• Proposed a new neural operator for rapid robot full-field deformation prediction. • Geometric-load graph constructed to unify structure and force representation. • 6000 times faster than finite element analysis while maintaining high accuracy. • Generalizable and applicable to varying geometries, postures, and loading conditions. Robotic design and optimization often require extensive simulation-based iterations, particularly for predicting static deformations under varying postures, external loads, and gravitational conditions. Conventional finite element simulations can achieve high accuracy but are computationally expensive, limiting their use in large-scale design optimization. Data-driven approaches have emerged to accelerate prediction; however, most existing methods are restricted to simplified scenarios, such as fixed geometries with varying poses or fixed poses with varying loads, and typically predict only partial deformation at points rather than the full deformation field. To enable fast and generalizable deformation prediction, a new neural operator architecture named Robot-NO is developed. A geometric–load graph based on the finite element mesh connectivity, where each node encodes spatial coordinates with local load conditions is constructed. A graph neural network is then employed to extract a unified global representation that captures both geometric structure and external force distribution. A query–decoder module combines global features with arbitrary query-coordinates to predict the full deformation field. This query-based formulation enables accurate continuous deformation prediction across varying robot geometries, postures, and load conditions. Experimental results demonstrate that the proposed model achieves a root mean square error of 0.91 μ m in deformation prediction and is 6000 times faster than conventional finite element simulations. This significant improvement enables rapid design, analysis and optimization for robotic mechanisms.

Design parameters optimization is regarded as an effective way to reduce energy consumption of a machine tool, which are contributed by several subsystems with coupling relationships affecting the total energy consumption. Therefore, it is of importance to study the coupling relationships between subsystems at the design stage for energy saving. However, there are multiple key energy consuming subsystems associated with various design parameters, leading to a complex optimization model of this problem. It is rather time-consuming to optimize such a complex model and hard to obtain global optimal results. To address this challenging problem, this article proposes an energy saving design approach of a gear hobbing machine based on Analytical Target Cascading (ATC) method. Firstly, the energy consumption models of each subsystem are established and the coupling relationships are analyzed. Then, the decomposition of the energy coupling model is conducted and the comprehensive optimization model is constructed by ATC. One novel adaptive sequential sampling method is proposed with the surrogate model to achieve the collaborative optimization of both structural parameters and control parameters. Finally, three optimization algorithms are developed to comparatively study the energy-optimal schemes. Testing results indicate that the energy consumption can be reduced by 9.72%, with lighter moving component and reduced deformation.Note to Practitioners—This article provides one method for the energy saving of the gear hobbing machine at the design stage. Previous studies only focus on the influence of one design factor on energy consumption, and the optimization is under a mixed optimization model, which is hard to obtain global optimal results. This article develops an energy saving design method considering the comprehensive influence of design factors for energy saving of multiple subsystems. Based on ATC, an energy coupling model can be divided into several energy consuming subsystems and the independent optimization of each subsystem can be achieved. To ensure the lightweight and control abilities, a surrogate model and a novel sequential sampling method are applied. Results show the effectiveness of the proposed method and the energy reduction can be achieved with a good structural and control performance.

In this paper, a novel adjustable-stiffness force sensor is developed for multitask measurements requiring different force resolutions and ranges. The applied force of the force sensor is indirectly measured through the linear deformation instead of the structure strain through an optical linear encoder. The main structure of the force sensor is actually a linear variable stiffness mechanism with a compact size and a large stiffness change. Its stiffness can be continuously adjusted by changing the effective second moment of area of the structure. Thus, the force sensor has an adjustable range and resolution since the displacement resolution of the optical linear encoder is constant. The stiffness modeling of the sensor is performed based on the matrix method, which is then evaluated by the finite element analysis. A principle prototype is finally fabricated for the adjustable-stiffness test and a concrete application example. The testing results show that the stiffness and resolution of the force sensor can be changed by the proposed stiffness adjustment. Moreover, it is effective to measure different-resolution forces. This adjustable-stiffness approach can be also extended to the design of a torque sensor or a force/torque sensor.

