Combination Of Process Parameters Research Articles

Combined influence of traverse and rotation speeds in friction stir processing OF AlSi10Mg alloy produced by laser-powder bed fusion

In this study, friction stir processing (FSP) was used to enhance the corrosion properties through local modification of the microstructure of AlSi10Mg alloy fabricated by laser-powder bed fusion (L-PBF) technology. The impact of traverse and rotational speeds on the mechanical and electrochemical properties of L-PBF AlSi10Mg was analyzed with different combinations of processing parameters, with rotational speeds ranging from 800 to 1600 rpm and traverse speeds from 100 to 300 mm/min. The experimental results showed that the Si content within the stir zone is dependent on the combined influence of the rotational and traverse speeds, with a lower traverse speed promoting an enhanced grain refinement and a more homogenous Si distribution, indicated by a low LAGBs with a high HAGBs, resulting in the decrease of the hardness value and improvement of the corrosion performance of the processed material. The lowest hardness was found on the samples processed with a traverse speed of 100 mm/min and the best corrosion performance was observed from the samples processed with a rotational speed of 1600 rpm and a traverse speed of 100 mm/min. The results also indicated that the rotational speed showed a major role on the process, with the influence of the traverse speed diminishing when the material is processed at higher rotational speeds.

Multi-objective optimization of high-power fiber laser cutting process using data augmentation-based ANN-Adam model

During fiber laser cutting of sheet metal, excessive heat-affected zone (HAZ) and recast layer thicknesses (RLT) can adversely affect subsequent precision machining and welding processes on the parts. As a result, it is necessary to optimize the process parameters to control the HAZ and RLT. In this work, laser power, gas pressure, focal plane position (FPP), and cutting speed are selected as input parameters. The cutting experiments are conducted using response surface methodology, and the factors that significantly affect the cutting quality are identified by analysis of variance (ANOVA). To improve the generalization ability and robustness of the model, a data augmentation-based artificial neural network (ANN) model is developed to achieve accurate prediction of cut quality characteristics. Further, this study performs a multi-objective optimization of the cutting process parameters using the Adam optimizer. The research results show that the mean relative errors of the ANN model in predicting the HAZ and RLT are 2.44 % and 1.55 %, respectively. The determination coefficients R2 are 0.9542 and 0.9779, respectively. The optimal combinations of process parameters obtained through optimization are laser power 8.9 kW, gas pressure 0.8 bar, FPP 11.8 mm, and cutting speed 1.3 m/min. Experimental validation shows that the relative errors between the predicted and the experiment values of the cut quality characteristics are kept within 5 % under the optimal process parameters. This fully verifies the effectiveness of the optimization model in guiding the selection of laser-cutting process parameters and improving cutting quality.

An investigation of the ensemble machine learning techniques for predicting mechanical properties of printed parts in additive manufacturing

This study investigates the ensemble machine learning models to predict the mechanical properties of the 3D-printed Polylactic Acid (PLA) specimens. We studied the effects of five process parameters, including the build orientation, infill angle, layer thickness, printing speed, and nozzle temperature, on the printed parts tensile strength and surface roughness. Machine learning models are developed using the experimental data collected from the printed 27 specimens. Gradient Boosting Regression, Extreme Gradient Boosting Regression, Adaptive Boosting Regression, Random Forest Regression, and Extremely Randomized Tree Regression models were developed during the machine learning modeling stage to predict the surface roughness and tensile strength of the printed parts. This research demonstrates the effectiveness of Extremely Randomized Tree Regression model in providing accurate tensile strength predictions with root mean square error (RMSE) of 1.03, mean absolute error (MAE) of 0.82, and mean absolute percentage error (MAPE) of 2.20%. Similarly, Random Forest Regression model shows better accuracy in predicting surface roughness having RMSE of 0.408, MAE of 0.31, and MAPE of 9.28%. Moreover, the comparative study confirms that ensemble machine learning techniques are more useful than the traditional support vector and k-nearest neighbor machine learning models for predicting the surface roughness and tensile strength of the printed parts. The results highlight a novel approach of using ensemble machine learning models in identifying complex correlations in the dataset, establishing the foundation for improved product design and mechanical property optimization through adjustment of the process parameters combination.

