Adaptive parameter optimization approach for robotic grinding of weld seam based on laser vision sensor

Automatic robot grinding technology has been widely applied in the modern manufacturing industry. A flexible abrasive belt wheel used to grind the weld can avoid burns on the base material and improve the processing efficiency. However, when the robot grinds a weld seam, the material removal depth does not coincide with the feed depth because of the soft contact and uneven weld height, affecting the weld seam surface uniformity. Given these problems, an adaptive parameter optimization approach for the robotic grinding of a weld seam was proposed based on a laser vision sensor and a material removal model. First, the depth of weld seam removal was obtained by a laser vision sensor based on triangulation in real-time. Then, a macroscopic material removal model considering flexible deformation was established to determine the relationship between the weld height and process parameters, and the model coefficient was experimentally fitted to ensure the accuracy and reliability of the model. In addition, the data of real-time interaction structure between the robot controller and grinding system were obtained and used to unsure that the rotational speed of the belt wheel increased in the convex part and decreased in the concave part, in order to obtain a uniform weld seam surface. Comparative experiments were performed to verify the effectiveness and superiority of the method, and experiments on the surface roughness and weld seam surface height difference were conducted to verify the universality of the method. Experimental results show that the residual height of the weld after grinding can be controlled within 0.2mm, and the maximum removal height difference can be controlled within 0.05mm. The surface roughness Ra of the weld after grinding could reach 0.408 µm.     

Remote robotic underwater grinding system and modeling for rectification of hydroelectric structures

A submersible grinding robot has been designed to automate the dam gate metallic structure repair process. In order to measure and control the amount of material removed during the process, an empirical approach for modeling the material removal rate (MRR) of the underwater grinding application is proposed and presented in this paper. The objective is to determine the MRR in terms of the process parameters such as cutting speed and grinding power over a range of variable wheel diameters. Experiments show that water causes drag and a significant loss of power occurs during grinding. An air injector encasing the grinding wheel has been prototyped, and it is shown that power loss can be reduced by up to 80%. A model, based on motor characterization and empirical relations among system and process parameters, is developed for predicting MRR which will be used for the robotic grinding control system. A validation is carried out through experiments, and confirms the good accuracy of the model for predicting the depth of cut for underwater grinding. A comparative study for dry and underwater grinding is also conducted through experiments and shows that the MRR is higher for underwater grinding than in dry conditions at low cutting speeds.     

Digital twin modeling for loaded contact pattern-based grinding of spiral bevel gears

To distinguish with the conventional tooth flank grinding only considering geometric accuracy, an innovative digital twin modeling is proposed for loaded contact pattern based grinding of spiral bevel gears. Where, data-driven grinding simulation, sensitivity analysis strategy, adaptive decision and control are developed. Focusing on loaded contact pattern optimization, numerical loaded tooth contact analysis (NLTCA) considering noncentrosymmetric problem and tooth flank roughness is developed for data-driven relationship establishment. Then, an adaptive data-driven tooth flank grinding decision and control model is established. Where, the universal motion concept (UMC) machine settings is selected as the optimal design variable. It is actually an infinite approximation to the target tooth flank in form of an adaptive control system. Moreover, with point-to-point material removal distribution, the different optimization strategies are proposed for accurate tooth flank grinding. In particular, the overcutting problem on the tooth flank grinding programming is investigated. Finally, Levenberg-Marquardt method is applied to solve the established nonlinear lease square model for the accurate machine tool settings having modification variations. Thus, this accurate data-driven digital twin modeling can achieve loaded contact pattern-based grinding. The provided numerical and test instances can verify the proposed digital twin modeling.     

An adaptive trajectory planning algorithm for robotic belt grinding of blade leading and trailing edges based on material removal profile model

Robotic belt grinding of the leading and trailing edges of complex blades is considered to be a challenging task, since the microscopic material removal mechanism is complicated due to the flexible contact state accompanied with greatly varying curvature that finally affects the machined profile accuracy. The resulting poor accuracy of blade edges, to a great extent, is attributed to the trajectory planning method which less considers the dynamics. In this paper, an iso-scallop height algorithm based on the material removal profile (MRP) model is developed to plan the tool paths by taking into consideration the elastic deformation at contact wheel-workpiece interface. An improved constant chord-height error method considering the influence of elastic deformation is then proposed to adaptively plan the grinding points according to the curvature change characteristics of the free-form surface. Based on these two steps, a MRP model based adaptive trajectory planning algorithm is constructed to enhance the profile accuracy facing the robotic belt grinding operation. Simulation and experimental results demonstrate the effectiveness of the proposed trajectory planning algorithm for the robotic belt grinding of blades from the perspectives of surface roughness, profile accuracy and processing efficiency. Particularly this technology serves to solve the problem of over-cutting at the blade leading and trailing edges.     

