Explore the vast range of titles available.

Five-axis working machines are applied in the high-precision machining of complex convex surfaces. Therefore, this study integrated a horizontal parallel three-axis motion platform and a three-axis machine tools to create a reconfigurable precision five-axis machine tools (RPFMT). A DELTA OPEN computer numerical control controller was used as the control system architecture. A human–machine interface and programmable controller were incorporated into the developed tool to achieve automatic online measurement. A suitable cutting tool was selected to calculate the five-axis NC machining code for a complex convex surface. The NC codes were input into the LabVIEW software for five-axis postprocessing conversion. A concave workpiece was cut through rough and finishing machining to verify the accuracy of the produced RPFMT.

This paper presents a new and highly efficient voxel modeling method to determine cutter-workpiece engagement (CWE) in three-axis milling. The method voxelizes both the workpiece and milling cutter in a voxel-based machining simulation space. This allows workpiece model update during machining to be done without the intensive intersection calculations between the cutter and workpiece. In addition, unlike the existing methods that update workpiece and compute CWE individually, the updated workpiece model and CWE are computed in an integrated fashion in the present work. This further reduces the involved computational load. At each cutter location, a voxel model of the cutter surface is created first by slicing the cutter surface by the voxel layer boundary planes to obtain a sequence of circles along the cutter axis. A main contribution of this work is the development of a unique fast increment and decrement scheme to voxelize each circle. Workpiece update and CWE determination can then be done simultaneously from the created voxel model of the cutter surface. A series of case studies have been performed to demonstrate the effectiveness of the proposed method. It can be seen that the proposed method is able to compute CWE very efficiently while maintaining accuracy comparable to the specified voxel size.

It is still an open and challenging issue to the typical position control problems of the three‐axis electrical‐optical gyro‐stabilized platform systems (TEOGSP), due to inherent characteristics, for example, measurement noise, input saturation, parametric uncertainties, largely unknown load disturbance. To solve this problem, a saturated adaptive robust feedback controller using an adaptive cascaded extended state observer (SAFCESO) is proposed for compromising between the measurement noise effect and the sensitivity to disturbances. Firstly, the matched and mismatched disturbances existing in the TEOGSP system are estimated and rejected by the cascaded adaptive extended state observers (CESO). Secondly, the parametric uncertainties are evaluated by the adaptive control, and the match disturbances are attenuated by the robust control. Moreover, the adaptive robust control law does not require the velocity measurement signal and internal dynamics information of the system, which is practical to implement. Hence, all various uncertainties could be mainly compensated. Then, the improved auxiliary systems governed by smooth switching functions are developed and incorporated into the control design to compensate for the effect of the input saturation. Finally, the command filters are introduced to limit the magnitude of the virtual control and to calculate the derivative of the virtual control, respectively. The extensive comparative experimental results in the TEOGSP systems showed that the proposed SAFCESO method had superiorities in terms of high‐precision tracking accuracy, robustness, and noise reduction.

We create a new tactile recording system with which we develop a three-axis fingernail-color sensor that can measure a three-dimensional force applied to fingertips by observing the change of the fingernail’s color. Since the color change is complicated, the relationships between images and three-dimensional forces were assessed using convolution neural network (CNN) models. The success of this method depends on the input data size because the CNN model learning requires big data. Thus, to efficiently obtain big data, we developed a novel measuring device, which was composed of an electronic scale and a load cell, to obtain fingernail images with 0 $^\\circ$ to 360 $^\\circ$ directional tangential force. We performed a series of evaluation experiments to obtain movies of the color changes caused by the three-axis forces and created a data set for the CNN models by transforming the movies to still images. Although we produced a generalized CNN model that can evaluate the images of any person’s fingernails, its root means square error (RMSE) exceeded both the whole and individual models, and the individual models showed the smallest RMSE. Therefore, we adopted the individual models, which precisely evaluated the tangential-force direction of the test data in an $F_x$ - $F_y$ plane within around $\\pm$ 2.5 $^\\circ$ error at the peak points of the applied force. Although the fingernail-color sensor possessed almost the same level of accuracy as previous sensors for normal-force tests, the present fingernail-color sensor acts as the best tangential sensor because the RMSE obtained from tangential-force tests was around 1/3 that of previous studies.

This study examines the three-axis vibration isolation capabilities of a full-scale magnetorheological (MR) seat suspension system utilizing experimental methods to assess performance under both single-axis and simultaneous three-axis input conditions. To achieve this, a semi-active MR seat damper was designed and manufactured to address excitations in all three axes. The damper effectiveness was tested experimentally for axial and lateral motions, focusing on dynamic stiffness and loss factor using an MTS machine. Prior to creating the full-scale MR seat suspension, a scaled-down version at one-third size was developed to verify the damper’s ability to effectively reduce vibrations in response to practical excitation levels. Additionally, a narrow-band frequency-shaped semi-active control (NFSSC) algorithm was developed to optimize vibration suppression. Ultimately, a full-scale MR seat suspension was assembled and tested with a 50th percentile male dummy, and comprehensive three-axis vibration isolation tests were conducted on a hydraulic multi-axis simulation table (MAST) for both individual inputs over a frequency range up to 200 Hz and for simultaneous multi-directional inputs. The experimental results demonstrated the effectiveness of the full-scale MR seat suspension in reducing seat vibrations.