Precision control and parameter optimization in screw extrusion 3D printing of polypropylene materials

Fused Deposition Modeling (FDM), a widely-utilized additive manufacturing (AM) technology, has found significant favor among automotive manufacturers. Polypropylene (PP) compound is extensively employed in the production of automotive parts due to its superior mechanical properties and formability. However, aiming at the problem of poor dimensional accuracy of pure PP parts, the quality of products can be enhanced by optimizing the combination of processing parameters. In this paper, the dimensional accuracy of 3D-printed components made from pure PP material is investigated. Key influencing factors such as infill percentage, infill pattern, layer thickness, and extrusion temperature are considered. To gain a deeper understanding, fluid simulation is conducted, and mathematical models are established to correlate processing parameters with dimensional accuracy. Furthermore, the Taguchi's experiments are designed and the experimental data are subjected to rigorous Signal-to-Noise ratio and ANOVA analyses. Within the experimental range, the lower extrusion temperature, infill percentage and layer thickness yield the best dimensional accuracy. Considering the influence factors of X, Y and Z directions, the optimal processing parameters for PP prints using screw extrusion 3D printers are determined as follows: an extrusion temperature of 210 °C, an infill percentage of 40 %, a layer thickness of 0.3 mm, and a concentric circle infill pattern. This study provides reference value for the subsequent improvement of the dimensional accuracy of the printed parts.

Orthogonal experimental study of ultrasound-assisted fused deposition molding

To improve the quality of fused deposition molding (FDM), we introduced ultrasonic vibration into the molding process to enhance the strength of the tensile samples. We used the response surface method to optimize the FDM molding process. We carried out orthogonal experiments to investigate the effects of ultrasonic power and process parameters (extrusion temperature, hotbed temperature, and delamination thickness) on the mechanical properties of fused deposition acrylonitrile butadiene styrene products. We obtained the quadratic regression equations and response surfaces of the data through analysis of variance and significance analysis by software. Last, we received the optimum combination of process parameters for tensile strength as extrusion temperature 233.85 °C, hotbed temperature 93.38 °C, delamination thickness 0.1 mm, and ultrasonic power 43.09 W.

Parametric analysis, modeling and optimization of the process parameters in electric discharge machining of aluminium metal matrix composite

Optimizing electric discharge machining (EDM) for aluminum/SiCp metal matrix composites poses challenges due to intricate machine parameters and process complexity, impacting process economy and elevating product costs. The research aims to find the optimal combination of process parameters which include pulse on-time, pulse current, duty cycle (%), gap voltage, sensitivity and flushing pressure for EDM of Al/SiCp-MMC using a copper electrode for the selected response factors such as material erosion rate and surface roughness, Ra. The experiments were designed using the central composite design of response surface methodology and an advanced optimization technique known as Teaching–learning-based optimization (TLBO), is applied to find the optimal combination of process parameters to obtain maximum material erosion rate subject to the desired range of surface roughness (SR), Ra. The combination of the high pulse on-time (i.e. 150 μs) and high pulse current (i.e. 12A) results in high material removal rate with deep craters on the machined surface clearly visible in SEM images contrasting the minimized surface roughness at lower values of pulse on-time (50 μs) and the pulse current (4A). Pulse on - time (Ton) is found to be the most significant factor for material erosion rate and surface roughness with percentage contribution of 70.86 and 54.9 respectively for optimization of the response. The regression models were developed at 95% confidence level for material removal rate and surface roughness with R2 value of 0.93 and 0.95 respectively signifying high degree of accuracy in predicting the response. Confirmation tests conducted to check the adequacy of the established models revealed that the percentage error between the predicted and experimental responses is found to be within acceptable levels. Electron discharge machining of the aluminium metal matrix composite at the optimized conditions could provide economical aspect in the aerospace and automobile industry.

Research on cutting force characteristics of laser ultrasonically assisted turning of cemented carbide

Cemented carbide possesses excellent characteristics such as high strength and hardness, but its poor machinability has restricted its widespread application. Laser ultrasonically assisted turning (LUAT) can enhance the machinability of workpieces and is widely used in precision machining of difficult-to-machine materials. In this paper, LUAT is applied to the cutting of cemented carbide, and the characteristics of cutting force in the machining process are studied. A theoretical model of cutting force is established by analyzing the interaction between the cutting tool, chip and workpiece during the turning process. The maximum error was found to be 18.6% by comparing with the experimental data, which better predicted the changing trend of cutting force and verified the accuracy of the model. Furthermore, experiments on LUAT of cemented carbide are carried out using the Taguchi method. The influence of cutting tools and processing parameters on cutting force is analyzed, revealing that the amplitude has the most significant impact on cutting force. The optimal combination of processing parameters is also obtained. Finally, a comparison is made between different turning methods. Compared to conventional turning, laser assisted turning, and ultrasonically assisted turning, the total cutting force in LUAT is reduced by 53.44%, 45.62%, and 21.64%, respectively, resulting in improved surface quality of the workpiece.

