Evaluating the bending response of two osseointegrated transfemoral implant systems using 3D digital image correlation

Osseointegrated transfemoral implants have been introduced as a prosthetic solution for above knee amputees. They have shown great promise, providing an alternative for individuals who could not be accommodated by conventional, socket-based prostheses; however, the occurrence of device failures is of concern. In an effort to improve the strength and longevity of the device, a new design has been proposed. This study investigates the mechanical behavior of the new taper-based assembly in comparison to the current hex-based connection for osseointegrated transfemoral implant systems. This was done to better understand the behavior of components under loading, in order to optimize the assembly specifications and improve the useful life of the system. Digital image correlation was used to measure surface strains on two assemblies during static loading in bending. This provided a means to measure deformation over the entire sample and identify critical locations as the assembly was subjected to a series of loading conditions. It provided a means to determine the effects of tightening specifications and connection geometry on the material response and mechanical behavior of the assemblies. Both osseoinegrated assemblies exhibited improved strength and mechanical performance when tightened to a level beyond the current specified tightening torque of 12 N m. This was shown by decreased strain concentration values and improved distribution of tensile strain. Increased tightening torque provides an improved connection between components regardless of design, leading to increased torque retention, decreased peak tensile strain values, and a more gradual, primarily compressive distribution of strains throughout the assembly. \ua9 2011 American Society of Mechanical Engineers.Peer reviewed: YesNRC publication: Ye

Hybrid sequential fault estimation for multi-mode diagnosis of gas turbine engines

Health condition monitoring of Gas Turbine Engine (GTE) components is key for predictive maintenance planning. The task is challenging, as the gas-path components are mostly inaccessible for direct measurements, while at the same time hidden incipient faults must be diagnosed using the available measurements. The presence of multiple faults with similar symptoms adds to the complexity of the diagnostic process. In previous research work, a data-driven multi-mode fault parameter estimation scheme was introduced for real-time multimode diagnosis of GTEs under diverse operating conditions and fault scenarios. In this work, a hybrid diagnostic framework is developed that fuses the results from a measurement-based fault parameter estimation strategy together with a fault propagation model. The hybrid framework uses a novel particle filter (PF) structure with redundant measurements that facilitates updating the particle weights while reducing the dimensionality of the measurement likelihood. Applying the develo

Hybrid data-driven physics-based model fusion framework for tool wear prediction

An integral part of modern manufacturing process management is to acquire useful information from machining processes to monitor machine and tool condition. Various models have been introduced to detect, classify, and predict tool wear, as a key parameter of the machining process. In more recent developments, sensor-based approaches have been attempted to infer the tool wear condition from real-time processing of the measurement data. Experiments show that the physics-based prediction models can include large uncertainties. Likewise, the measurement-based (or sensor-based) inference techniques are affected by sensor noise and measurement model uncertainties. To manage uncertainties and noise of both methods, a hybrid framework is proposed to fuse together the results of the prediction model and the measurement-based inference data in a stepwise manner. The fu