Contour Control Research Articles

A Direct Contour Control Method for Free-Form Surface Machining Trajectories Based on Coordinate Transformation

The requirement for high-speed and high-precision contouring with free-form surfaces in the CNC machining process is increasing. The contour error is an essential criterion for the quality of CNC machining. For the problem of contour error control, the present research is mainly based on position tracking, and the control method directly aiming at contour control has not been well studied. This paper proposed a new type of coordinate transformation-based direct contour control method (CT-DCC) for free-form surface machining trajectories. By real-time monitoring of the contour error and the foot point of the free-form parametric trajectory, the contouring task is decomposed into the contour error control task in the normal direction and the velocity control problem in the feed direction. Using coordinate transformation, the output commands of the two tasks are converted to the axial motion command. CT-DCC completely eliminates position tracking in the traditional control to achieve direct control of the contour error, which provides a new solution for the contour control problem of free-form surface machining. Both the axial velocity control and the axial torque control-based CT-DCC scheme were studied, and the two-axis and three-axis direct contour controllers were tested. The simulation and experiment results show the feasibility of the proposed method.

Rock fracturing mechanism and arrangement of fracturing holes using hydro-mechanical splitters

For more-efficient tunneling in hard rock, reported here are simulations and experiments to analyze the rock fracturing mechanism and determine the optimal arrangement of fracturing holes when using hydro-mechanical splitters in tunnel excavation. First, a numerical model of rock fracturing is established using the cohesive-zone model, enabling analysis of the relationship between fracturing force and crack surface development during rock fracturing; this analysis involves examining the mechanisms for crack propagation and stress evolution, thereby comprehensively assessing the mechanism for rock fracturing using a hydro-mechanical splitter. Next, the stress evolution and crack propagation with multiple fracturing holes arranged in either a line or a circle are analyzed; how the fracturing force varies with different numbers of fracturing holes is obtained, and a method of using free-face unloading for circular expansion with a central free face is proposed. Finally, the tunnel contour control of rock fracturing using hydro-mechanical splitters is studied experimentally and ideal peripheral and bottom contours are obtained, thereby providing a way to use rock fracturing technology based on hydro-mechanical splitters for tunneling in hard rock.

Accurate Contour Error Estimation-based robust contour control for dual-linear-motor-driven gantry stages

This article presents a novel adaptive contour control scheme for dual-linear-motors-driven gantry stages (DLMDGSs) with time-varying model parameters and disturbances, aiming to realize high-precision contour control. Specifically, a novel adaptive contour controller based on a coupled model is proposed, incorporating powerful projection adaption laws, to achieve the synchronization of multi-axis motors, regression of adaptive parameters, and high-precision contour performance. Additionally, a contour error estimation (CEE) method with four-order convergence rate is designed to ensure estimation accuracy, enhance estimation robustness, and reduce estimation time. Moreover, considering the periodicity of common control contour tasks, an iterative learning control (ILC) reference trajectory compensation structure is adopted to further improve the contour control effect. Finally, the stability and convergence of the closed-loop system are rigorously proven, and comparative experiments are conducted on a DLMDGS platform to demonstrate the superiority of the proposed control strategy.

A modified U-Net convolutional neural network for segmenting periprostatic adipose tissue based on contour feature learning