Optimization of porosity behavior of hybrid reinforced titanium metal matrix composite through RSM, ANN, and GA for multi-objective parameters

Titanium matrix composites (TMCs) have high specific strength and stiffness, and high-temperature TMCs can reduce weight by up to 50% when compared with monolithic super alloys while preserving equal stiffness and strength in jet engine systems for propulsion. The purpose of this work examines the use of mathematical models and learning approaches to optimize response such as porosity and control variables in synthesized hybrid titanium metal matrix composites (HTMMCs) reinforced by B4C-SiC-MoS2-ZrO2. To further understand the impacts of process factors on porosity reduction, the study employs methodologies such as the response surface methodology (RSM), integrated artificial neural networks (ANN), and genetic algorithm (GA). The findings indicate that these strategies have the potential to contribute to the industry. The optimal combination of 7.5wt.% SiC, 7.5wt.% B4C, 7.5wt.% ZrO2, 4wt.% MoS2, and 73.5wt.% Ti compositions was determined utilizing process factors such as milling period (6h), compaction pressure (50MPa), compact duration (50min), sintering temperature (1200°C), and sintering time (2h) as compared to pure Ti grade 5. The mechanical properties of the optimum combination of reinforcement weight percentage and process parameters resulted in a minimum porosity of 0.118%, density of 4.36gcm3, and micro-hardness of 63.4HRC boosted by 1.76%, and compressive strength of 2500MPa increased by 2.6%. In addition, these HTMMCs had a minimal wear rate of 0.176mm3/Nm and a corrosion resistance rate of 2.15×10-4mmpy. The investigation result analysis discovered that the RSM and combined ANN-GA models considerably enhanced the forecasting of multidimensional interaction difficulties in composite material production that were highly statistically connected, with R2 values of 0.9552 and 0.97984. The ANN-GA model provided a 95% confidence range for porosity predictions, which increased the production use of titanium-based particle composites. Furthermore, HMMCs can be utilized in the automotive and aviation industries with enhanced corrosion and wear resistance.

Numerical Simulation of Lost-Foam Casting for Key Components of A356 Aluminum Alloy in New Energy Vehicles.

The intricate geometry and thin walls of the motor housing in new energy vehicles render it susceptible to casting defects during conventional casting processes. However, the lost-foam casting process holds a unique advantage in eliminating casting defects and ensuring the strength and air-tightness of thin-walled castings. In this paper, the lost-foam casting process of thin-walled A356 alloy motor housing was simulated using ProCAST software (2016.0). The results indicate that the filling process is stable and exhibits characteristics of diffusive filling. Solidification occurs gradually from thin to thick. Defect positions are accurately predicted. Through analysis of the defect volume range, the optimal process parameter combination is determined to be a pouring temperature of 700 °C, an interfacial heat transfer coefficient of 50, and a sand thermal conductivity coefficient of 0.5. Microscopic analysis of the motor housing fabricated using the process optimized through numerical simulations reveals the absence of defects such as shrinkage at critical locations.

OPTIMASI MULTI PARAMETER MESIN 3D PRINTER FDM UNTUK MATERIAL THERMOPLASTIC POLYURETHANE GUNA MENGHASILKAN ERROR DIMENSI TERKECIL MENGGUNAKAN METODE 2K FACTORIAL DESIGN

Fused Deposition Modeling or FDM is a 3D printing technology that is commonly used in manufacturing processes based on Additive Manufacturing Technology. One of the materials that can be used on FDM machines is thermoplastic polyurethane (TPU). One measure of the quality of printed results is the machine's accuracy in producing workpieces that have the smallest possible dimensional error. So this research aims to obtain the printing parameters that have the most influence on print results and find out the optimal combination of printing process parameters on the machine to produce the smallest dimensional error for TPU material. The design of experiment method used is 2k factorial design using 4 process parameters, 2 levels and 3 responses. The parameters used are nozzle temperature, print speed, fill density, and enclosure box. The optimization results obtained an optimal combination of printing process parameters to produce the smallest dimensional error, namely nozzle temperature of 210°C, print speed of 30 mm/s, fill density of 100%, and not using an enclosure box.

