Pragmatic Micrometre to Millimetre Calibration Using Multiple Methods for Low-Coherence Interferometer in Embedded Metrology Applications.

In-situ metrology utilised for surface topography, texture and form analysis along with quality control processes requires a high-level of reliability. Hence, a traceable method for calibrating the measurement system's transfer function is required at regular intervals. This paper compares three methods of dimensional calibration for a spectral domain low coherence interferometer using a reference laser interferometer versus two types of single material measure. Additionally, the impact of dataset sparsity is shown along with the effect of using a singular calibration dataset for system performance when operating across different media.

Performance Evaluation of a Robot-Mounted Interferometer for an Industrial Environment.

High value manufacturing requires production-integrated, fast, multi-sensor and multi-scale inspection. To meet this need, the robotic deployment of sensors within the factory environment is becoming increasingly popular. For microscale measurement applications, robot-mountable versions of high-resolution instruments, that are traditionally deployed in a laboratory environment, are now becoming available. However, standard methodologies for the evaluation of these instruments, particularly when mounted to a robot, have yet to be fully defined, and therefore, there is limited independent evaluation data to describe the potential performance of these systems. In this paper, a detailed evaluation approach is presented for light-weight robot mountable scanning interferometric sensors. Traditional evaluation approaches are considered and extended to account for robotic sensor deployment within industrial environments. The applicability and value of proposed evaluation is demonstrated through the comprehensive characterization of a Heliotis H6 interferometric sensors. The results indicate the performance of the sensor, in comparison to a traditional laboratory-based system, and demonstrate the limits of the sensor capability. Based-on the evaluation an effective strategy for robotic deployment of the sensor is demonstrated.

Performance Assessment of a New Variable Stiffness Probing System for Micro-CMMs.

When designing micro-scale tactile probes, a design trade-off must be made between the stiffness and flexibility of the probing element. The probe must be flexible enough to ensure sensitive parts are not damaged during contact, but it must be stiff enough to overcome attractive surface forces, ensure it is not excessively fragile, easily damaged or sensitive to inertial loads. To address the need for a probing element that is both flexible and stiff, a novel micro-scale tactile probe has been designed and tested that makes use of an active suspension structure. The suspension structure is used to modulate the probe stiffness as required to ensure optimal stiffness conditions for each phase of the measurement process. In this paper, a novel control system is presented that monitors and controls stiffness, allowing two probe stiffness values ("stiff" and "flexible") to be defined and switched between. During switching, the stylus tip undergoes a displacement of approximately 18 µm, however, the control system is able ensure a consistent flexible mode tip deflection to within 12 nm in the vertical axis. The overall uncertainty for three-dimensional displacement measurements using the probing system is estimated to be 58 nm, which demonstrates the potential of this innovative variable stiffness micro-scale probe system.

Plastic Deformation of Micromachined Silicon Diaphragms with a Sealed Cavity at High Temperatures.

Single crystal silicon (SCS) diaphragms are widely used as pressure sensitive elements in micromachined pressure sensors. However, for harsh environments applications, pure silicon diaphragms are hardly used because of the deterioration of SCS in both electrical and mechanical properties. To survive at the elevated temperature, the silicon structures must work in combination with other advanced materials, such as silicon carbide (SiC) or silicon on insulator (SOI), for improved performance and reduced cost. Hence, in order to extend the operating temperatures of existing SCS microstructures, this work investigates the mechanical behavior of pressurized SCS diaphragms at high temperatures. A model was developed to predict the plastic deformation of SCS diaphragms and was verified by the experiments. The evolution of the deformation was obtained by studying the surface profiles at different anneal stages. The slow continuous deformation was considered as creep for the diaphragms with a radius of 2.5 mm at 600 °C. The occurrence of plastic deformation was successfully predicted by the model and was observed at the operating temperature of 800 °C and 900 °C, respectively.

Detecting Changes and Avoiding Catastrophic Forgetting in Dynamic Partially Observable Environments.

The ability of an agent to detect changes in an environment is key to successful adaptation. This ability involves at least two phases: learning a model of an environment, and detecting that a change is likely to have occurred when this model is no longer accurate. This task is particularly challenging in partially observable environments, such as those modeled with partially observable Markov decision processes (POMDPs). Some predictive learners are able to infer the state from observations and thus perform better with partial observability. Predictive state representations (PSRs) and neural networks are two such tools that can be trained to predict the probabilities of future observations. However, most such existing methods focus primarily on static problems in which only one environment is learned. In this paper, we propose an algorithm that uses statistical tests to estimate the probability of different predictive models to fit the current environment. We exploit the underlying probability distributions of predictive models to provide a fast and explainable method to assess and justify the model's beliefs about the current environment. Crucially, by doing so, the method can label incoming data as fitting different models, and thus can continuously train separate models in different environments. This new method is shown to prevent catastrophic forgetting when new environments, or tasks, are encountered. The method can also be of use when AI-informed decisions require justifications because its beliefs are based on statistical evidence from observations. We empirically demonstrate the benefit of the novel method with simulations in a set of POMDP environments.

Symbolic-Based Recognition of Contact States for Learning Assembly Skills.

Imitation learning is gaining more attention because it enables robots to learn skills from human demonstrations. One of the major industrial activities that can benefit from imitation learning is the learning of new assembly processes. An essential characteristic of an assembly skill is its different contact states (CS). They determine how to adjust movements in order to perform the assembly task successfully. Humans can recognize CSs through haptic feedback. They execute complex assembly tasks accordingly. Hence, CSs are generally recognized using force and torque information. This process is not straightforward due to the variations in assembly tasks, signal noise and ambiguity in interpreting force/torque (F/T) information. In this research, an investigation has been conducted to recognize the CSs during an assembly process with a geometrical variation on the mating parts. The F/T data collected from several human trials were pre-processed, segmented and represented as symbols. Those symbols were used to train a probabilistic model. Then, the trained model was validated using unseen datasets. The primary goal of the proposed approach aims to improve recognition accuracy and reduce the computational effort by employing symbolic and probabilistic approaches. The model successfully recognized CS based only on force information. This shows that such models can assist in imitation learning.