ObjectiveThis study trains a U-shaped fully convolutional neural network (U-Net) model based on peripheral contour measures to achieve rapid, accurate, automated identification and segmentation of periprostatic adipose tissue (PPAT). MethodsCurrently, no studies are using deep learning methods to discriminate and segment periprostatic adipose tissue. This paper proposes a novel and modified, U-shaped convolutional neural network contour control points on a small number of datasets of MRI T2W images of PPAT combined with its gradient images as a feature learning method to reduce feature ambiguity caused by the differences in PPAT contours of different patients. This paper adopts a supervised learning method on the labeled dataset, combining the probability and spatial distribution of control points, and proposes a weighted loss function to optimize the neural network's convergence speed and detection performance. Based on high-precision detection of control points, this paper uses a convex curve fitting to obtain the final PPAT contour. The imaging segmentation results were compared with those of a fully convolutional network (FCN), U-Net, and semantic segmentation convolutional network (SegNet) on three evaluation metrics: Dice similarity coefficient (DSC), Hausdorff distance (HD), and intersection over union ratio (IoU). ResultsCropped images with a 270 × 270-pixel matrix had DSC, HD, and IoU values of 70.1%, 27 mm, and 56.1%, respectively; downscaled images with a 256 × 256-pixel matrix had 68.7%, 26.7 mm, and 54.1%. A U-Net network based on peripheral contour characteristics predicted the complete periprostatic adipose tissue contours on T2W images at different levels. FCN, U-Net, and SegNet could not completely predict them. ConclusionThis U-Net convolutional neural network based on peripheral contour features can identify and segment periprostatic adipose tissue quite well. Cropped images with a 270 × 270-pixel matrix are more appropriate for use with the U-Net convolutional neural network based on contour features; reducing the resolution of the original image will lower the accuracy of the U-Net convolutional neural network. FCN and SegNet are not appropriate for identifying PPAT on T2 sequence MR images. Our method can automatically segment PPAT rapidly and accurately, laying a foundation for PPAT image analysis.

Real-time Model Predictive Contouring Control via Block Successive Convex Approximation

The real-time implementation of model predictive control (MPC) in robotics field is known to be challenging due to the inherent non-convex and nonlinear dynamics involved, as well as the need for a longer prediction horizon for better control performance. This paper presents a real-time model predictive contouring control (MPCC) scheme by utilizing the block successive convex approximation (BSCA) method. By adopting this method, the original computationally intensive non-convex problem can be decomposed into smaller subproblems with explicit solutions. We show the effectiveness and the real-time performance of the proposed method through a wheeled mobile robot path following task using MPCC controller in simulation.

Research on the effect of the variation of laser power on the contour control and performance control of laser cladding Ni60CuMo/IN718 coating on RuT300

To improve the wear resistance of Vermicular cast iron (RuT300) surface and overcome the crack inhibition bottleneck of high-hardness coating, a Ni60CuMo/IN718 composite coating was fabricated on RuT300 surface. Results indicate the cracking-free coating with a maximum hardness of 475 HV0.5 at 800 W. In addition, the hard phases MC (M=Nb, Ti), Cr7C3, CrB and the self-lubricating phase Cu3.8Ni were in-situ synthesized in the coating. Increasing laser power raised C content from substrate in the molten pool, providing more C elements for in-situ synthesized Cr7C3. Along with the consumption of Cr by carbide generation, the Cr content used for in-situ synthesized CrB decreases. Moreover, the hardness of Cr7C3 is lower than that of CrB, and the low-hardness Fe in the substrate intensifies the coatings dilution with the increasing power. Therefore, these effects result in a non-positive correlation between the increase of laser power and the coatings hardness. Due to the better anti-wear effect of the high hardness coating, the hardest coatings have the lowest wear rates at room temperature (RT). Abrasive wear is the primary wear mechanism for these coatings at RT. Fe2O3 generated by high temperature friction plays a lubricating effect, but the coating with the low hardness softened by high temperature is not conducive to the compaction and forming of the oxide film. As a result, coatings that produce a moderate oxide film at 300 °C and 500 °C have a lower wear rate in the same environment. The main wear mechanism for the coatings is oxidative wear.

Current status prepectoral and subpectoral breast reconstruction in the USA.

