An Analytical Model for Repositioning of 6 D.O.F Fixturing System

Lien vers la version éditeur: http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=8749247&fulltextType=RA&fileId=S2257777712000164Dimensional errors of the parts from a part family cause the initial misplacement of the workpiece on the fixture affecting the final product quality. Even if the part is positioned correctly, the external machining forces and clamping load cause the part to deviate from its position. This deviation depends on the external load and the fixture stiffness. In this article, a comprehensive analytical model of a 3-2-1 fixturing system is proposed, consisting of a kinematic and a mechanical part. The kinematic model relocates the initially misplaced workpiece in the machine reference through the axial advancements of six locators taking all the fixturing elements to be rigid. The repositioned part then shifts again from the corrected position due to the deformation of fixturing elements under clamping and machining forces. The mechanical model calculates this displacement of the part considering the locators and clamps to be elastic. The rigid cuboid baseplate, used to precisely re-locate the workpiece, is also considered elastic at the interface with the locators. Using small displacement hypothesis with zero friction at the contact points, Lagrangian formulation enables us to calculate the rigid body displacement of the workpiece, deformation of each locator, as well as the stiffness matrix and mechanical behavior of the fixturing system. This displacement of the workpiece is then finally compensated by the advancement of the six axial locators calculated through the kinematic model

Reconfiguration and tool path planning of hexapod machine tools

Hexapod machine tools have the potential to achieve increased accuracy, speed, acceleration and rigidity over conventional machines, and are regarded by many researchers as the machine tools of the next generation. However, their small and complex workspace often limits the range of tasks they can perform, and their parallel structure raises many new issues preventing the direct use of conventional tool path planning methods. This dissertation presents an investigation of new reconfiguration and tool path planning methods for enhancing the ability of hexapods to adapt to workspace changes and assisting them in being integrated into the current manufacturing environments. A reconfiguration method which includes the consideration of foot-placement space (FPS) determination and placement parameter identification has been developed. Based on the desired workspace of a hexapod and the motion range of its leg modules, the FPS of a hexapod machine is defined and a construction method of the FPS is presented. An implementation algorithm for the construction method is developed. The equations for identifying the position and orientation of the base joints for the hexapod at a new location are formulated. For the position identification problem, an algorithm based on Dialytic Elimination is derived. Through examples, it is shown that the FPS determination method can provide feasible locations for the feet of the legs to realize the required workspace. It is also shown that these identification equations can be solved through a numerical approach or through Dialytic Elimination using symbolic manipulation. Three dissimilarities between hexapods and five-axis machines are identified and studied to enhance the basic understanding of tool path planning for hexapods. The first significant difference is the existence of an extra degree of freedom (γ angle). The second dissimilarity is that a hexapod has a widely varying inverse Jacobian over the workspace. This leads to the result that a hexapod usually has a nonlinear path when following a straight-line segment over two sampled poses. These factors indicate that the traditional path planning methods should not be used for hexapods without modification. A kinematics-based tool path planning method for hexapod machine tools is proposed to guide the part placement and the determination of γ angle. The algorithms to search for the feasible part locations and γ sets are presented. Three local planning methods for the γ angle are described. It is demonstrated that the method is feasible and is effective in enhancing the performance of the hexapod machine. As the nonlinear error is computationally expensive to evaluate in real time, the measurement of total leg length error is proposed. This measure is proved to be effective in controlling the nonlinear error

Predictive Maintenance on the Machining Process and Machine Tool

This paper presents the process required to implement a data driven Predictive Maintenance (PdM) not only in the machine decision making, but also in data acquisition and processing. A short review of the different approaches and techniques in maintenance is given. The main contribution of this paper is a solution for the predictive maintenance problem in a real machining process. Several steps are needed to reach the solution, which are carefully explained. The obtained results show that the Preventive Maintenance (PM), which was carried out in a real machining process, could be changed into a PdM approach. A decision making application was developed to provide a visual analysis of the Remaining Useful Life (RUL) of the machining tool. This work is a proof of concept of the methodology presented in one process, but replicable for most of the process for serial productions of pieces

