Axial Depth Research Articles

Indirect measurement of process parameters in peripheral end milling based on acoustic emission signals

In machining, current methods for online estimation of cutting parameters heavily rely on piezoelectric dynamometers. Albeit their high precision, integration into industrial environments is limited due to high cost and practical machine mounting constraints. To address this challenge, this study presents an indirect measurement method of the cutting depths in peripheral milling based on acoustic emission sensors. The proposed method involves extracting time-based features from a tailor-made envelope of the acoustic emission signal, identifying transitions between cutting phases from its waveform, and using these features to estimate radial and axial depths of cut through a geometric model of the tool-workpiece contact zone. Experiments show that this method can achieve relative errors below 5% in measuring the depths of cut, approaching the precision levels of piezoelectric dynamometers. This suggests that acoustic emission is an alternative process variable, enriching the spectrum of monitoring systems traditionally dependent on the measurement of cutting forces.

Investigation into the Failure Characteristics and Mechanism of Rock with Single Elliptical Defects under Ultrasonic Vibrations

In order to investigate the effects of elliptical defects on rock failure under ultrasonic vibrations, ultrasonic vibration tests and PFC2D numerical simulations were conducted on rocks with single elliptical defects. The research results indicated that the fracture fractal dimension, axial strain, and crack depth of specimens with elliptical defects at 45° and 90° were the smallest and largest, respectively. The corresponding strain and fractal dimension showed a positive linear and logarithmic function relationship with time. The maximum crack depth of 46.50 mm was observed on the specimens with an elliptical defect angle of 90°. Specimens with elliptical defects at 0°, 30°, 75°, and 90° exhibited more dense and frequent acoustic emission events than those with elliptical defects at 15°, 45°, and 60°. During the ultrasonic vibration process, the maximum total energy (87.86 kJ) and energy consumption coefficient (0.963) were observed on specimens with elliptical defect angles of 30° and 45°, respectively. The difference in the stress field led to varying degrees of plastic strain energy in the specimens, resulting in different forms of crack propagation and triggering differential acoustic emission events, ultimately leading to specimen failure with different crack shapes and depths. The fractal dimensions of elliptical defect specimens under ultrasonic vibration have a high degree of consistency with the changes in axial strain and failure depth, and the fractal dimension of defect specimens is positively correlated with the degree of failure of defect specimens.

Multi-nanoparticle recognition and tracking based on SPTGAN-YOLOv3 modeling

Nanoparticle tracking techniques based on point spread function (PSF) engineering can monitor the movement of nanoparticles in 3D space in real time. However, increasing the axial tracking depth decreases the signal-to-noise ratio (SNR), which makes the reconstruction of nanoparticle trajectories more difficult. This paper proposes a deep learning-based object detection model SPTGAN-YOLOv3 for nanoparticle tracking technique. The SPTGAN-YOLOv3 model utilizes a noisy image with a low SNR as its input. It learns the mapping from noisy to noise-free images, generating a high SNR image. This rendered image is then used as the input for the object detection network, enabling the recognition and tracking of nanoparticles. Comparative analysis with conventional particle tracking methods demonstrates that the SPTGAN-YOLOv3 model significantly improves the precision and efficiency in recognizing and tracking multiple nanoparticles. Moreover, experimental results indicate that this technique notably enhances the SNR of nanoparticle tracking images and enables precise identification of nanoparticles.

Stability Analysis of Milling Based on the Barycentric Rational Interpolation Differential Quadrature Method

Chatter causes great damage to the machining process, and the selection of appropriate process parameters through chatter stability analysis is of great significance for achieving chatter-free machining. This article proposes a milling stability analysis method based on the barycentric rational interpolation differential quadrature method (DQM). The dynamics of the milling process considering the regeneration effect is first modelled as a time-delay differential equation (DDE). When adjacent pitch angles of the milling cutter are symmetric, the milling dynamic equation contains a single time delay. Otherwise, when adjacent pitch angles are asymmetric, the dynamic equation contains multiple time delays. The barycentric rational interpolation DQM is then used to approximate the differential and delay terms of the milling dynamics equation, and to construct a state transition matrix between adjacent milling periods. Finally, the chatter stability lobe diagram (SLD) is obtained based on the Floquet theory. According to the SLD, the appropriate spindle speed can be selected to obtain the maximum stable axial depth of cutting, thereby effectively improving the material removal rate. The accuracy and efficiency of the proposed method have been validated by two widely used milling models, and the results show that the proposed method has good accuracy and computational efficiency.