Modelling and Prediction of Process Parameters with Low Energy Consumption in Wire Arc Additive Manufacturing Based on Machine Learning

Modelling and Prediction of Process Parameters with Low Energy Consumption in Wire Arc Additive Manufacturing Based on Machine Learning

Research on Multi-Objective Process Parameter Optimization Method in Hard Turning Based on an Improved NSGA-II Algorithm

To address the issue of local optima encountered during the multi-objective optimization process with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm, this paper introduces an enhanced version of the NSGA-II. This improved NSGA-II incorporates polynomial and simulated binary crossover operators into the genetic algorithm’s crossover phase to refine its performance. For evaluation purposes, the classic ZDT benchmark functions are employed. The findings reveal that the enhanced NSGA-II algorithm achieves higher convergence accuracy and surpasses the performance of the original NSGA-II algorithm. When applied to the machining of the high-hardness material 20MnCrTi, four algorithms were utilized: the improved NSGA-II, the conventional NSGA-II, NSGA-III, and MOEA/D. The experimental outcomes show that the improved NSGA-II algorithm delivers a more optimal combination of process parameters, effectively enhancing the workpiece’s surface roughness and material removal rate. This leads to a significant improvement in the machining quality of the workpiece surface, demonstrating the superiority of the improved algorithm in optimizing machining processes.

Parameter optimization, microstructural and mechanical properties of fiber laser lap welds of DP1200 steel sheets

In this research, the effects of laser power, laser welding speed and laser incidence angle were evaluated in terms of weld geometry, microstructure, microhardness, fracture surfaces and tensile shear load in fiber laser welding joints of DP1200 steel sheets. Response Surface Methodology (RSM) was utilized to derive mathematical relationships between the process parameters and the tensile shear load after the tensile test. The optimum combination of process parameters was a laser power of 2800 W, a laser welding speed of 40 mm/s, and a laser incidence angle of 70°. A thermal camera was used to record the laser welding process, and the cooling rates were evaluated by analyzing this data. The influence of cooling rate upon microstructure phase formation for DP1200 steel is investigated, with continuous cooling transformation (CCT) diagrams. Also, post-welding stress and displacement were simulated by Simufact Welding software. At high heat input (55.79 J/mm) occurred coarse dendritic martensitic lath growth while at low heat input (37.6 J/mm) fine dendritic martensitic structure was observed in the weld zone was obtained owing to the high rate of cooling. The microstructure consists of full martensite at a higher than 10 °C/s cooling rate. The findings reveal that the phases in FZ and HAZ, the morphology and martensite/bainite constituents differed related to the heat input and laser incidence angle, which ultimately affects the joint’s microhardness, fracture dynamics and tensile shear load.

Multi-sensor-based process monitoring and quality assessment during laser forming of novel titanium foam to prevent cellular structure damage

Titanium foam finds its application in different fields like battery electrodes, heat exchangers, biological prosthetics, and lightweight structures for aeronautical parts. However, the inherent property of the foam to fail during mechanical loading conditions poses a challenge in providing final shape to these materials for practical applications. The present work investigates forming of Titanium foam (Ti Foam) using inline process monitoring for achieving controlled foam deformation while maintaining its quality without failure. Infrared (IR) pyrometer and laser-based displacement sensor were used to monitor the foam deformation behavior and thermal signature. The monitoring data were used to understand the process mechanism of laser forming. The bending mechanism within the foam was analysed with respect to solid sheet bending process. The correlation of the achieved bending angles, was attempted with the process parameters for surface, microstructural, elemental, and phase transformation. A critical analysis of Ti foam behavior, like surface melting, in situ oxidation, phase transformation, and formation of nano-structures on foam surface was addressed based on process parameter combinations. Micro-computed tomography (μCT) was carried out to investigate the pore deformation mechanism. Metallurgical characterisations for foam surface and phase analysis were carried out using X-ray photoelectron spectroscopy (XPS) and tunneling electron microscopy (TEM) to determine the chemical and crystallographic states of the laser-formed surface.