Position-based force tracking adaptive impedance control strategy for robot grinding complex surfaces system

As a key technology of robot grinding, force control has great influence on grinding effects. Based on the traditional impedance control, a position-based force tracking adaptive impedance control strategy is proposed to improve the grinding quality of aeroengine complex curved parts, which considers the stiffness damping environmental interaction model, modifies the reference trajectory by a Lyapunov-based approach to realize the adaptive grinding process. In addition, forgotten Kalman filter based on six-dimensional force sensor is used to denoise the force information and a three-step gravity compensation process including static base value calculation, dynamic zero update and contact force real-time calculation is proposed to obtain the accurate contact force between tool and workpiece in this method. Then, to verify the effectiveness of the proposed method, a simulation experiment which including five different working conditions is conducted in MATLAB, and the experiment studying the deviation between the reference trajectory and the actual position is carried out on the robot grinding system. The results indicate that the position-based force tracking adaptive impedance control strategy can quickly respond to the changes of environmental position, reduce the fluctuation range of contact force in time by modifying the reference trajectory, compensate for the defect of the steady-state error of the traditional impedance control strategy and improve the surface consistency of machined parts.     

Creep feed grinding optimization by an integrated GA-NN system

The present work is aimed to optimize creep feed grinding (CFG) process by an approach using integrated Genetic Algorithm-Neural Network (GA-NN) system. The aim of this creep feed grinding optimization is obtain the maximal metal removal rate (MRR) and the minimum of the surface roughness (R _a ). For optimization, metal removal rate is calculated with a mathematic formula and a Back Propagation (BP) artificial neural-network have been used to prediction of R _a . The parameters used in the optimization process were reduced to three grinding conditions which consist of wheel speed, workpiece speed and depth of cut. All of other parameters such as workpiece length, workpiece material, wheel diameter, wheel material and width of grinding were taken as constant. The BP neural network was trained using the scaled conjugate gradient algorithm (SCGA). The results of the neural network were compared with experimental values. It shows that the BP model can predict the surface roughness satisfactorily. For optimization of creep feed grinding process, an M-file program has been written in ‘Matlab’ software to integrate GA and NN. After generation of each population by GA, firstly, the BP network predicts R _a and then MRR has been calculated with mathematic formula. In this study, the importance of R _a and MRR is equal in the optimization process. By using this integrated GA-NN system optimal parameters of creep feed grinding process have been achieved. The obtained results show that, the integrated GA-NN system was successful in determining the optimal process parameters.     

Modelling and optimization of grinding processes

The paper describes different methods for modelling and optimization of grinding processes. First the process and product quality characterizing quantities have to be measured. Afterwards different model types, e.g. physical–empirical basic grinding models as well as empirical process models based on neural networks, fuzzy set theory and standard multiple regression methods, are discussed for an off-line process conceptualization and optimization using a genetic algorithm. The assessment of grinding process results, which build the individuals in the genetic algorithm's population, is carried out using a target tree method. The methods presented are integrated into an existing grinding information system, which is part of a three control loop system for quality assurance.     

A new force-depth model for robotic abrasive belt grinding and confirmation by grinding of the Inconel 718 alloy

Robotic abrasive belt grinding has been successfully applied to the grinding and polishing of aerospace parts. However, due to the flexible characteristics of robotic abrasive belt grinding and the time-varying characteristics of the polishing contact force, as well as the plastic and difficult-to-machine material properties of Inconel 718 alloy, it is very difficult to control the actual removal depth and force of the polished surface, which brings great challenges to robot automatic polishing. Therefore, the relationship between the grinding force and the grinding depth in the robotic abrasive belt grinding is analyzed in detail, the robot machining pose error model considering the deformation of the grinding head is established, and the Inconel 718 alloy machining experiment of the robotic abrasive belt grinding is designed. The mapping relationship between the grinding force and the grinding depth is obtained, and the grinding force ratio in the downgrinding and upgrinding mode is discussed. The experimental and theoretical comparisons results show that with the increase of the grinding depress depth, both the grinding depth and the grinding force show an irregular increasing trend, and the increasing trend of the grinding force (increases by about 344.44%–445.45%) is obviously greater than that of the grinding depth (increases by about 52.94%). When the grinding depress depth is large (greater than 3 mm), the feed direction force and the normal force appear obvious secondary pressure peaks at the beginning and end of grinding, which has not been seen in previous studies. In addition, regardless of whether it is downgrinding or upgrinding, the grinding force ratio decreases with the increase of the depress depth, and the grinding force ratio of downgrinding (average 0.668) is smaller than that of upgrinding (average 0.724). This study provides a reference for robotic abrasive belt grinding, and the surface quality of Inconel 718 alloy of robotic abrasive belt grinding can be further improved through the optimization of force and depth.     