Breast cancer remains the most commonly diagnosed cancer in women in the United States. In the setting of a mastectomy, implant-based reconstruction (IBR) remains the most common type of breast reconstruction performed. IBR is typically performed in two-stages (tissue expander - implant) or one stage [direct-to-implant (DTI)]. As a consequence of the limitations associated with submuscular placement of implants, prepectoral implant placement has been gaining more acceptance worldwide. The prepectoral plane eliminates the need for chest wall muscle dissection, disinsertion and manipulation avoiding the muscle related complications of the subpectoral approach such as increased pain, spasm and animation deformity. In addition, prepectoral placement shortens the recovery time and provides greater control of breast shape and contour. With the latest generation form-stable silicone implants coupled with the wide use and acceptance of acellular dermal matrices (ADMs) and other meshes to support the implant, this technique is becoming the mainstay of implant-based reconstruction, under these circumstances the subpectoral technique will continue to be the benchmark to which the prepectoral technique will be compared to. The authors sought to review the literature emanating from North America that pertains to this topic and provide an up-to-date assessment of the current practices of the prepectoral and submuscular technique.

Robust Control of Dual-Linear-Motor-Driven Gantry Stage for Coordinated Contouring Tasks Based on Feed Velocity

Contouring control is one of the most important tasks for the dual-linear-motor-driven gantry system (DLMDGS), whose control performance determines the in various precision machining equipment, mainly industrial machine tools. Nevertheless, there are some limitations in present results, including the insufficient consideration of system uncertainty and the approximation errors introduced by the computation of the path length. To overcome these issues, the effects of the rotational dynamic and uncertainties are studied and further refined in this article, and a robust control scheme with adaptive neural network identification is designed based on the desired contouring velocity. The method improves existing approaches and avoids the imperfection of the previous one, based on tracking control under global task coordinate frame. The effects of various environmental forces are considered and the B-spline wavelet neural network is employed to approximate unknown dynamic caused by the thrust ripples of the three motors. Some experiments on a real system verify the effectiveness and superiority of the proposed velocity-based contouring control method.

Development of Pitch Cycle-Based Iterative Learning Contour Control for Thread Milling Operations in CNC Machine Tools

The helical contour motion accuracy of feed drive axes is important for thread milling operations in computer numerical control (CNC) machine tools. However, the motion dynamics and external disturbances significantly affect the contour motion results, while the feed drive axes perform helical motions in thread milling operations. Although existing iterative learning contour control (ILCC) methods can improve contour motion accuracy, the problems of data recording and processing on memory usage and computational burden in control systems, wasted materials, and increased costs in thread manufacturing still limit the practical applications of ILCC. Therefore, considering the similar motion dynamics and external disturbances of the feed drive axes during the pitch cycle motions of a helical path, this study developed a pitch cycle-based iterative learning contour control (PCB-ILCC) method to address the control system and thread manufacturing problems caused by the use of ILCC. For PCB-ILCC, this study adopted contour error vector estimation by referring to the interpolated positions on the pitch cycle of the helical path to simplify the computational complexity and designed the ILCC using the cycle learning method to easily implement the ILCC structure. Thus, this study developed a permanent magnet synchronous motor (PMSM) driving control utilizing the robust control method to mitigate the problems of motion dynamics and external disturbances on the feed drive axes. Thread milling experiments performed on a five-axis CNC machining center demonstrated the feasibility of the PCB-ILCC and validated that it can significantly improve the helical contour motion accuracy of the feed drive axes and achieve an 80% contour error reduction rate in comparison with the proportional–proportional–integral control, which is extensively used in commercialized PMSM drivers and CNC controllers.

