Improved workpiece location accuracy through fixture layout optimization

Fixture layout for machining thin-walled structures plays an important role in fixture performance. This work focuses on the optimization design of machining fixture layout including clamp layout and the layout of support heads. A universal optimization methodology that can automatically generate the optimal machining fixture layout is proposed. The optimization of fixture layout is implemented based on the criteria of minimizing maximum deflection of the thin-walled workpiece while remain relatively small torque caused by the clamping tables. An evolutionary algorithm is employed to solve the fixture layout optimization for the optimal solution. The optimal position of the support heads and the layout of clamping tables obtained from this work vary along with the change of cutting force. To validate the proposed optimization methodology, a fixture layout design for a typical thin-walled structure is optimized as an example. The case study demonstrates the application of the proposed approach in the fixture layout design for thin-walled structures, which is beneficial to design an optimal fixture layout.

Fixtures are the work-holding devices, widely used in manufacturing, to completely immobilize the workpiece during machining. The position of fixture elements around the workpiece strongly influences the workpiece deformation which in-turn affects the machining accuracy. The workpiece deformation can be minimized by finding the appropriate position for the locators and clamps. Thus, it is necessary to model the complex behavioral relationship that exists in the fixture–workpiece system. In this research paper, response surface methodology is used to model the relationship between position of locators and clamps and maximum deformation of the workpiece during end-milling, and then the developed model has been optimized by genetic algorithm and particle swarm optimization. As the predictive model is being developed by response surface methodology, a huge reduction in computational complexity and time is achieved during the optimization of machining fixture layout. Also, it is evident that the approach which integrates response surface methodology and particle swam optimization produces better results.

Optimisation of machining fixture layout under multi-constraints

Finite element study of a workpiece in a machining fixture

Machining fixture layout optimization using the genetic algorithm

A review of the recent development of the machining fixture configuration/layout is presented in this paper. The literature review is mainly focused on the recently developed optimal fixture configuration under the dynamic conditions of the workpiece. In this review paper, the fixture design, fixture analysis, fixture synthesis, fixture layout design, optimization of fixture layout design, various optimization algorithms, and case studies of two- and three-dimensional workpiece geometries under dynamic conditions have been emphasized specially. The further scopes of the research are finally summarized.

To estimate influence of velocity on kinematic accuracy for a cross-linked Stewart type Parallel Machine Tool, position and orientation errors of the moving tool platform are researched. Based on rigid body dynamics, inertial and friction forces and moments are considered. Firstly, analytical expression of driving force is derived for each link. Secondly, change of length is derived using Hooke’s Law for each link. Then mapping matrix between change of link length and change of platform position and orientation is derived based on both Euler angle and revolving angle around a spatial axis. Finally, analytical expression of position and orientation errors of the moving platform is derived. Figures of distribution of position and orientation errors in workspace under two velocities are obtained respectively. The results show that, in frequently used workspace, all of the three components of position error are less than 3.5μm. All of them increase with z coordinate of platform center. Position error is influenced slightly by velocity. The difference of position error between two velocities is less than 2%.

In the field of digital media, intelligent extraction and layout optimization of visual elements face challenges such as inaccurate semantic understanding of elements and low efficiency in generating layout strategies. This study proposes an extraction and layout optimization model that integrates visual semantic understanding with intelligent optimization strategies, based on a segmentation Vision Transformer and Multi-Objective Firefly Algorithm. The model also utilizes the improved optical flow methods to efficiently capture dynamic information during the design process. Experimental results show that the segmentation Vision Transformer algorithm achieves an extraction accuracy of 98.8±0.2% for different categories of visual elements. As the training progresses to 50 iterations, the average Intersection-Over-Union stabilizes at 0.95, and the harmonic mean of recall reaches 98.17±0.38\%. The evaluation of the integrated model shows that it achieves 99% accuracy in extracting visually similar elements. After layout optimization using the model, the aesthetic score increases to 95.6, and the spatial occupancy rate improves to 97.2%. The above results indicate that the model proposed by the research institute can effectively enhance the accuracy of visual element extraction and the quality of layout optimization, significantly reducing the reliance of traditional methods on manual rules, and providing an efficient and adaptive solution for the automated design of digital media.