机器人修磨中融合先验知识的适应学习建模方法

针对机器人修磨磨削量建模中处理突变因素的难题,本文首先从机器学习建模方法的角度指出该问题与统计学习的不同点,并把问题形式化,然后在此基础上提出了融合先验知识的适应学习建模方法.该方法基于半经验公式生成虚拟样本,不但弥补了适应学习建模中新样本不足的问题,而且把半经验公式中的信息更充分地融合到学习机模型中.实验结果证明,该...     

基于自适应动态规划的矿渣微粉生产过程跟踪控制

矿渣微粉是一种新型绿色环保型建材,可以大大提高水泥混凝土的力学性能.本文以矿渣微粉生产过程为研究对象,针对该过程难以通过机理建模进行辨识和控制的特点,利用数据驱动的思想,建立矿渣微粉生产过程的递归神经网络模型.在此基础上,利用自适应动态规划,设计具有控制约束的跟踪控制器,并将其应用到矿渣微粉生产过程中.仿真分析表明,建立的数据驱动模型能够有效地辨识矿渣微粉生产过程,同时,本文提出的控制方法能够实现输入受限的微粉比表面积及磨内压差的最优跟踪控制.     

Feature extraction and selection based on vibration spectrum with application to estimating the load parameters of ball mill in grinding process

Feature extraction and selection are important issues in soft sensing and complex nonlinear system modeling. In this paper, a new feature extraction and selection approach based on the vibration frequency spectrum is proposed to estimate the load parameters of wet ball mill in grinding process. This approach can simplify the modeling process. In this study, the vibration acceleration signals are first transformed into the frequency spectrum by fast Fourier transform (FFT). Then the candidate features are extracted and selected from the frequency spectrum, which include characteristic frequency sub-bands, spectral principal components, and features of local peaks. Mutual information, spectral segment clustering and kernel principal component analysis are used to obtain these candidate features. Finally, a combinatorial optimization method based on adaptive genetic algorithm selects the input sub-set and parameters of the soft sensor model simultaneously. This approach is successfully applied in a laboratory scale wet ball mill. The test results show that the proposed approach is effective for modeling the parameters of mill load.     

基于SVM 的机器人高精度磨削建模

为了改进机器人磨削过程中对磨削量的控制,提出了一种基于SVM 回归的磨削过程建模方法,通过 分析与磨削量相关的一组可测变量——机器人进给速率、接触力、工件表面曲率,利用机器学习的方法建立回归模 型,对磨削量进行预测．这种方法可以避免逐一分析复杂的动力学参数．实验结果表明,该方法可以取得良好的效 果,模型的预测精度达到90%以上,基本满足实际加工的要求．     

磨矿粒度动态过程的一种快速Monte Carlo仿真方法

磨矿是降低矿物粒度的工业过程,产品粒度是磨矿过程的关键质量指标. 由于磨矿粒度难以在线检测且磨矿生产过程具有综合复杂特性,难以采用传统控制方法实现磨矿粒度的控制. 因此,建立磨矿粒度和关键工艺参数的动态模型对于磨矿运行控制和优化具有重要意义. 采用总量平衡原理获得磨矿粒度的微分方程模型多数情况下无法获得解析解. 而基于Monte Carlo （MC）方法的磨矿粒度模型能够精确模拟磨矿粒度分布的动态变化,但是其仿真效率低难以实用. 本文针对这一问题提出一种新的MC仿真方法： 在定总量方法的基础上引入新的颗粒移除机制,在移除过程中动态地分配各个粒级颗粒数目并保持破裂前后各个粒级颗粒所占总颗粒数的百分比不变,避免颗粒移除过程中由于粒级差异导致的抽样误差,且避免MC仿真速度随着仿真推进下降的问题. 仿真实验验证表明,本方法能够在保证一定精度前提下显著提高磨矿粒度MC仿真的计算速度. 最后,通过一个实例介绍了本文仿真模型在磨矿优化控制中的应用.     

Automated robotic grinding by low-powered manipulator

A new robotic grinding process has been developed for a low-powered robot system using a spring balancer as a suspension system. To manipulate a robot-arm in the vertical plane, a large actuator torque is required due to the tool weight and enormous gravity effect. But the actuators of the robot system always exhibit a limited torque capacity. This paper presents a cheap and available system for precise grinding tasks by a low-powered robot system using a suspension system. For grinding operations, to achieve position and force-tracking simultaneously, this paper presents an algorithm of the hybrid position/force-tracking scheme with respect to the dynamic behavior of a spring balancer. Material Removal Rate (MRR) is developed for materials SS400 and SUS304. Simulations and experiments have been carried out to demonstrate the feasibility of the proposed system.     