Parameters Optimization and Physicochemical Properties Characterization of Nanobiochar Prepared from Apple Branches by Ball Milling

Nanobiochar exhibits promising applications across a spectrum of fields due to its exceptional physicochemical properties. Ball milling, a prominent nanobiochar synthesis method, is influenced by process factors and biochar pretreatments, yet dominant controls still remain uncertain. In this study, the apple branch bulk biochar (pyrolyzed under [Formula: see text][Formula: see text]C) was ball milled under different milling speeds, ball-to-powder mass ratios, and milling durations to investigate the optimal process parameter combinations for ball milling method. Then, we evaluated the impact of pyrolysis temperature and pretreatment temperature of bulk biochar on ball milling effect using optimized parameter combinations. Subsequently, the physicochemical properties of the bulk biochar and nanobiochar were measured. The results showed that high speed with low ball-to-powder mass ratio, low speed with high ball-to-powder mass ratio, and high pyrolysis temperature could significantly decrease the particle sizes of nanobiochar. Ball milling duration and pretreatment temperature had no significant effect on the particle sizes of biochar. The optimal combination of the rotational speed, ball-to-powder mass ratio and milling duration was 400 RPM, 100:1, 2[Formula: see text]h and 700 RPM, 20:1, 2[Formula: see text]h. The bulk biochar pyrolyzed at [Formula: see text][Formula: see text]C could be milled into biochar nanoparticles with an average particle size smaller than 100[Formula: see text]nm via above parameters. Nanobiochar shows improved properties such as specific surface area, cation exchange capacity, and surface functional groups over bulk biochar. This study provides a scientific basis for the widespread application of ball milling in the field of preparing nanobiochar.

Optimization of parameters and formulation of numerical model employing GRA–PCA and RSM approach for friction stir welded Ti–6Al–4V alloy joints

In this work, an endeavour was made to determine the impact of the tool’s shoulder diameter, pin profile, rotational and traverse speeds on the mechanical properties of the friction stir welded namely Ti–6Al–4 V alloy joints. A total of 21 experiments were carried out based on the central composite design (CCD) concept of response surface methodology (RSM). A quadratic regression based numerical model was formulated to ascertain the relationship amidst the parameters of FSW process and the mechanical properties of the fabricated Ti alloy joints. Analysis of variance (ANOVA) was employed to confirm the importance of parameters and their interactive impacts. Optimized process parameter combinations were ascertained by grey relation based analysis (GRA) was coupled together with principal component analysis (PCA), a hybrid based approach. Single score of GRG based response was determined and GRG scores were ranked for all experiments. 1st rank was attained by the experiment no. 20 possessing a GRG score of 2.9989. Optimized values of 25 mm shoulder diameter having a taper cylindrical pin geometry employed at a tool traverse speed of 40 mm min−1, rotational speed of 1400 rpm resulted in the generation of flaw free Ti alloy joint possessing a largest tensile strength of 809.8 MPa, yield strength of 778.7 MPa and percentage of elongation of 6.9%. SEM based fractography of Ti alloy joint specimens announced that taper cylindrical pin geometry along with 25 m shoulder diameter of employed tool have an inevitable part in generating frictional heat in ideal volumes and a perfect stirring action during FSW process.

Enhancing Electrical Conductivity of Stretchable Liquid Metal-Silver Composites through Direct Ink Writing.

Structure-property-process relationships are a controlling factor in the performance of materials. This offers opportunities in emerging areas, such as stretchable conductors, to control process conditions during printing to enhance performance. Herein, by systematically tuning direct ink write (DIW) process parameters, the electrical conductivity of multiphase liquid metal (LM)-silver stretchable conductors is increased by a maximum of 400% to over 1.06 × 106 S·m-1. This is achieved by modulating the DIW print velocity, which enables the in situ elongation, coalescence, and percolation of these multiphase inclusions during printing. These DIW printed filaments are conductive as fabricated and are soft (modulus as low as 1.1 MPa), stretchable (strain limit >800%), and show strain invariant conductivity up to 80% strain. These capabilities are demonstrated through a set of electromagnetic induction coils that can transfer power wirelessly through air and water, even under deformation. This work provides a methodology to program properties in stretchable conductors, where the combination of material composition and process parameters leads to greatly enhanced performance. This approach can find use in applications such as soft robots, soft electronics, and printed materials for deformable, yet highly functional devices.

