Abstracts:In this paper, the trajectory tracking control of a 6-DoF pneumatically actuated Gough–Stewart parallel robot is investigated. The dynamic model of each link, comprising of a pneumatic actuator and a proportional electrical valve is extracted with the aim of obtaining the corresponding state space representation of the pneumatic system. Unknown parameters of the dynamic model consisting friction force of the cylinder and parameters of the proportional valve are identified by employing genetic algorithm. Position control of the pneumatic actuator is performed based on Back-Stepping Sliding Mode controller according to the dynamic model of the system. As such trajectory tracking control is performed for different trajectories by employing a rotation sensor and calculated position based on joint space and task space simultaneously. Desired sinusoidal trajectories with pure motions are tracked with root mean square error of the pure translations and rotations lower than 0.85 (cm) and 1.9 (deg), respectively. The results reveal that the trajectory is tracked by the Back-Stepping Sliding Mode controller properly. This shows the efficiency of the control strategy and the proposed method for calculating the position of the end-effector.

Abstracts:Robotic machining is an increasing application due to various advantages of robots such as flexibility, maneuverability and competitive cost. For robotic machining, the machining accuracy is the major concern of current researches. And particular attention is paid to the proper modeling of manipulator stiffness properties, the cutting force estimation and the robot posture optimization. However, through our research, the results demonstrate the spindle configuration largely affects the deformation of the robot end-effector (EE). And it may even account for approximately half of the total deformation for machining applications with the force acting perpendicular to the tool. Furthermore, the closer distance between the tool tip and the EE does not mean that the deformation tends to be smaller. Thus, it is reasonable to consider optimizing the spindle configuration based on the optimal robot posture, thereby exhausting advantages of the robot and further reducing machining errors. In this paper, a spindle configuration analysis and optimization method is presented, aiming at confirming the great influence of the spindle configuration on the deformation of the robot EE and minimizing it. First, a deformation model based on the spindle configuration (SC-based deformation model) is presented, which establishes a mapping between the spindle configuration and the deformation of the robot EE. And it confirms the large effect of the spindle configuration on the deformation of the EE. Then, a complementary stiffness evaluation index (CSEI) is proposed. And it adopts matrix norms to evaluate the influence of the spindle configuration on the complementary stiffness matrix in the SC-based deformation model. Using this index, the proposed SC-based deformation model is simplified for the ODG-JLRB20 robot adopted in this paper. Finally, a spindle configuration optimization model is derived to minimize the simplified SC-based deformation model using an iterative procedure. With this model, the optimal spindle configuration with respect to the EE can be obtained for a specific machining trajectory. Experimental results conducted on the ODG-JLRB20 robot demonstrate the correctness and effectiveness of the present method.

Abstracts:Mobile robots have been widely implemented in industrial automation and smart factories. Different types of mobile robots work cooperatively in the workspace to complete some complicated tasks. Therefore, the main requirement for multi-robot systems is collision-free navigation in dynamic environments. In this paper, we propose a sensor network based navigation system for ground mobile robots in dynamic industrial cluttered environments. A range finder sensor network is deployed on factory floor to detect any obstacles in the field of view and perform a global navigation for any robots simultaneously travelling in the factory. The obstacle detection and robot navigation are integrated into the sensor network and the robot is only required for a low-level path tracker. The novelty of this paper is to propose a sensor network based navigation system with a novel artificial potential field (APF) based navigation algorithm. Computer simulations and experiments confirm the performance of the proposed method.

Abstracts:This paper proposes an efficient base position (BP) optimization method for mobile painting robot manipulators (MPRMs). An approximate decoupled model is first established to overcome the coupling problem of painting robots. And the manipulating characteristics are summarized as three constraints: positioning, orientation and singularity avoidance constraints. Then, joint-level performance criteria of one manipulating point and a painting path, which reflect the manipulability and dexterity, were constructed successively. Considering multiple constraints, the BP optimization problem is translated into a standard inequality constrained optimization problem of the path criterion. Two algorithms are designed to solve this problem: one is based on the internal penalty function method used to obtain an initial BP; the other is based on the generalized Lagrange multiplier method used to get the near-optimal BP. This method was applied to a real MPRM system painting three typical surfaces: flat, cylindrical and truncated conical surfaces. Application results demonstrate the effectiveness as well as the availability of the approximate decoupled model. Simultaneously, compared with previous methods, the efficiency is improved by hundreds of times.

