Identification and model-based compensation of Striebeck friction

The paper deals with the measurement, identification and compensation of low velocity friction in positioning systems. The introduced algorithms are based on a linearized friction model, which can easily be introduced in tracking control algorithms. The developed friction measurement and compensation methods can be implemented in simple industrial controller architectures, such as microcontrollers. Experimental measurements are provided to show the performances of the proposed control algorithm

Detection of Interconnect Failure Precursors using RF Impedance Analysis

Many failures in electronics result from the loss of electrical continuity of common board-level interconnects such as solder joints. Measurement methods based on DC resistance such as event detectors and data-loggers have long been used by the electronics industry to monitor the reliability of interconnects during reliability testing. DC resistance is well-suited for characterizing electrical continuity, such as identifying an open circuit, but it is not useful for detecting a partially degraded interconnect. Degradation of interconnects, such as cracking of solder joints due to fatigue or shock loading, usually initiates at an exterior surface and propagates towards the interior. A partially degraded interconnect can cause the RF impedance to increase due to the skin effect, a phenomenon wherein signal propagation at frequencies above several hundred MHz is concentrated at the surface of a conductor. Therefore, RF impedance exhibits greater sensitivity compared to DC resistance in detecting early stages of interconnect degradation and provides a means to prevent and predict an important cause of electronics failures. This research identifies the applicability of RF impedance as a means of a failure precursor that allows for prognostics on interconnect degradation based on electrical measurement. It also compares the ability of RF impedance with that of DC resistance to detect early stages of interconnect degradation, and to predict the remaining life of an interconnect. To this end, RF impedance and DC resistance of a test circuit were simultaneously monitored during interconnect stress testing. The test vehicle included an impedance-controlled circuit board on which a surface mount component was soldered using two solder joints at the end terminations. During stress testing, the RF impedance exhibited a gradual non-linear increase in response to the early stages of solder joint cracking while the DC resistance remained constant. The gradual increase in RF impedance was trended using prognostic algorithms in order to predict the time to failure of solder joints. This prognostic approach successfully predicted solder joint remaining life with a prediction error of less than 3%. Furthermore, it was demonstrated both theoretically and experimentally that the RF impedance analysis was able to distinguish between two competing interconnect failure mechanisms: solder joint cracking and pad cratering. These results indicate that RF impedance provides reliable interconnect failure precursors that can be used to predict interconnect failures. Since the performance of high speed devices is adversely affected by early stages of interconnect degradation, RF impedance analysis has the potential to provide improved reliability assessment for these devices, as well as accurate failure prediction for current and future electronics

Robotic learning of force-based industrial manipulation tasks

Even with the rapid technological advancements, robots are still not the most comfortable machines to work with. Firstly, due to the separation of the robot and human workspace which imposes an additional financial burden. Secondly, due to the significant re-programming cost in case of changing products, especially in Small and Medium-sized Enterprises (SMEs). Therefore, there is a significant need to reduce the programming efforts required to enable robots to perform various tasks while sharing the same space with a human operator. Hence, the robot must be equipped with a cognitive and perceptual capabilities that facilitate human-robot interaction. Humans use their various senses to perform tasks such as vision, smell and taste. One sensethat plays a significant role in human activity is ’touch’ or ’force’. For example, holding a cup of tea, or making fine adjustments while inserting a key requires haptic information to achieve the task successfully. In all these examples, force and torque data are crucial for the successful completion of the activity. Also, this information implicitly conveys data about contact force, object stiffness, and many others. Hence, a deep understanding of the execution of such events can bridge the gap between humans and robots. This thesis is being directed to equip an industrial robot with the ability to deal with force perceptions and then learn force-based tasks using Learning from Demonstration (LfD).To learn force-based tasks using LfD, it is essential to extract task-relevant features from the force information. Then, knowledge must be extracted and encoded form the task-relevant features. Hence, the captured skills can be reproduced in a new scenario. In this thesis, these elements of LfD were achieved using different approaches based on the demonstrated task. In this thesis, four robotics problems were addressed using LfD framework. The first challenge was to filter out robots’ internal forces (irrelevant signals) using data-driven approach. The second robotics challenge was the recognition of the Contact State (CS) during assembly tasks. To tackle this challenge, a symbolic based approach was proposed, in which a force/torque signals; during demonstrated assembly, the task was encoded as a sequence of symbols. The third challenge was to learn a human-robot co-manipulation task based on LfD. In this case, an ensemble machine learning approach was proposed to capture such a skill. The last challenge in this thesis, was to learn an assembly task by demonstration with the presents of parts geometrical variation. Hence, a new learning approach based on Artificial Potential Field (APF) to learn a Peg-in-Hole (PiH) assembly task which includes no-contact and contact phases. To sum up, this thesis focuses on the use of data-driven approaches to learning force based task in an industrial context. Hence, different machine learning approaches were implemented, developed and evaluated in different scenarios. Then, the performance of these approaches was compared with mathematical modelling based approaches.</div