Development of Mobile Machining Cell

This report covers some initial aspects of development of the mobile InnoMill machining cell. The new machining paradigm where the machine is mounted on the workpiece is compared to the old paradigm where the workpiece is mounted inside the machine, and differences are discussed. Parametric studies of the workpiece case study of the InnoMill project, the Vestas V112-3.0MW wind turbine hub, are performed to supply insight regarding load capacity etc. for the machine designers. The hub finite element model is validated using experimental results from Operational Modal Analysis performed on the hub. Furthermore, the InnoMill concept is described, and work regarding the 6 degree of freedom parallel kinematic manipulator which is present in the concept is performed. A numerical procedure accounting for base deflections due to static loading is proposed and implemented. Additionally, a six degree of freedom spring-mass model vibrational response is compared to vibrational response obtained from experiments on the 6 degree of freedom parallel kinematic manipulator at Aarhus University. The model, which is based on assumptions commonly found in literature, is rejected. Finally, an outlook for the remaining part of the PhD project is presented

Using ensembles for accurate modelling of manufacturing processes in an IoT data-acquisition solution

The development of complex real-time platforms for the Internet of Things (IoT) opens up a promising future for the diagnosis and the optimization of machining processes. Many issues have still to be solved before IoT platforms can be profitable for small workshops with very flexible workloads and workflows. The main obstacles refer to sensor implementation, IoT architecture, and data processing, and analysis. In this research, the use of different machine-learning techniques is proposed, for the extraction of different information from an IoT platform connected to a machining center, working under real industrial conditions in a workshop. The aim is to evaluate which algorithmic technique might be the best to build accurate prediction models for one of the main demands of workshops: the optimization of machining processes. This evaluation, completed under real industrial conditions, includes very limited information on the machining workload of the machining center and unbalanced datasets. The strategy is validated for the classification of the state of a machining center, its working mode, and the prediction of the thermal evolution of the main machine-tool motors: the axis motors and the milling head motor. The results show the superiority of the ensembles for both classification problems under analysis and all four regression problems. In particular, Rotation Forest-based ensembles turned out to have the best performance in the experiments for all the metrics under study. The models are accurate enough to provide useful conclusions applicable to current industrial practice, such as improvements in machine programming to avoid cutting conditions that might greatly reduce tool lifetime and damage machine components.Projects TIN2015-67534-P (MINECO/FEDER, UE) of the Ministerio de Economía Competitividad of the Spanish Government and projects CCTT1/17/BU/0003 and BU085P17 (JCyL/FEDER, UE) of the Junta de Castilla y León, all of them co-financed through European-Union FEDER funds

Integrating tolerances in G and M codes using neural networks

Continuous integrated solutions from CAD down to the preparation of NC programs were developed in the recent years. However, if tolerances should be considered, the interaction of human experts is still necessary. A way to fill this gap in the production process is shown in this thesis. The study builds a relationship between the given design tolerances and including these tolerances in machining by generating respective G and M codes. The study focuses on physical phenomena and their inter-relationship while manufacturing. For example how the speed of machining, torque, power, depth of cut, etc. influences machining under specified tolerances. Artificial neural networks (ANN) have been used to generate required outputs because of their capability to learn from a given set of data points. Four different kinds of neural networks, as a module, have been used. with different kinds of learning rules (algorithms) depending on the type of inputs and outputs. The whole model incorporates retrieval of tolerances from a CAD software and running the algorithms for (i) Dimensional tolerance analysis, (ii) Control of feed rate, spindle speed, depth of cut and cutting forces, (iii) Propagation of errors in multistage machining, and (iv) Vectorization of geometrical tolerances. Machining processes would include (i) Milling, (ii) Turning, and (iii) Drilling. Then the corresponding outputs are interpreted and analyzed to generate G and M codes. This study has shown how ANN can revolutionize NC machine manufacturing. A case study illustrates the effectiveness of the proposed method