Abstracts:3D printing and particularly fused filament fabrication is widely used for prototyping and fabricating low-cost customized parts. However, current fused filament fabrication 3D printers have limited nozzle condition monitoring techniques to minimize nozzle clogging errors. Nozzle clogging is one of the most significant process errors in current fused filament fabrication 3D printers, and it affects the quality of the prototyped parts in terms of geometry tolerance, surface roughness, and mechanical properties. This paper proposes a nozzle condition monitoring technique in fused filament fabrication 3D printing using a vibration sensor, which is briefly described as follows. First, a bar mount that supports the liquefier in fused filament fabrication extruder was modeled as a beam excited by a system of process forces. The boundary conditions were identified, and the applied forces were analyzed for Direct and Bowden types of fused filament fabrication extruders. Second, a new 3D printer with a fixed extruder and a moving platform was designed and built for conducting nozzle condition monitoring experiments. Third, nozzle clogging was simulated by reducing the nozzle extrusion temperature, which caused partial solidification of the filament around inner walls of the nozzle. Fourth, sets of experiments were performed by measuring the vibrations of a bar mount during extrusion of polylactic acid, acrylonitrile butadiene styrene, and SemiFlex filaments via Direct and Bowden types of fused filament fabrication extruders. Findings of the current study show that nozzle clogging in fused filament fabrication 3D printers can be monitored using an accelerometer sensor by measuring extruder’s bar mount vibrations. The proposed technique can be used efficiently for monitoring nozzle clogging in fused filament fabrication 3D printers as it is based on the fundamental process modeling.

Abstracts:This paper presents several novel methods that improve the current input shaping techniques for vibration suppression for multi-degree of freedom industrial robots. Three different techniques, namely, the optimal S-curve trajectory, the robust zero-vibration shaper, and the dynamic zero-vibration shaper, are proposed. These methods can suppress multiple vibration modes of a flexible joint robot under a computed torque control based on a rigid model. The time delays for each method are quantified and compared. The optimal S-curve trajectory finds the maximum jerk to obtain the minimum vibration. The robust zero-vibration shaper can suppress multiple modes without an accurate model. The delay of the dynamic zero-vibration shaper is smaller than the existing input shaping techniques. Our analysis is verified both by simulation and experiment with a six degrees-of-freedom commercial industrial robot.

Abstracts:On-Machine Inspection (OMI) system has been playing an important role in modern aeronautical manufacturing, owing to its high efficiency, great convenience, and low application cost. The inspection path planning of OMI, which aims to find an optimal path that traverses all inspection points without collisions and by costing as little time as possible, is a main bottleneck that limits the achievements of higher efficiency and less inspection time. Besides, the path planning for OMI in aerospace manufacturing faces new challenges, which are barely explored, because aerospace structures have unique characteristics of complex geometrical features, large-scale dimensions and high-precision processing requirements. Thus, this paper proposes a novel, easily-implemented and robust inspection path planning method to plan paths for OMI of aerospace structures based on the properties of aerospace structures. In order to lift the inspection efficiency, this method makes three improvements on the path planning. First, reorganize the inspection features based on the cluster technology. Second, construct the adjacent feature graph based on Voronoi Diagram to plan the path. Third, a search algorithm is designed to search the adjacent feature graph to decide the sequence of inspection features and a convex hull based algorithm is used to avoid collisions. The proposed method has been tested for several cases and solid experimental results have shown that these improvements take effects in path planning for OMI of aerospace structures and suited paths can be provided for the inspection.

Abstracts:A data-driven optimization model to collaborative manufacturing system considering geometric and physical performances is proposed to improve competitiveness of hypoid gear product development facing with economic globalization. Firstly, to deal with the vagueness or impreciseness of the voice of customer (VOC), the fuzzy quality function deployment (fuzzy-QFD) using fuzzy weighted average method in the fuzzy expected value operator is introduced into hypoid gear manufacturing. It can convert them into the critical to qualities (CTQs), and the technical geometric and physical performance requirements. And then, the multi-objective optimization (MOO) modification of machine settings is proposed to establish a basic data-driven model for collaborative system. Different with the conventional modification only considering geometric performance, it provides an improved modification also considering the physical performances. Finally, a double-curved shell model of hypoid gear finite element is used to perform the numerical loaded tooth contact analysis (NLTCA), as well as to establish the data-driven relationships between machine settings and physical performance evaluations. Immediately, whole development is divided into three sub-problems: i) optimal operations of the noise factors by measurement and numerical control (NC) compensation, ii) identification of the prescribed ease-off topography by multi-objective optimization using iterative reference point approach and iii) modification considering geometric performance by a trust region algorithm with step strategy. The numerical instance in practical applications is given to verify the proposed methodology.