Novel methods for optimizing CNC aluminum alloy machining parameters in polymer mold cavities

The examination of the machining of 7075-T6 aluminum alloy polymer mold cavities using Taguchi optimization and analysis of variance is presented in this paper. This study identified the best CNC milling cutting parameters and used a mathematical model to quantify the surface roughness of the machined cavities. The findings showed that while using a flat endmill, the spindle speed multiplied by feed rate contributed 28.01% to surface roughness, and when using a ball endmill, the squared depth of cut contributed 41.27%. Using both flat and ball endmills, the depth of the cut contributed 98.53% to the material removal rate. A refined second-order linear regression model was employed to forecast the endmill-machined surface roughness. The Warp Surf Portable tester measured values that were outside the error range of approximately 0.257% and 2.8%, respectively, for the expected values. Surface roughness has a 99.97% correlation coefficient in the regression model, indicating a very significant link. Additionally, the study improved the cutting parameters for a ball endmill, which were 3005 Rpm, 726.7 mm/min, and 0.43 mm, and for a flat endmill, these were spindle speed (2500 Rpm), feed rate (650 mm/min), and axial cut depth (0.5 mm). The outcomes demonstrated how well the techniques enhanced mold cavity machining and cost estimation using Ra and MRR data. Consequently, these results can be applied to future academic studies and industrial applications.

Research on theoretical and experimental investigations of burr formation mechanism in micro-milling process of medical ploy-l-lactide

Medical grade poly-l-lactide (PLLA) has been widely applied for medical devices and consumables, e.g. biodegradable cardiovascular stents and surgical thread, due to its excellent biocompatibility. High-speed micro-milling technology shows great potential in high-precision and high-quality manufacturing of material PLLA, while it usually produces problems such as material burrs and defects. There is few research and analysis on the material removal mechanism and theory in the micro-milling process of polymers, and thus this paper aims to investigate the burr formation mechanism and processing quality in micro-milling process of medical PLLA. Firstly, a novel thermal-mechanical coupled theoretical model was proposed by considering the minimum cutting thickness and cutting-edge radius size effects, which can reveal the plastic flow law during micro-milling process of polymer material. This model was used to calculate the burr width at up and down milling sides of cutting area. Then material characterization and micro-milling experiments on two types of PLLA with different molecular weights were carried out to validate the established theoretical model, and further the micro-milling parameters were optimized to achieve the control of material burr formation and growth. As a result, a cardiovascular stent was fabricated by micro-milling with the optimized parameters (spindle speed of 25,000 rpm, feed rate of 100 mm/min and axial depth of cut of 5 μm), and the stents obtained significantly high surface quality. These results suggest that the proposed theoretical model has great advantages in the high-quality micro-milling of medical material PLLA. This work provides a potential stent manufacture technique in the development and clinical application of bioresorbable polymeric stents.

Application of minimum quantity GnP nanofluid and cryogenic LN2 in the machining of Hastelloy C276

The current study delves into the combined influence of lubrication and cooling on the machinability of a difficult-to-cut superalloy. A comparative study of four lubricating mediums—dry, Minimum Quantity Lubrication (MQL), nanoMQL (NMQL), and Cryo-NMQL—was conducted during the milling of Hastelloy C276. Findings reveal that the Cryo-NMQL medium resulted in a 25.49%, 29.84%, and 42.50% decrease in cutting force, cutting temperature, and surface roughness, respectively, compared to dry cutting. This lubricating medium also decreased tool wear by 44.55%, as confirmed by SEM images showing reduced adhesion and abrasion. Analysis of chip morphology indicated a finer lamella structure with minimal serration under Cryo-NMQL, indicating enhanced efficiency. Furthermore, Cryo-NMQL refined the grain structure, enhancing microhardness for improved superalloy machining. ANOVA analysis identified feed rate and cutting speed as the most impactful parameters affecting the machining responses. Lastly, based on MORSM analysis, optimal machining parameters were determined as a cutting speed of 76.60 m/min, feed rate of 0.12 mm/tooth, radial depth of cut of 6.7 mm, and axial depth of cut of 0.6 mm. Experimental and predicted values closely matched, with a composite desirability score of 0.912, showcasing the efficacy of MORSM in addressing machining difficulties. This holistic investigation underscores Cryo-NMQL as a promising solution for the efficient and effective machining of superalloys.