With the rapid development of the logistics industry, the demand for logistics activities is increasing significantly. Concurrently, growing urbanization is causing the space for logistics and warehousing to become limited. Thus, more and more attention is being paid to the planning and construction of logistics facilities. However, due to spatiotemporal trajectory data (such as truck GPS data) being used less often in planning, the method of quantitative analysis for freight spatiotemporal activity is limited. Thus, the spatial layout of logistics and warehousing land does not match the current demand very well. In addition, it is necessary to consider the interactive relationship with the urban built environment in the process of optimizing layout, in order to comprehensively balance the spatial coupling with the functions of housing, transportation, industry, and so on. Therefore, the layout of logistics and warehouse land could be treated as a multi-objective optimization problem. This study aims to establish a model for logistics and warehouse land layout optimization to achieve a supply–demand matching. The proposed model comprehensively considers economic benefits, time benefits, cost benefits, environmental benefits, and other factors with freight GPS data, land-use data, transportation network data, and other multi-source data. A genetic algorithm is built to solve the model. Finally, this study takes the Wuhan urban development area as an example to practice the proposed method in three scenarios in order to verify its effectiveness. The results show that the optimization model solves the problem of mismatch between the supply and demand of logistics spaces to a certain extent, demonstrating the efficiency and scientificity of the optimization solutions. Based on the results of the three scenarios, it is proven that freight activities could effectively enhance the scientific validity of the optimization solution and the proposed model could optimize layouts under different scenario requirements. In summary, this study provides a practical and effective tool for logistics- and warehouse-land layout evaluation and optimization for urban planners and administrators.

Optimization of fixture layout by means of the genetic algorithm

Analysis and characterization of workpiece-fixture contact forces are important in fixture design since they define the fixture stability during clamping and strongly influence workpiece accuracy during manufacturing. This paper presents a method for predicting and analyzing the normal and frictional contact forces between workpiece-fixture contacts. The fixture and workpiece are considered to be rigid bodies, and the model solution is solved as a constrained quadratic optimization by applying the minimum norm principle. The model reveals some intricate properties of the passive contact forces, including the potential of a locator release and the history dependency during a sequence of clamping and/or external force loading. Further, a notion of passive force closure is considered to characterize the passive nature of the fixture forces. Geometric conditions for two types of passive force closure (concordant and discordant closures) are provided, showing a complication of released locator under clamping with a limited role in force closure. Model predictions are shown to be in good agreement with known results of an elastic-contact model prediction and experimental measurements. The passive force closure conditions are illustrated with examples. This presented method is conceptually simple and computationally efficient. It is particularly useful in the early stages of fixture design and process planning.

Although capsule endoscopy is already used for diagnosis of the gastrointestinal tract, a method to precisely localize the capsules, important for accurate diagnosis, is lacking. Static magnetic localization is a promising solution for that purpose. In this paper, the simulation of a differential static magnetic localization system with dynamic geomagnetic compensation was optimized. First, a convergence-test for the position and orientation errors as a function of the dimension of the computational domain was conducted. Subsequently, the diameter-to-length ratio of a permanent magnet was varied and the corresponding position and orientation errors, as well as the mean magnetic flux density measured at the sensor positions, were compared. The results revealed that for a computational domain radius of 800 mm, the position and orientation errors converged to less than 0.1 mm and 0.1°, respectively. The position and orientation errors were also of that order, even with the smallest permanent magnet employed in the study. Furthermore, the mean magnetic flux density measured at the sensors of the proposed magnetic localization system would be detectable using state-of-the-art magnetometers. It is concluded that the differential localization method is also feasible for small permanent magnets, which is especially important considering the limited space within endoscopy capsules.

Workpiece localization is the process of obtaining the location of a workpiece in a reference frame of a robotic workspace. The location (position and orientation) is represented by the transformation between a local frame associated with the workpiece and the specified reference frame in the workspace. In this work, we study the workpiece localization problem without the two commonly adopted restrictive assumptions: the data used to calculate the transformation is readily available and the correspondence between the data sets used for calculation is known. The goal is to automate the localization process starting from efficient data collection to determining the workpiece location in the workspace. We describe a strategy that includes the following aspects: predicting the correspondence between the measured data and the workpiece CAD model data; generating representative vectors that would aid in determining the next-best-view for collecting new information of the workpiece location; evaluating a search region to find the next sensor location that satisfies both the robot kinematics as well as sensor field-of-view constraints while giving the maximum view gain; and calculating the rigid body transformation from the local frame to the world frame to localize the workpiece. Numerical simulation and experimental results are presented and discussed for the proposed strategy.

Accuracy test of five-axis CNC machine tool with 3D probe-ball. Part II: errors estimation

Magnetic localization provides a feasible way to estimate the position and orientation of the wireless capsule endoscopy embedded with a permanent magnet. However, the magnetic localization suffers from the drifting effect from magnetic noises and the uncertain initial guess during the optimization. In this paper, a localization system with noise cancellation is proposed to reduce the drifting effect of the geomagnetic noise and obtain the initial guess in a simple and effective way. Compared to the prior arts, the proposed system is able to achieve a lower positioning and orientation error under the same system configuration and makes it possible for the patient to move around while wearing the localization system. Besides, the proposed algorithm improves the consistency of positioning error within the localization region. Within a volume of 380 mm by 270 mm by 240 mm covered by 16 digital magnetic sensors, the average positioning error obtained from the proposed algorithm is around 10 mm and the average orientation error is around 12°.