Contour error dynamic analysis and predictive control for multi-axis motion system

Contour tracking accuracy is the key performance index of a multi-axis motion control system. In the previous design of the contour error feedback controller, the independent design idea of the tracking controller and contour controller of each logic axis is often adopted, which is not easy to realize the optimal control of tracking accuracy and contour accuracy. The coupling increment of each controller is inevitable in motion control compensation, which impacts the control system’s stability. Here, we propose a predictive control method for contour error. At first, through the state space model of the feed system, the system dynamics are judged to predict the contour accuracy in the future finite time domain. Besides, the linear quadratic performance index function of contour error is established, and the performance of the control objective is continuously optimized during the limited time domain with the form of rolling optimization. A closed-loop solution is obtained, which is most suitable for the current motion situation and could add to the system to realize the optimal control considering the trajectory contour and the tracking stability of each servo axis in the subsequent few sampling cycles. Finally, utilizing comparative experiments on the three-axis motion control platform, it is verified that the proposed contour predictive control strategy not only effectively reduces the tracking error but also significantly improves the contour accuracy, which is suitable for application in a complex industrial environment.

Design of a trajectory contour controller for a dual‐axis precision motion stage based on improved iterative learning

AbstractTo solve the problem of contour error (CE) arising from the dual‐axis coupling of the X–Y coordinate planar motor stage under reciprocating motion conditions in integrated circuit (IC) manufacturing, the following two perspectives are considered: indirect intervention from a single‐axis servo tracking error (TE) control and direct intervention from a dual‐axis cross‐coupling control (CCC). An improved motion controller is designed using the iterative learning control (ILC) principle in conjunction with the motion characteristics of the dual‐axis mechanism. Specifically, the variable gain iterative learning controller with a variable forgetting factor (VFF‐VGILC) is designed to address the characteristics of single‐axis trajectory repetition and variable velocity planning in dual‐axis coupling. The significance of the VFF‐VGILC is to increase the servo control bandwidth of a single axis. On the other hand, the cross‐coupled iterative learning controller (CCILC) is designed on the dual‐axis cross‐coupling, while a pre‐compensation strategy is introduced in the CCC structure to form the pre‐CCILC. After experimental designing and testing are performed, the X–Y coordinate planar motor stage can effectively reduce the CE under various trajectory movements compared with the traditional controller. The trajectory curves drawn can meet the motion control requirements better and effectively enhance the motion control capability of the mechanism.

Feedforward compensation of contour errors in robotic machining system using compliance model

Industrial robots are receiving attention as alternatives for large machine tools owing to their high flexibility, large workspace, and cost-effectiveness. However, their low machining accuracy hinders their adoption for machining processes. The major error source is its compliance error caused by the cutting force resulting from the low stiffness of the robot manipulator. To increase the machining accuracy, an external measurement system and a compliance model were used to measure or estimate the compliance errors, and a control algorithm was subsequently applied to compensate for those errors. However, the control algorithm in terms of improving the dimensional accuracy of the workpiece has not been presented thus far. Unlike previous methods that deal with each axis compliance error separately, in this study, a contour control algorithm is proposed to effectively improve dimensional accuracy of the machined workpiece. The compliance model and a force torque sensor were used to predict the actual position of the cutting tool, and the contour errors were calculated using the interpolated reference positions and the predicted actual positions. A feedforward controller was designed to compensate for the estimate contour errors by modifying the reference positions. Experiments were conducted to evaluate the effectiveness of the proposed algorithm.

Parameter Optimization of Gas Metal Arc Welding Process for Welding Dissimilar Steels

Welding is a method of joining similar and dissimilar metals through the use of coalescence. GMAW is a semi-mechanized, fully-mechanized, or automatic process that is widely used in fabrication. GMAW is expected to evolve further to allow for better arc control, bead contour control, deposition control, and higher productivity. The goal of this study is to enhance welding process variables for dissimilar steel metal joints made of duplex stainless steel (DSS) to ASTM/UNS S32205 and erosional steel CORTEN-A to ASTM A242. Such a dissimilar metal joint finds use in the transportation sector, particularly in the construction of rail cars. The square butt joint between two 2 mm-thick sheets was investigated using the gas metal arc welding (GMAW) process with CO₂ as the shielding gas and flux-cored wire of grade 309L as the filler material. The L₉ Taguchi array was used to optimize the tensile strength of the resulting weld joint, which was the desired quality characteristic. GMAW process parameters such as voltage, wire feed rate, and welding speed are optimized at three levels. Using ANOVA, the effects of each factor have been studied. It was found that the ideal set of parameters exists and that the voltage is the most crucial factor. A confirmation test was performed to validate the results, and it was accompanied by a figure and tables.