Performance evaluation of coated tool in end milling of XH67MBTHO alloy

ABSTRACT XH67MBTHO is a promising nickel-based alloys which finds applications in aerospace engineering due to its exceptional hot hardness, corrosion resistance and strength at superior temperatures. However, due to its weak thermal conductivity(k=13.4W/mK) and high tendency to work harden, XH67MBTHO exhibits poor machinability. Therefore, the major objective of this study is to investigate the machinability characteristics of XH67MBTHO, using TiAlN coated milling inserts at three levels of speed, feed, and axial depth of cut. Significant parameters affecting responses were identified using ANOVA. SEM and EDS analysis of worn-out tool shows delamination, adhesion, abrasion and diffusion were major tool wear mechanism. Also, as machining conditions shift from higher to lower, tool life increases to 1200 seconds from 49.18 seconds and surface roughness decreases from 0.52 µm to 0.18 µm respectively. Also, a regression and Artificial Neural Network model were developed to predict the process response in terms of input parameters.

Parameters optimization for chatter-free milling tool path based on spindle speed variation method

Existing methods orienting for optimizing cutting parameters in milling process with spindle speed variation (SSV) only optimally selected the axial depth of cut and only considered the acceleration constraint of spindle. This article proposes a systematic optimization method to obtain the best combination of multiple machining parameters (axial and radial depths of cut) and SSV’s parameters (the ratio of variation amplitude and the ratio of variation frequency) so that the chatter-free milling tool path can be shortened. Both acceleration and jerk constraints of spindle are considered. A C3 continuous variable feedrate scheduling algorithm is used to eliminate the influence of spindle speed variation on feed per tooth, which is thus not required to treat as an optimization variable. The machining parameters of each iterative step are updated by following that the number of axial and radial cutting layers could be decreased. Discrete optimization method is combined with chatter stability analysis to solve the problem. A series of milling experiments verify that the proposed method can achieve the shortest tool path with chatter-free condition.

Roughness Parameters with Statistical Analysis and Modelling Using Artificial Neural Networks After Finish Milling of Magnesium Alloys with Different Edge Helix Angle Tools

The paper presents the results of a study investigating the roughness parameters Rq, Rt, Rv, and Rp of finished-milled magnesium alloys AZ91D and AZ31B. Carbide end mills with varying edge helix angles were used in the study. Statistical analysis was additionally performed for selected machining conditions. In addition, modelling of selected roughness parameters on the end face for the AZ91D alloy was carried out using artificial neural networks. Results have shown that the tool with λs = 20° is more suitable for the finish milling of magnesium alloys because its use leads to a significant reduction in surface roughness parameters with increased cutting speed. Increased feed per tooth leads to increased surface roughness parameters. Both radial and axial depth of cut has an insignificant effect on surface roughness parameters. It has been proven that finish milling is an effective finishing treatment for magnesium alloys. In addition, it was shown that artificial neural networks are a good tool for the prediction of selected surface roughness parameters after finishing milling of the magnesium alloy AZ91D.

Computational and Experimental Analysis of Surface Residual Stresses in Polymers via Micro-Milling.

This research conducts an in-depth investigation into the residual stresses in resin micro-milling processes. Considering that resin is the most crucial matrix material in composites, the construction of a precise machining theory for it is not only key to achieving high-quality- and efficient processing of composite materials but also fundamental to enhancing the overall performance of the materials. This paper meticulously examines the surface integrity and accuracy of epoxy polymers following precision machining, primarily revealing the significance of residual stresses and size effects in extending the lifespan of precision components and promoting their miniaturization. We have adopted an innovative finite element (FE) simulation method, integrated with the Mulliken-Boyce constitutive model, to profoundly analyze the impacts of residual stresses on the surfaces and sub-surfaces of thermosetting polymers. This research further explores the influence of critical machining parameters such as chip thickness, cutting edge radius, feed per tooth, and axial depth on cutting forces, as well as the inherent size effects in polymers. Utilizing X-ray diffraction (XRD) technology, we accurately measured the residual stresses generated during the micro-milling process. The close correlation between FE simulations and experimental results validates the accuracy and effectiveness of our method. This study represents a substantial breakthrough in finite element simulation techniques for high-precision machining of polymer materials, injecting valuable theoretical and practical knowledge into the field.

