufacturing (WAAM) variables imparts bead geometries with characteristics of multi-layer structures. Thus, the present study aimed to optimize the gas metal arc welding (GMAW)-based WAAM variables of travel speed (TS), wire feed speed (WFS), and voltage (V) for the bead geometries of bead width (BW) and bead height (BH) on an SS 316L substrate. Single-layer depositions were made through a metallic wire of SS 316L by following an experimental matrix of the Box–Behnken design (BBD) technique. Multivariable regression equations were generated for design variables and responses, and ANOVA was used to investigate the feasibility of the obtained regression equations. WFS was the highest contributor affecting the BW, followed by V and TS, while WFS was again the highest contributor affecting the BH, followed by TS and V. Heat transfer search (HTS) optimization was used to attain optimal combinations. The single-objective optimization result showed a maximum bead height and minimum bead width of 6.72 mm and 3.72 mm, respectively. A multi-layer structure was then fabricated by considering an optimization case study, and it showed optimized parameters at a WFS of 5.50 m/min, TS of 141 mm/min, and voltage of 19 V with the bead height and bead width of 5.01 mm and 7.81 mm, respectively. The multi-layered structure obtained at the optimized parameter was found to be free from disbonding, and seamless fusion was detected between the obtained layers of the structure. The authors believe that the present study will be beneficial for industrial applications for the fabrication of multi-layer structures.

Parametric Optimization and Influence of Near-Dry WEDM Variables on Nitinol Shape Memory Alloy

Nitinol-shape memory alloys (SMAs) are widely preferred for applications of automobile, biomedical, aerospace, robotics, and other industrial area. Therefore, precise machining of Nitinol SMA plays a vital role in achieving better surface roughness, higher productivity and geometrical accuracy for the manufacturing of devices. Wire electric discharge machining (WEDM) has proven to be an appropriate technique for machining nitinol shape memory alloy (SMA). The present study investigated the influence of near-dry WEDM technique to reduce the environmental impact from wet WEDM. A parametric optimization was carried out with the consideration of design variables of current, pulse-on-time (T_on), and pulse-off-time (T_off) and their effect were studied on output characteristics of material removal rate (MRR), and surface roughness (SR) for near-dry WEDM of nitinol SMA. ANOVA was carried out for MRR, and SR using statistical analysis to investigate the impact of design variables on response measures. ANOVA results depicted the significance of the developed quadratic model for both MRR and SR. Current, and T_on were found to be major contributors on the response value of MRR, and SR, respectively. A teaching–learning-based optimization (TLBO) algorithm was employed to find the optimal combination of process parameters. Single-response optimization has yielded a maximum MRR of 1.114 mm³/s at T_on of 95 µs, T_off of 9 µs, current of 6 A. Least SR was obtained at T_on of 35 µs, T_off of 27 µs, current of 2 A with a predicted value of 2.81 µm. Near-dry WEDM process yielded an 8.94% reduction in MRR in comparison with wet-WEDM, while the performance of SR has been substantially improved by 41.56%. As per the obtained results from SEM micrographs, low viscosity, reduced thermal energy at IEG, and improved flushing of eroded material for air-mist mixture during NDWEDM has provided better surface morphology over the wet-WEDM process in terms of reduction in surface defects and better surface quality of nitinol SMA. Thus, for obtaining the better surface quality with reduced surface defects, near-dry WEDM process is largely suitable.