Decision makers are required to make critical decisions throughout all stages of a life-cycle in large-scale projects. These decisions are important as they impact upon the outcome and the success of projects. In this paper we present an evidential reasoning framework to aid decision-makers in the decision making process. This approach utilizes the Dezert-Smarandache Theory (DSm) to fuse heterogeneous evidence sources that suffer from levels of uncertainty, imprecision and conflicts to provide beliefs for decision options. To analyze the impact that source reliability and priority has upon the decision making process a reliability discounting technique along with a priority discounting technique are applied. Application of the evidential reasoning framework is illustrated using a Case Study based in the Aerospace domain.

PKM (parallel kinematic machine) based production systems have shown their potential for large volume manufacturing. However, integrating 5-axis PKM machine tools directly into an existing system will lead to unnecessary redundant motions, which may cause many negative effects on the accuracy and productivity of the system. To cope with this problem, a new PKM based production system for aircraft wing assembly, named PAW, is proposed by using a holistic design approach in this paper. PAW is constructed by a unique PKM architecture mounted on a gantry-like worktable. Mobility, kinematics and Jacobian analyses are conducted, as well as dimensional optimization for the new PKM. Specially, the task space variables are directly employed for kinematic analysis, and the task workspace is directly utilized for the dimensional synthesis. As a result, the PAW system shows a better performance comparing to the those currently in production. It is not only suitable for wing assembly tasks (e.g. drilling) but also for other large volume manufacturing tasks, such as trimming and sealing.

Workspace analysis and optimization are important in a manipulator design. As the complete workspace of a 6-DOF manipulator is embedded into a 6-dimensional space, it is difficult to quantify and qualify it. Most of the literatures only considered the 3-D sub workspaces of the complete 6-D workspace. In this paper, a finite-partition approach of the special Euclidean group SE(3) is proposed based on the topology properties of SE(3), which is the product of special orthogonal group SO(3) and Ropf3. It is known that the SO(3) is homeomorphic to a solid ball D3with antipodal points identified while the geometry of Ropf3can be regarded as a cuboid. Furthermore, the solid ball and the cuboid can be parametrically and proportionally partitioned into a number of elements. Therefore, a basis volume element of SE(3) is the product of a basis volume element of Ropf3and a basis volume element of SO(3), which is the product of a basis volume element of D3and its associated integration measure. By this way, the integration of the complete 6-D workspace volume become the simple summation of the basis volume elements of SE(3). Two global performance indices, i.e., workspace volume ratio (Wr) and global condition index (GCI)., are defined over the complete 6-D workspace. An optimization algorithm is developed for a 3RPlowbarPS parallel manipulator to illustrate the effectiveness of the finite-partition approach. As a result, the workspace optimization method is valid although it is computationally intensive

A new three-limb, six-degree-of-freedom (DOF) parallel manipulator (PM), termed a selectively actuated PM (SA-PM), is proposed. The end-effector of the manipulator can produce 3-DOF spherical motion, 3-DOF translation, 3-DOF hybrid motion, or complete 6-DOF spatial motion, depending on the types of the actuation (rotary or linear) chosen for the actuators. The manipulator architecture completely decouples translation and rotation of the end-effector for individual control. The structure synthesis of SA-PM is achieved using the line geometry. Singularity analysis shows that the SA-PM is an isotropic translation PM when all the actuators are in linear mode. Because of the decoupled motion structure, a decomposition method is applied for both the displacement analysis and dimension optimization. With the index of maximal workspace satisfying given global conditioning requirements, the geometrical parameters are optimized. As a result, the translational workspace is a cube, and the orientation workspace is nearly unlimited.

A parallel manipulator (PM) based on 3-limb design termed a selectively actuated parallel manipulator (SA-PM) is proposed. The end-effector of the manipulator can produce 3-DOF spherical motion, 3-DOF translation, 3-DOF hybrid motion, or complete 6-DOF spatial motion depending on the types of the actuation (rotary or linear) chosen for the actuators. The manipulator architecture decouples translation and rotation of the end-effector for individual control. The structure synthesis of SA-PM is achieved using the line geometry. A Lego model of the SA-PM is built for the constructibility and preliminary study. Singularity analysis of the SA-PM based on geometry is presented for all actuation schemes to facilitate the kinematic design of the manipulator. Because of the decoupled motion, the SA-PM has a great potential for high precision motion alignment and assembly, especially in micro- or nano-motion stage design.