Ernur Karadoğan

We investigated the effect of arm pivot joints that are typically used during haptic exploration by evaluating four joints of the human arm (metacarpophalangeal joint of the index finger, wrist, elbow, and shoulder joints). Using a virtual stiffness discrimination task, a four-session psychophysical experiment was conducted with 38 participants (25 male and 13 female); each session was conducted with one of the four joints as the pivot joint during haptic exploration. The participants were asked to judge the stiffness of the top surface of two computer-generated cylinders by determining the stiffer one while using their dominant hand’s index finger. A two-alternative forced-choice procedure was employed by assigning one cylinder a constant stiffness value of 1.0 N/mm (standard side) and the remaining cylinder a variable stiffness value (comparison side). Using a custom-made stylus for the Geomagic TouchTM (3D Systems, Inc., Rock Hill, SC, USA) haptic interface, the participants were able to feel the stiffness of these virtual surfaces only with their index fingers. It was observed that the average Weber fraction monotonically decreased as the pivot joint shifted toward the torso (i.e., a shift from the metacarpophalangeal joint to the shoulder joint); this decrease was not statistically significant, which suggests that the selection of the pivot joint was not a determining factor for the sensitivity to discriminate stiffness. In general, the palpation speed and force exerted by the participants on the standard side during the haptic exploration showed a tendency to increase when the pivot joint shifted toward the torso; the difference in average palpation speed and force across the pivot joints was not statistically significant.

Haptic technology enables users to utilize their sense of touch while engaging with a virtual representation of objects in a simulated environment. It is a bidirectional technology in that it facilitates the interaction between the user and these virtual representations by allowing them to apply force onto one another, which is analogous to our real-world interactions with physical objects as action-reaction pairs. The sense of touch is a powerful and innate learning tool that we readily employ starting from very early ages as infants even before learning to walk. Therefore, it is natural that incorporating haptic technology into pedagogical methods has been an active research area as it has significant potential to enrich the learning experience and provide an engaging environment for learners. In this paper, we reviewed studies from various disciplines that incorporate haptics to increase the quality of teaching and learning while emphasizing the underlying cognitive theories. In that direction, we describe two of the most common cognitive theories, the Cognitive Load and Embodied Cognition theories, that developers use to support haptic technology’s implications and use in learning environments. We then explore the effects of haptic design on its current applicability following these two theories. Finally, we summarize the best design practices to develop haptic simulations for learning, address gaps in current research, and propose new research directions.

Shape memory alloys (SMAs) are classified as smart materials due to their capacity to display shape memory effect and pseudoelasticity with changing temperature and loading conditions. The thermomechanical behavior of SMAs has been simulated by several constitutive models that adopted microscopic thermodynamic or macroscopic phenomenological approaches. The Ivshin–Pence model is one of the most popular SMA macroscopic phenomenological constitutive models. The construction of the model requires involvement of parameters that possess inherent uncertainty. Under varying operating temperatures and loading conditions, the uncertainty in these parameters propagates and, therefore, affects the predictive power of the model. The propagation of uncertainty while using this model in real-life applications can result in performance discrepancies or failure at extreme conditions. In this study, we employed a probabilistic approach to perform the sensitivity and uncertainty analysis of the Ivshin–Pence model. Sobol and extended Fourier Amplitude Sensitivity Testing (eFAST) methods were used to perform the sensitivity analysis for simulated isothermal loading/unloading at various operating temperatures. It is evident that the model’s prediction of the SMA stress–strain curves varies due to the change in operating temperature and loading condition. The average and stress-dependent sensitivity indices present the most influential parameters at several temperatures.

A shape memory alloy (SMA) can remember its original shape and recover from strain due to loading once it is exposed to heat (shape memory effect). SMAs also exhibit elastic response to applied stress above the characteristic temperature at which transformation to austenite is completed (pseudoelasticity or superelasticity). Shape memory effect and pseudoelasticity of SMAs have been addressed by several microscopic thermodynamic and macroscopic phenomenological models using different modeling approaches. The Tanaka and Liang-Rogers models are two of the most widely used macroscopic phenomenological constitutive models for describing SMA behavior. In this paper, we performed sensitivity and uncertainty analysis using Sobol and extended Fourier Amplitude Sensitivity Testing (eFAST) methods for the Tanaka and Liang-Rogers models at different operating temperatures and loading conditions. The stress-dependent and average sensitivity indices have been analyzed and are presented for determining the most influential parameters for these models. The results show that variability is primarily caused by a change in operating temperature and loading conditions. Both models appear to be influenced by the uncertainty in elastic modulus of the material significantly. The analyses presented in this paper aim to provide a better insight for designing applications using SMAs by increasing the understanding of these models’ sensitivity to the input parameters and the cause of output variability due to uncertainty in the same input parameters.

In this paper, we describe software modules that provide both visual and haptic feedback to the student, and evaluate their effectiveness. The system integrates software modules with a haptic interface that can augment teaching and learning in a required undergraduate engineering Dynamics course. Students can change parameters, predict answers, compare outcomes, interact with animations, and feel the results using a haptic interface. Three software modules were evaluated in two separate studies. The first study focused on subjective ratings based on student opinions. The second study assessed the effect of the modules on students' conceptual understanding for force and motion using a pretest/posttest design. The results revealed that the practice with the modules significantly improved the conceptual understanding of the targeted concepts. In addition, students showed a significant preference by stating that the modules would increase their interest in Dynamics as a subject and their engagement in the Dynamics course.

Brinson model is one of the most widely used shape memory alloy models due to its prediction power over a wide range of operating temperatures and inclusion of measurable engineering variables. The model involves parameters that are determined based on experimental data specific to a particular alloy. Therefore, it is subject to both experimental uncertainty and natural random variability in its parameters that propagate throughout the loading/unloading of the material. In this article, we analyse the sensitivity of the Brinson model to its parameters using a probabilistic approach, and present how the uncertainties in these parameters at different operating temperatures propagate as evidenced by the resulting stress–strain curves. The analyses were performed for isothermal loading/unloading and at various operating temperatures representing possible phase changes between martensite–austenite and martensite–martensite variants. The results show that the sensitivity of the model varies considerably based on the operating temperature and loading conditions. In addition, the variability in the model’s output is amplified after phase transitions during loading, and loading the material above the critical stress for martensite transition reduces variability during unloading. Based on the results of the sensitivity analysis, recommendations as to which parameters affect the variability of the model-predicted stress–strain curves are presented.