Construction of a Cutting-Tool Wear Prediction Model through Ensemble Learning

This study begins by conducting side milling experiments on SKD11 using tungsten carbide TiAlN-coated end mills to compare the surface roughness performance between two combinations of milling process parameters (feed rate and radial depth of cut), along with three ultrasonic-assisted methods (rotary, dual-axis, and rotary combined with dual-axis). The results suggest that the rotary (z-axis oscillation) ultrasonic-assisted method may provide better performance. Subsequently, this superior ultrasonic-assisted method was applied both with and without laser locally preheating assistance, respectively. Using a Taguchi orthogonal array, milling process parameters (spindle speed, feed rate, and radial depth of cut) were planned for experiments with the same cutting tool and the workpiece just mentioned above. The surface roughness serves as the objective function while being constrained by cutting-tool life. The characteristics of the smaller-the-better in the Taguchi method were applied to determine the optimal combination of process parameters. Based on the optimal milling process parameters obtained and the superior hybrid-assisted method adopted, milling experiments were repeatedly performed to collect the data on cutting force and cutting-tool wear. Feature engineering was performed on the cutting force signals, and different domain characteristics from both the time and frequency domains were extracted. Hereafter, feature selection by random forest and data standardization were further applied to feature extractions, and the data processing was thus completed. For the processed data, a cutting-tool wear prediction model was constructed by ensemble learning. This method leverages various machine learning regression models, including decision tree, random forest, extremely randomized tree, light gradient boosting machine, extreme gradient boosting, AdaBoost, stochastic gradient descent, support vector regression, linear support vector regression, and multilayer perceptron. After hyper-parameter tuning, the ensemble voting regression prediction was performed based on these ten mentioned models. The experimental results demonstrate that the ensemble voting regression model surpasses the performance of each individual machine learning regression model. In addition, this regression model achieves a coefficient of determination (R2) of 0.94576, a root mean square error (RMSE) of 0.24348, a mean squared error (MSE) of 0.05928, and a mean absolute error (MAE) of 0.18182. Therefore, the ensemble learning approach has been proven to be a feasible and effective method for monitoring cutting-tool wear.

Process Optimization of Pellet Manufacturing from Mixed Materials in Ultrasonic Vibration-Assisted Pelleting

Achieving carbon neutrality and alleviating the rural energy predicament are crucial aspects in rural areas, particularly in the severe cold regions of northeast China. Pellets serve as clean, renewable energy sources and are ideal alternative fuels. This study investigated the influencing factors and effects of mixed raw materials in ultrasonic vibration-assisted pelleting (UV-A pelleting). Rice straw and corn stover were mixed to produce pellets, and a central composite rotatable design (CCRD) was conducted to analyze the variables and their interactions on pellet density and durability. Mathematical regression models for pellet density and durability were established and then validated through ANOVA analysis. The results showed that all variables significantly affected the density and durability of pellets. The mixing ratio had a greater impact on pellet durability compared to density due to differences in ingredients. The optimal combination of process parameters included a mixing ratio of 25%, molding pressure of 4 MPa, pelleting time of 37 s, and ultrasonic power output at 200 W, resulting in a pellet density of 1301.18 kg/m3 with a durability reaching 94.26%. The desirability value (0.997) under these optimal conditions confirmed the validity of the models; further experiments also verified their effectiveness. The combustion of the optimized pellet was analyzed using thermogravimetric (TG) and derivative thermogravimetric (DTG) analysis in an air atmosphere. Four combustion stages and ignition temperature were provided.

Enhanced formation quality and tensile shear load of 7B04 aluminum alloys lap joint by impacting flow friction stir lap welding

Avoiding the upward-bending hook is a highly pressing and challenging task in the field of similar aluminum alloys friction stir lap welding (FSLW). To enhance formation quality and then obtain the welded lap joint with high tensile shear load, a novel impacting flow FSLW (IF-FSLW) process based on the X-shape reverse-threaded pin was proposed in this study. Three welding tools with different tool geometry parameters were developed to analyze how the locations of the materials accumulated zone (MAZ) influenced the joint formation and the joint loading capacity, and 7B04 aluminum alloys were chosen as research subject. Results showed that the horizontally-impacting and vertically-impacting effects of MAZ on and above the original lap interface made the hook respectively present the forky structure at the end and the downward-bending morphology, and the horizontally-impacting effect of MAZ made the stir zone (SZ) have a protruding morphology near the MAZ. Under the tensile fracture mode, the IF-FSLW joint under configuration A had a greatly heightened tensile shear load under the combined actions of the downward-bending hook and the enlarged SZ. The tensile shear load of IF-FSLW joint reached 8.62 kN/mm under the reasonable combination of tool geometry parameters and welding process parameters. The IF-FSLW process by the X-shape reverse-threaded pin provides an effective way to obtain a superb-strength lap joint of aluminum alloys.