Control of a grinding mill circuit using fractional order controllers

This paper presents the design and application of fractional single-input–single-output (SISO) controllers to a grinding mill circuit, which is a multiple-input–multiple-output (MIMO) process. Two kinds of controllers are presented: fractional order proportional-integral (FOPI) controllers, and a combination of FOPI and fractional order model reference adaptive controllers (FOMRAC). The parameters of the controller are tuned using off-line particle swarm optimization. In the presence of disturbances and process noise, the SISO fractional controllers achieve similar or better performance compared to linear model predictive control (LMPC).     

发动机叶片机器人精密砂带磨削精度检测技术

当前精密砂带磨削精度检测技术检测准确率低,检测效率差。为了解决上述问题,引用发动机机器人研究了一种新的精密砂带磨削精度检测技术,对其精度数据进行采集,将采集出的系统数据作为基础信息来源,获取叶片零部件的点云信息,处理机器人的工作主坐标系,通过三维激光扫描获取叶片机器人的准确信息,同时配以打磨剖光操作,以PCA算法解析,进一步将数据集简化,根据数据主要分布规律选择合适的算法加工位置与范围,在三维空间中,将点分别对应坐标轴中的点进行匹配,通过对磨削接触面的轮廓以及磨削表面完整性进行分析,以实现对发动机叶片机器人精密砂带磨削精度的检测。实验结果表明,相较于传统检测技术,发动机叶片机器人精密砂带磨削精度检测技术检测精度提高了31.28%,检测误差降低了15.21%。     

基于案例推理增强学习的磨矿过程设定值优化

磨矿粒度和循环负荷是磨矿过程产品质量与生产效率的关键运行指标,相对于底层控制偏差,回路设定值对其影响要严重的多.然而,磨矿过程受矿石成分与性质、设备状态等变化因素影响,运行工况动态时变,难以建立模型,因此难以通过传统的模型方法优化回路设定值.本文将增强学习与案例推理相结合,提出一种数据驱动的磨矿过程设定值优化方法.首先根据当前运行工况,采用基于Prey-Predator优化的案例推理方法,决策出可行的基于Elman神经网络的Q函数网络模型;然后利用实际运行数据,在增强学习的框架下,根据Q函数网络模型优化回路设定值.在基于METSIM的磨矿流程模拟系统上进行实验研究,结果表明所提方法可根据工况变化在线优化回路设定值,实现磨矿运行指标的优化控制.     

Adaptive splitbelt treadmill walking of a biped robot using nonlinear oscillators with phase resetting

To investigate the adaptability of a biped robot controlled by nonlinear oscillators with phase resetting based on central pattern generators, we examined the walking behavior of a biped robot on a splitbelt treadmill that has two parallel belts controlled independently. In an experiment, we demonstrated the dynamic interactions among the robot mechanical system, the oscillator control system, and the environment. The robot produced stable walking on the splitbelt treadmill at various belt speeds without changing the control strategy and parameters, despite a large discrepancy between the belt speeds. This is due to modulation of the locomotor rhythm and its phase through the phase resetting mechanism, which induces the relative phase between leg movements to shift from antiphase, and causes the duty factors to be autonomously modulated depending on the speed discrepancy between the belts. Such shifts of the relative phase and modulations of the duty factors are observed during human splitbelt treadmill walking. Clarifying the mechanisms producing such adaptive splitbelt treadmill walking will lead to a better understanding of the phase resetting mechanism in the generation of adaptive locomotion in biological systems and consequently to a guiding principle for designing control systems for legged robots.     

基于B样条曲线的电力巡检机器人越障控制

为了提高电力巡检机器人越障控制能力,该文提出基于B样条曲线的电力巡检机器人越障控制技术,首先构建电力巡检机器人的被控对象模型,结合电力巡检机器人驱动动力学分布,进行电力巡检机器人的定位控制,同时采用避障算法进行电力巡检机器人巡检过程中的越障控制,结合位姿参数的自适应调节方法进行电力巡检机器人越障运动学模型构造。在此基础上,建立电力巡检机器人越障控制目标函数,采用B样条曲线跟踪寻优方法进行机器人的越障路径规划,采用自适应的模糊信息加权方法,进行电力巡检机器人越障控制优化。仿真结果表明,采用该方法进行电力巡检机器人运动轨迹测定分布结果稳定,接近运动轨迹的标准值。其越障控制的灵敏度较高,自适应控制能力较强,电力巡检机器人运动轨迹测定分布结果稳定,提高了电力巡检机器人越障性能。     

机器人砂带磨削系统作业精度分析与误差补偿

为了更好地反映及提高工业机器人砂带磨削系统的整体性能,通过分析机器人应用系统的特点,详细 描述了工业机器人应用系统“作业精度”的含义及衡量标准．在此基础上,推导了机器人砂带磨削系统作业精度模 型,设计了机器人砂带磨削系统作业误差测量工具及校准系统,建立了实际的机器人砂带磨削系统．通过实际的机 器人磨削实验验证了方法的有效性．