Energy harvesting for wearable applications

Energy harvesting, the process of collecting low level ambient energy and converting it into electrical energy, is a promising approach to power wearable devices. By converting the energy of the human body by using piezoelectric and thermoelectric principles, the need for batteries and charging can be avoided, and the autonomy of wearable devices can be significantly increased. Due to the inherent random nature of human motion, however, the energy harvesting devices need to be specifically designed in order to ensure their optimal operation and sufficient power generation. Using several combined approaches, a new class of autonomous devices, suitable for telemedicine, patient monitoring or IoT applications, can be developed

Influence of design parameters on the behaviour of cross-spring pivots

Compliant mechanisms gain at least part of their mobility from the deflection of the flexible member. They are characterised by high precision, possibility of monolithic manufacturing, as well as the absence of backlash and wear. Numerical methods are used in this work to characterise the behaviour of compliant rotational mechanisms, known as cross-spring pivots, aimed at ultrahigh-precision positioning applications. The results obtained by using nonlinear finite element calculations are compared with experimental data reported in literature. The finite element model developed in this way makes it possible to conider the influence of lateral loads and of non-symmetrical pivot configurations where the angle or point of intersection of the leaf springs can be varied. This allows assessing the influence of the cited design parameters on the minimisation of the parasitic shifts of the geometric centre of the pivot, as well as on the minimisation of the variability of the rotational stiffness of the pivot while ensuring its stability. The obtained results allow determining design solutions applicable in ultrahigh-precision positioning applications, e.g. in the production or in handling and assembly of MEMS devices

Influence of design parameters on the behaviour of cross-spring pivots

Compliant mechanisms gain at least part of their mobility from the deflection of the flexible member. They are characterised by high precision, possibility of monolithic manufacturing, as well as the absence of backlash and wear. Numerical methods are used in this work to characterise the behaviour of compliant rotational mechanisms, known as cross-spring pivots, aimed at ultrahigh-precision positioning applications. The results obtained by using nonlinear finite element calculations are compared with experimental data reported in literature. The finite element model developed in this way makes it possible to conider the influence of lateral loads and of non-symmetrical pivot configurations where the angle or point of intersection of the leaf springs can be varied. This allows assessing the influence of the cited design parameters on the minimisation of the parasitic shifts of the geometric centre of the pivot, as well as on the minimisation of the variability of the rotational stiffness of the pivot while ensuring its stability. The obtained results allow determining design solutions applicable in ultrahigh-precision positioning applications, e.g. in the production or in handling and assembly of MEMS devices

Issues in the mechanical engineering design of high-precision kinematic couplings

A three V-groove (Maxwell-type) kinematic mount design configuration constrains all degrees of freedom of the apparatus mounted onto it, thus allowing its high-precision positioning and re-positioning. The analysis of this mechanical assembly comprises force and moment balances, as well as expressions for stress-strain and error motion calculations. For determined loading conditions and the geometry of the mount, the resulting loads across each groove-ball interface imply, however, the necessity to consider the complex nonlinear Hertz theory of point contacts between elastically deforming solids. The available analytical approaches to the calculation of the conditions at the ball-V groove contacts are hence recalled in this work with the aim of establishing the respective limits of applicability. The obtained results are validated experimentally. A structured calculation procedure is then used to assess the stability of a kinematic mount employed to support a large mechanical component at a particle accelerator facility, depending on the value and orientation of the external loads acting on the studied assembly. Stability conditions for different design configurations are consecutively established

Characterization of influential parameters on friction in the nanometric domain using experimental and machine learning methods

Friction is a ubiquitous phenomenon of great research interest in engineering practice. Fundamental frictional features of two solids in contact and in relative motion are governed by microscopic single asperity contacts at their interface. A structured multidisciplinary approach to the experimental determination of friction in the nanometric domain is presented in this work. The dependence of nanoscale friction on process parameters comprising the materials in relative motion, normal forces, sliding velocities and the temperature conditions is studied experimentally by employing scanning probe microscopy. The data hence attained from multidimensional experimental measurements on thin-film samples is used for the development of machine learning-based models. In fact, due to the stochastic nature of the considered phenomena, conventional regression methods yield poor predictive performances, prompting thus the usage of the machine learning numerical paradigm. Such an approach enables obtaining an insight into the concurrent influence of the process parameters on nanoscale friction. A comparative study allows thus showing that, while the best typical regression models result in coefficients of determination (R2) of the order of 0.3, the predictive performances of the used machine learning models, depending on the considered sample, yield R2 in the range from 0.54 to 0.9. The proposed method, aimed at accomplishing an in-depth insight into the physical phenomena influencing nanoscale frictional interactions, will be complemented next with advanced studies based on genetic programming-based artificial intelligence methods. These could, in fact, allow obtaining a functional description of the dependence of nanoscale friction on the studied variable parameters, thus enabling not only true nanoscale friction prediction but also an important tool for control purposes

Characterization of influential parameters on friction in the nanometric domain using experimental and machine learning methods

Friction is a ubiquitous phenomenon of great research interest in engineering practice. Fundamental frictional features of two solids in contact and in relative motion are governed by microscopic single asperity contacts at their interface. A structured multidisciplinary approach to the experimental determination of friction in the nanometric domain is presented in this work. The dependence of nanoscale friction on process parameters comprising the materials in relative motion, normal forces, sliding velocities and the temperature conditions is studied experimentally by employing scanning probe microscopy. The data hence attained from multidimensional experimental measurements on thin-film samples is used for the development of machine learning-based models. In fact, due to the stochastic nature of the considered phenomena, conventional regression methods yield poor predictive performances, prompting thus the usage of the machine learning numerical paradigm. Such an approach enables obtaining an insight into the concurrent influence of the process parameters on nanoscale friction. A comparative study allows thus showing that, while the best typical regression models result in coefficients of determination (R2) of the order of 0.3, the predictive performances of the used machine learning models, depending on the considered sample, yield R2 in the range from 0.54 to 0.9. The proposed method, aimed at accomplishing an in-depth insight into the physical phenomena influencing nanoscale frictional interactions, will be complemented next with advanced studies based on genetic programming-based artificial intelligence methods. These could, in fact, allow obtaining a functional description of the dependence of nanoscale friction on the studied variable parameters, thus enabling not only true nanoscale friction prediction but also an important tool for control purposes