In this paper, the optimum design of parallel kinematic toolheads is implemented using genetic algorithms with the consideration of the global stiffness and workspace volume of the toolheads. First, a complete kinetostatic model is developed which includes three types of compliance, namely, actuator compliance, leg bending compliance and leg axial compliance. Second, based on this model, two kinetostatic performance indices are introduced to provide a new means of measuring compliance over the workspace. These two kinetostatic performance indices are the mean value and the standard deviation of the trace of the generalized compliance matrix. The mean value represents the average compliance of the Parallel Kinematic Machines over the workspace, while the standard deviation indicates the compliance fluctuation relative to the mean value. Third, design optimization is implemented for global stiffness and working volume based on kinetostatic performance indices. Additionally, some compliance comparisons between Tripod toolhead and other two principal Tripod-based Parallel Kinematic Machines are conducted.
In this paper, a new method for optimal calibration of parallel kinematic machines (PKMs) is presented. The basis of the methodology is to exploit the least error sensitive regions in the workspace to yield optimal calibration. To do so, an error model is developed that takes into consideration all the geometric errors due to imprecision in manufacturing and assembly. Based on this error model, it is shown that the error mapping from the geometric errors to the pose error of the PKM depends on the Jacobian inverse. The Jacobian inverse would introduce spurious errors that would affect the calibration results, if used without proper care. Hence, areas in the workspace with smaller condition numbers are selected for calibration. Simulations and experiments are presented to show the effectiveness of the proposed method. Calibration software based on the proposed method has been embedded in the tripod developed at the National Research Council of Canada’s Integrated Manufacturing Technologies Institute.
Machine learning can be enhanced through the integration of external knowledge. This method, called knowledge informed machine learning, is also applicable within the field of Prognostics and Health Management (PHM). In this paper, the various methods of knowledge informed machine learning, from a PHM context, are reviewed with the goal of helping the reader understand the domain. In addition, a knowledge informed machine learning technique is demonstrated, using the common IMS and PRONOSTIA bearing data sets, for remaining useful life (RUL) prediction. Specifically, knowledge is garnered from the field of reliability engineering which is represented through the Weibull distribution. The knowledge is then integrated into a neural network through a novel Weibull-based loss function. A thorough statistical analysis of the Weibull-based loss function is conducted, demonstrating the effectiveness of the method on the PRONOSTIA data set. However, the Weibull-based loss function is less effective on the IMS data set. The results, shortcomings, and benefits of the approach are discussed in length. Finally, all the code is publicly available for the benefit of other researchers.
In this paper, error sensitivity analysis is discussed for the purpose of optimal calibration of parallel kinematic machines (PKMs). The idea is to find a less error sensitive area in the workspace for calibration. To do so, an error model is developed that takes into consideration all the geometric errors due to imprecision in manufacturing and assembly. Based on this error model, it is shown that the error mapping from the geometric errors to the pose error of the PKM depends on the Jacobian inverse. The Jacobian inverse would introduce spurious errors that would affect the calibration results, if used without proper care. Hence, it is suggested to select the areas in the workspace with smaller condition numbers for calibration. A case study is presented to illustrate the proposed method.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.