Time-Inverted Kuramoto Model Meets Lissajous Curves: Multi-Robot Persistent Monitoring and Target Detection

This letter proposes a distributed strategy to achieve both persistent monitoring and target detection in a rectangular and obstacle-free environment. Each robot has to repeatedly follow a smooth trajectory and avoid collisions with other robots. To achieve this goal, we rely on the time-inverted Kuramoto dynamics and the use of Lissajous curves. We analyze the resiliency of the system to perturbations or temporary failures, and we validate our approach through both simulations and experiments on real robotic platforms. In the letter, we adopt Model Predictive Contouring Control as a low level controller to minimize the tracking error while accounting for the robots' dynamical constraints and the control inputs saturation. The results obtained in the experiments are in accordance with the simulations.

A Nonlinear Model-Predictive Contouring Controller for Shared Control Driving Assistance in High-Performance Scenarios

An increasing number of vehicles today are equipped with advanced driver-assistance systems that provide humans involved in the driving tasks with continuous and active support. State-of-the-art implementations of these systems frequently rely on an underlying vehicle controller based on the model-predictive control strategy. In this article, we propose a nonlinear model-predictive contouring controller for a driving assistance system in high-performance scenarios. The design follows specific features to ensure the effectiveness of the interaction, namely, adaptability with respect to the current vehicle state, high-performance driving capabilities, and tunability of the assistance system. First, the control algorithm performance is evaluated offline and compared with a commercial lap-time minimizer, then experimental implementation of the assistance system with the human driver (HD) in the loop has been accomplished on a professional dynamic driving simulator, where an evaluation of the specific features has been performed: 1) a gg-bound is exploited to adapt the controller’s behavior to different driver abilities; 2) the controller’s adaptability to unexpected HD behavior is tested; and 3) the controller’s ability to handle the vehicle at the limit of maneuverability is established. The obtained strategy, then, demonstrates to be suitable as an underlying vehicle controller for a driver-assistance system on a racing track.

Distributed Model Predictive Contouring Control for Real-Time Multi-Robot Motion Planning

Existing motion planning algorithms for multi-robot systems must be improved to address poor coordination and increase low real-time performance. This paper proposes a new distributed real-time motion planning method for a multi-robot system using Model Predictive Contouring Control (MPCC). MPCC allows separating the tracking accuracy and productivity, to improve productivity better than the traditional Model Predictive Control (MPC) which follows a time-dependent reference. In the proposed distributed MPCC, each robot exchanges the predicted paths of the other robots and generates the collision-free motion in a parallel manner. The proposed distributed MPCC method is tested in industrial operation scenarios in the robot simulation platform Gazebo. The simulation results show that the proposed distributed MPCC method realizes real-time multi-robot motion planning and performs better than three commonly-used planning methods (dynamic window approach, MPC, and prioritized planning).

Model Predictive Contouring Control for Time-Optimal Quadrotor Flight

In this article, we tackle the problem of flying time-optimal trajectories through multiple waypoints with quadrotors. State-of-the-art solutions split the problem into a planning task—where a global time-optimal trajectory is generated—and a control task—where this trajectory is accurately tracked. However, at the current state, generating a time-optimal trajectory that considers the full quadrotor model requires solving a difficult time allocation problem via optimization, which is computationally demanding (in the order of minutes or even hours). This is detrimental for replanning in the presence of disturbances. We overcome this issue by solving the time allocation problem and the control problem concurrently via Model Predictive Contouring Control (MPCC). Our MPCC optimally selects the future states of the platform at runtime, while maximizing the progress along the reference path and minimizing the distance to it. We show that, even when tracking simplified trajectories, the proposed MPCC results in a path that approaches the true time-optimal one, and which can be generated in real time. We validate our approach in the real world, where we show that our method outperforms both the current state of the art and a world-class human pilot in terms of lap time achieving speeds of up to 60 km/h.