The control of a three-axis parallel mechanism suffers from problems of nonlinearity, strong coupling, and time-varying uncertainties of each motor, which present technical challenges to the dynamic modeling and motion control of the mechanism. This paper proposes a new model-free decoupled and robust repetitive controller to improve the motion trajectory tracking accuracy. Based on the time-delay estimation (TDE) technique, this method can solve the problem of missing parameters of the complete dynamics of the mechanism and realize efficient decoupled control of the mechanism through indirectly applying the overall dynamics to the control structure for closed-loop motion control. To reduce the TDE error, a robust repetitive controller is implemented in the joint space of the mechanism to improve the tracking accuracy of the mechanism with no need of any external sensors. The closed-loop stability of the proposed controller is demonstrated using the Lyapunov approach. Simulations and experimental studies are conducted on the three-axis parallel mechanism. Through comparison with other methods, the results validate that the proposed controller presents better performance in terms of trajectory tracking accuracy and end-effector pose stabilizing ability.

Computer vision applications in the industry have been a constant field of research in the academic community. Industrial daily challenges such as object detection, measurement, and quality inspection are examples of situations where some automation could be employed using such techniques. In this paper, a system based on stereo vision and image analysis has been developed to automate a habitual activity present in all machining companies: workpiece referencing in three-axis machining centers. The proposed vision system uses two cameras mounted in the spindle of the machining center to acquire images. All images are processed in custom software to return the position of the workpiece coordinate to the machining worker. Experimental results validate the application of the proposed system in a real CNC machining process.

Shortening total length of tool path is preferred in numerical control (NC) machining since it can reduce machining time effectively. Compared with iso-planar tool path, iso-scallop tool path is shorter in length due to the pursuing of maximum interval values. However, the process of iso-scallop tool path generation is more complicated and time-consuming. To simplify the computing process and improve efficiency, this paper presents an efficient iso-scallop tool path generation method for three-axis scattered point cloud machining. Avoiding offsetting points or fitting surface, scallop points and iso-scallop cutter location (CL) points are directly calculated based on scattered data points by iterative algorithms. In order to reduce the times of iterative calculations, initial scallop-height points and CL points are calculated to be closer to the wanted theoretical points. Only a small number of key data points are searched for use so as to reduce calculation amount, and the number gradually decreases with the increase of iterations. Two typical point clouds are used to test the presented method. The experiment results indicate that the scallop height on the machined surface is uniform, and total length of the generated tool paths is much shorter than that of iso-planar tool paths. Moreover, the computation efficiency is also improved and is higher than our previous method (Int J Adv Manuf Technol 63: 137–146, 2012).

Brain tumor segmentation using Magnetic Resonance Imaging (MRI) is vital for clinical decision making. Traditional deep learning-based studies using convolutional neural networks have predominantly processed MRI data as two-dimensional slices, leading to the loss of contextual information. While three-dimensional (3D) convolutional layers represent an advancement, they have not fully exploited pathological information according to the three-axis nature of 3D MRI data—axial, coronal, and sagittal. Recognizing these limitations, we introduce a Tri-Axis based Context-Aware Reverse Network (TACA-RNet). This innovative approach leverages the unique 3D spatial orientations of MRI, learning crucial information on brain anatomy and pathology. We incorporated three specialized modules: a Tri-Axis Channel Reduction module for optimizing feature dimensions, a MultiScale Contextual Fusion module for aggregating multi-scale features and enhancing spatial discernment, and a 3D Axis Reverse Attention module for the precise delineation of tumor boundaries. The TACA-RNet leverages three specialized modules to enhance the understanding of tumor characteristics and spatial relationships within MRI data by fully utilizing its tri-axial structure. Validated on the Brain Tumor Segmentation Challenge 2018 and 2020 datasets, the TACA-RNet demonstrated superior performances over contemporary methodologies. This underscores the critical role of leveraging the three-axis structure of MRI to enhance segmentation accuracy.

In Japan, the aging and decreasing number of agricultural workers is a significant problem. For wine grape harvesting, especially for large farming areas, there is physical strain to farmers. In order to solve this problem, this study focuses on developing an automated harvesting robot for wine grapes. The harvesting robot needs high dust, water, and mud resistance because grapevines are grown in hard conditions. Therefore, a three-axis linear robot was developed using a rack and pinion mechanism in this study, which can be used in outdoor conditions with low cost. Three brushless DC motors were utilized to drive the three-axis linear robot. The motors were controlled using a control area network (CAN) bus to simplify the hardware system. The accuracy of the robot positioning was evaluated at the automated harvesting condition. The experiment results show that the accuracy is approximately 5 mm, 9 mm, and 9 mm in the x-axis (horizontal), y-axis (vertical), and z-axis (depth), respectively. In order to improve the accuracy, we constructed an error model of the robot and conducted a calibration of the robot. The accuracy was improved to around 2 mm of all three axes after calibration. The experimental results show that the accuracy of the robot is high enough for automated harvesting of the wine grapes.