Tool life assessment of high strength cast iron alloys in dry face milling operations

The advancement in cast iron alloy properties has led to more efficient materials, especially in the automotive industry. However, this progress has also increased the demand for more durable tools with greater resistance to cutting operations. Conducting an in-depth investigation into the impact of these materials on tool lifespan is crucial, as it directly affects productivity. The main objective of this study was to analyze the lifespan of uncoated cemented carbide tools during milling operations using four high-strength cast iron alloys under various dry cutting conditions. A full factorial Design of Experiment (DoE) (23) was employed. The input variables included cutting speed (vc) of 230 and 350 m/min, tool feed (fz) of 0.1 and 0.2 mm/tooth, and materials, namely cast irons (FC 250, FC 300(Mo), FC300(Mo+RG), and FV 450). Additionally, material tests were conducted on billets without holes to simulate mild cutting conditions and with holes for more severe machining conditions. The established machining operation was face milling without cutting fluid, with the axial depth of cut (ap) and radial depth of cut (ae) maintained constant at 1 mm and 60 mm, respectively. The results showed that FV 450 had the shortest tool life, as expected for the hardest and resistant material, with a maximum lifetime difference of 83 % less when compared to FC 250, the softest one that had the longest tool lifespan. The introduction of holes also significantly reduced the overall tool life by about 57 %. The findings highlight the importance of factors such as cutting speed, feed per tooth, material composition, impact, and workpiece surface quality, which significantly influence tool performance.

A Hybrid Nondominant-Based Genetic Algorithm (NSGA-II) for Multiobjective Optimization to Minimize Vibration Amplitude in the End Milling Process

Aluminium is a noncorrosive, lightweight material used to fabricate parts for the aerospace, automobile, and construction industries. Due to the low-temperature resistance, more heat is generated. At the same time, in machining, tremendous efforts are taken to keep friction and chatter to a minimum and to attain better quality and perfect output, and also more attention is required while selecting the machining process parameters. Spindle speed, rate of feed, radial and axial depth of cut, and radial rake angle of the tool are the parameters utilized to machine aluminium 6063 using the HSS tool on CNC milling to estimate spindle and worktable vibration using a prediction model. In this study, the design of the experiment of the response surface methodology approach is used to create a second-order statistical equation for experimentation with the Design-Expert v12 software. The performance characteristics are analyzed using the ANOVA method. The spindle speed achieved the lowest vibration between 2000 and 3000 rpm. Next, axial and radial depths were the most vibration-affecting parameter compared to the rate of feed and radial rake angle of the tool. To find the best feasible response, the nondominant sorting genetic algorithm II (NSGA II) approach was trained and tested using MATLAB software. Using a Pareto-optimal technique, the optimum worktable vibration ranged from 0.00284 to 0.00165 mm/s2, whereas the spindle vibration ranged from 0.02404 to 0.01336 mm/s2. The predicted values were found to be in an excellent argument when Pareto-optimal solutions are used.

A Forked Microvascular Phantom for Ultrasound Localization Microscopy Investigations.

In the development of ultrasound localization microscopy (ULM) methods, appropriate test beds are needed to facilitate algorithmic performance calibration. Here, we present the design of a new ULM-compatible microvascular phantom with a forked, V-shaped wall-less flow channel pair ( 250 μ m channel width) that is bifurcated at a separation rate of 50 μ m/mm. The lumen core was fabricated using additive manufacturing, and it was molded within a polyvinyl alcohol (PVA) tissue-mimicking slab using the lost-core casting method. Measured using optical microscopy, the lumen core's flow channel width was found to be 252 ± 15 μ m with a regression-derived flow channel separation gradient of 50.89 μ m/mm. The new phantom's applicability in ULM performance analysis was demonstrated by feeding microbubble (MB) contrast flow (1.67 to 167 μ L/s flow rates) through the phantom's inlet and generating ULM images with a previously reported method. Results showed that, with longer acquisition times (10 s or longer), ULM image quality was expectedly improved, and the variance of ULM-derived flow channel measurements was reduced. Also, at axial depths near the lumen's bifurcation point, the current ULM algorithm showed difficulty in properly discerning between the two flow channels because of the narrow channel-to-channel separation distance. Overall, the new phantom serves well as a calibration tool to test the performance of ULM methods in resolving small vasculature.