Industrial Robot Contouring Control Based on Non-Uniform Rational B-Spline Curve

This paper presents a novel algorithm about the industrial robot contouring control based on the NURBS (non-uniform rational B-spline) curve. First, aiming at the error between the industrial robot’s actual trajectory and the desired trajectory, the contour error is proposed as the trajectory evaluation index, and the estimation algorithm of contour error based on the tangent approximation is proposed. Based on the tangent approximation algorithm, the estimation algorithm of contour error in the local task coordinate frame is proposed to realize the transformation from the Cartesian coordinate frame to the local task coordinate frame. Second, according to the configuration of the industrial robot, a modified cross-coupling control scheme based on the local task coordinate frame is designed. Finally, the Bernoulli’s lemniscate curves are constructed by NURBS curve and five-order polynomial curve, respectively, and they are symmetrical. The contrast experiment is designed using the two types of constructed Bernoulli’s lemniscate curves as the incentive trajectory. Through the analysis and comparison between the obtained uniaxial tracking error and the contour error curve of the two incentive trajectories, it is concluded that the incentive trajectory constructed by the NURBS curve has better contour control performance than that constructed by the five-order polynomial curve. The results drawn from this paper lay a certain foundation for the future high-precision contouring control of industrial robots.

Error estimation and cross-coupled control based on a novel tool pose representation method of a five-axis hybrid machine tool

Five-axis hybrid machine tools combine the advantages of series and parallel mechanisms; however, the time-varying, non-linear, and strong coupled characteristics of parallel mechanisms cause contour control difficulties, particularly during high-speed motion. To solve this problem, this study explores the error-transfer laws between the five feed axes, tool centre position (TCP), and tool orientation (TO). It proposes a new tool-pose representation method, called ‘TP’, for five-axis machine tools; and designs a cross-coupled controller taking TP as the control target. Moreover, a kinematic model of the hybrid machine tool is established, and the error transfer law is solved through the first-order differentiation of the kinematic model. Subsequently, two points are taken on the tool axis, the redundancy of coordinates is removed by coordinate transformation, and TP is achieved. Finally, a cross-coupled controller is designed. The input of the controller are the tracking errors obtained according to the five feed-axes commands and encoder feedbacks. In the controller, according to the laws of error transfer, the TP contour error is estimated in real time, and the control variables are assigned to the feed axes. Finally, taking a five-axis hybrid machine tool as an example, the error transfer characteristic analysis as well as error estimation and cross-coupled control (CCC) experiments are performed. The experimental results obtained through the numerical control (NC) program feedback of the computer numerical control (CNC) system verify the correctness of the theory and validity of the method.

MANAGEMENT OF A MAXILLO-ZYGOMATIC FIBROUS DYSPLASIA IN A GROWING-UP CHILD: EIGHT YEARS’ FOLLOW-UP CASE REPORT

Fibrous dysplasia (FD) is a benign dysplastic disorder of bone development in which the normal bone matrix is replaced by fibroblastic proliferation. The aim of this case report is to report a case of a 12-year-old White male patient who was referred to an oral and maxillofacial surgery service with the main complaint of facial asymmetry and tumefaction in the right maxillo-zygomatic region. Physical examination, computed tomography, and incisional biopsy were performed, which confirmed the diagnosis of FD. A stereolithographic model was generated to manufacture a surgical guide. Osteoplasty was conducted under general anesthesia and the patient was follows up every 6 months after surgery. After 8 years of follow-up, a positive control of facial asymmetry, contour, and volume of bone affected were achieved. Final computed tomography showed that the maxilla and zygomatic bones increased in size and volume, with no compression of important structures and no functional damage.