A high-precision image registration method for multi-channel single molecule localization

Single-molecule localization technology has been widely used in single-particle tracking and super-resolution imaging of biological samples, as it can bypass the diffraction limit of optical systems. Multi-channel single-molecule localization uses multiple imaging channels to simultaneously track different targets or perform multi-color super-resolution imaging, and can also improve the axial depth of single-particle tracking or achieve higher localization precision and density for super-resolution imaging. However, the difference between images in each channel can affect collaborative localization or quantitative analysis, so image registration is a key step in its image data preprocessing. Moreover, due to the high precision of single-molecule localization, its requirements for multi-channel image registration accuracy are also high. Existing technologies generally use control point-based registration methods and often use complicated and precise methods to obtain fiducial images for locating control point pairs to achieve high-precision image registration, which involves high sample or experimental equipment requirements and is difficult to directly extend to other systems. Therefore, developed in this work, is a high-precision image registration method that can directly use randomly distributed fluorescent beads as fiducial samples based on local nonlinear transformation and elimination of mismatched points. By monitoring and iteratively filtering control points in the process of feature matching and transformation model parameter estimation to eliminate control point pairs that are not accurately matched due to inaccurate or poor precision of single-molecule localization, the adverse effects on accurate acquisition and precise matching of control points when using randomly distributed fluorescent beads as fiducial samples are eliminated. At the same time, a second-order polynomial fitting based on local weighted mean is used for estimating the transformation model parameter to better adapt to the existence of local nonlinear deformation between different channels. The results show that using this method only requires three iterations to find and eliminate control point pairs that are not accurately located and matched, thereby achieving more accurate transformation model parameter and improving the registration accuracy by an order of magnitude, achieving a registration accuracy of about 6 nm in a complex dual-channel single-molecule localization imaging system based on orthogonal astigmatism.

Whole-process analytical model on progressive collapse response of reinforced concrete structures under middle column loss

Since progressive collapse is essentially a dynamic process, the whole-process resistance evolution path is critical for a structure against progressive collapse. Previous analytical studies focus primarily on one particular phase of a single load transfer mechanism, such as compressive arch action (CAA) or catenary action (CA) capacity. This study develops a unified analytical model to evaluate the whole resistance-displacement curve of reinforced concrete (RC) structures. In the analytical model, compatibility, equilibrium condition, constitutive laws of concrete and reinforcing bars, and fracture of reinforcing bars are considered. To avoid complex iterations, curvature equations and combination schemes of cross-sectional forces are also established. The analytical model is verified against experimental resistances and axial forces of RC beam-column substructures measured in 24 tests, and it is subsequently utilized to illustrate the evolution of cross-sectional forces throughout the loading process. Furthermore, a sensitivity analysis based on the K-L entropy index is conducted to quantify the effects of different parameters on the progressive collapse resistance. The results show that the yield strength and diameter of reinforcing bars are vital to the whole resistance curves. The axial stiffness and beam depth primarily affect CAA capacity, while having a less significant impact on the CA capacity. Fracture displacement of reinforcing bars is the most influential parameter for CA capacity, suggesting the importance of considering the fracture of reinforcing bars in the analytical model.

Study of optimization for material processing parameters by means of probabilistic methodology for multi-objective optimization

Optimization for material processing parameters is a typical problem of multi-objective optimization, therefore selection and use of proper multi-objective optimization approach is indispensible. The inherent characteristic of newly proposed probabilistic methodology for multi-objective optimization is that it is with the feature of optimization of multiple objectives at the same time in viewpoint of system theory and in spirit of probability theory. In the present paper, the probabilistic methodology is employed to perform the designs of materials processing for improving quality and cost saving at the same time. The laser welding process of ANSI 304 austenitic stainless steel by using a pulsed Nd: YAG laser welding system and thin-wall machining of milling aluminum alloy 2024-T351 are taken as two examples. The quantitative optimum design of materials processing is performed equitably by conducting the assessment of preferable probability of each alternative. The studies indicate that: (1). the optimized parametric combination for the laser welding process of 2 mm thickness ANSI 304 austenitic stainless steel by using a pulsed Nd: YAG laser welding system is at laser parameters of 2.7 kW peak power, welding speed of 2 cm/min and pulse duration of 4 ms; (2). the optimized combination parameter for the thin-wall machining of milling aluminum alloy 2024-T351 is at tool diameter of 8 mm, feed per tooth of 0.06 mm/z, axial cut depth of 24 mm and radial cut depth of 0.625 mm. The optimal configurations guarantee the comprehensive quality of product and reducing energy consumption.

Surface Quality of AZ91D Magnesium Alloy After Precision Milling with Coated Tools

This study investigates the surface quality of AZ91D magnesium alloy specimens after precision milling. The milling was conducted with the use of TiB2- and TiAlN-coated carbide end mills. The following variables were used in the milling process: cutting speed vc, feed per tooth fz and axial depth of cut ap. The surface quality was analysed based on surface roughness parameters and Abbott-Firestone curves. The results showed that the surface quality after machining depended on the tool coating type and applied machining parameters; the only exception was the axial depth of cut because changes in its value did not have any significant effect on the obtained surface quality. After precision milling, AZ91D specimens were characterized by very low surface roughness parameters. The impact of machining conditions was also evaluated with ANOVA analysis, which confirmed the significant effect of cutting speed and feed per tooth. It also indicated the dependence of the roughness parameters on the tool coating type. The tool coating type and machining conditions did not have any significant effect on the Abbott-Firestone curves. Despite the changes applied, the shape of the curves remained similar. The obtained results provide both theoretical and practical knowledge about the achievable surface roughness of AZ91D magnesium alloy specimens after precision milling.

Investigation of the performance of integrated intelligent models to predict the roughness of Ti6Al4V end-milled surface with uncoated cutting tool

Abstract Titanium alloys are broadly used in the medical and aerospace sectors. However, they are categorized within the hard-to-machine alloys ascribed to their higher chemical reactivity and lower thermal conductivity. This aim of this research was to study the impact of the dry-end-milling process with an uncoated tool on the produced surface roughness of Ti6Al4V alloy. This research aims to study the impact of the dry-end milling process with an uncoated tool on the produced surface roughness of Ti6Al4V alloy. Also, it seeks to develop a new hybrid neural model based on the training back propagation neural network (BPNN) with swarm optimization-gravitation search hybrid algorithms (PSO-GSA). Full-factorial design of the experiment with L27 orthogonal array was applied, and three end-milling parameters (cutting speed, feed rate, and axial depth of cut) with three levels were selected (50, 77.5, and 105 m/min; 0.1, 0.15, and 0.2 mm/tooth; and 1, 1.5, and 2 mm) and investigated to show their influence on the obtained surface roughness. The results revealed that the surface roughness is significantly affected by the feed rate followed by the axial depth. A 0.49 µm was produced as a minimum surface roughness at the optimized parameters of 105 m/min, 0.1 mm/tooth, and 1 mm. On the other hand, a neural network having a single hidden layer with 1–20 hidden neurons, 3 input neurons, and 1 output neuron was trained with both PSO and PSO–GSA algorithms. The hybrid BPNN–PSO–GSA model showed its superiority over the BPNN–PSO model in terms of the minimum mean square error (MSE) that was calculated during the testing stage. The best BPNN–PSO–GSA hybrid model was the 3–18–1 structure, which reached the best testing MSE of 3.8 × 10−11 against 2.42 × 10−5 of the 3–8–1 BPNN–PSO hybrid model.

Axial compression behavior of square CFST short column with triangular-corner gap

In order to investigate the impact of the triangular-corner gap between the outer steel tube and the core concrete on the axial compression behavior of square concrete-filled steel tubular (SCFST) columns, this study conducted axial compression tests on 117 SCFST columns and three reference hollow steel tubes. The main test parameters included steel tube thickness, concrete strength, side length of the triangular gap, and axial gap depth. The test results revealed four typical damage modes in the SCFST columns with triangular-corner gaps. The presence of the gap defect had a significant effect on the ultimate bearing capacity of the SCFST columns, causing a decrease in the range of 1.66 %–17.72 % when the gap ratio ranged from 0.45 % to 12.50 %. Moreover, the influence of the gap ratio on the ductility and initial stiffness of the SCFST with triangular-corner gaps was dependent on the concrete strength and the steel tube thickness. Finally, a simplified formula for calculating the ultimate bearing capacity of SCFST columns with triangular-corner gaps was proposed based on the test data and force analysis. The maximum error between the theoretically calculated value and the test value is within 15 %.