An Enhanced Robot Massage System in Smart Homes Using Force Sensing and a Dynamic Movement Primitive

With requirements to improve life quality, smart homes, and healthcare have gradually become a future lifestyle. In particular, service robots with human behavioral sensing for private or personal use in the home have attracted a lot of research attention thanks to their advantages in relieving high labor costs and the fatigue of human assistance. In this paper, a novel force-sensing- and robotic learning algorithm-based teaching interface for robot massaging has been proposed. For the teaching purposes, a human operator physically holds the end-effector of the robot to perform the demonstration. At this stage, the end position data are outputted and sent to be segmented via the Finite Difference (FD) method. A Dynamic Movement Primitive (DMP) is utilized to model and generalize the human-like movements. In order to learn from multiple demonstrations, Dynamic Time Warping (DTW) is used for the preprocessing of the data recorded on the robot platform, and a Gaussian Mixture Model (GMM) is employed for the evaluation of DMP to generate multiple patterns after the completion of the teaching process. After that, a Gaussian Mixture Regression (GMR) algorithm is applied to generate a synthesized trajectory to minimize position errors. Then a hybrid position/force controller is integrated to track the desired trajectory in the task space while considering the safety of human-robot interaction. The validation of our proposed method has been performed and proved by conducting massage tasks on a KUKA LBR iiwa robot platform

Gradient based hyper-parameter optimisation for well conditioned kriging metamodels

In this work a two step approach to efficiently carrying out hyper parameter optimisation, required for building kriging and gradient enhanced kriging metamodels, is presented. The suggested approach makes use of an initial line search along the hyper-diagonal of the design space in order to find a suitable starting point for a subsequent gradient based optimisation algorithm. During the optimisation an upper bound constraint is imposed on the condition number of the correlation matrix in order to keep it from being ill conditioned. Partial derivatives of both the condensed log likelihood function and the condition number are obtained using the adjoint method, the latter has been derived in this work. The approach is tested on a number of analytical examples and comparisons are made to other optimisation approaches. Finally the approach is used to construct metamodels for a finite element model of an aircraft wing box comprising of 126 thickness design variables and is then compared with a sub-set of the other optimisation approaches

A study into the effects of gas flow inlet design of the Renishaw AM250 laser powder bed fusion machine using computational modelling

Previous work has highlighted the importance of the gas flow system in laser powder bed fusion (L-PBF) processes. Inhomogeneous gas flow experienced at the surface of the powder bed can cause variations in mechanical properties over a build platform, where insufficient removal of by-products which cause laser attenuation and redisposition of byproducts are believed to contribute to these variations. The current study analyses the gas flow experienced over a build platform in a Renishaw AM250 metal powder bed fusion machine via Hot Wire Anemometer (HWA) testing. Velocity profiles of the flow directly above the powder-bed and through the centre plane normal to the inlets have been categorized. These HWA results illustrate the inhomogeneity of the gas flow experienced over the build platform and from literature imply that there will be insufficient removal of by-products and hence variable build quality in specific areas of the build platform. A Computational Fluid Dynamics (CFD) model was created in ANSYS Fluent and validated against HWA results coupled with a Discrete Phase Model (DPM) representing the expulsion of spatter. Velocity contours of simulated against experimental are compared, where the results appear in good agreement. The multiphase CFD model was then used to explore the effects of changing inlet design parameters using a Design of Experiments (DOE) study based on an Optimal Space Filling (OSF) method. This was to understand the effect of design parameters on flow uniformity, local gas velocity over the processing area and spatter particulate accumulation within the build chamber. The initial design study found that flow uniformity could potentially be increased by 21.05% and spatter accumulation on the processing area could be reduced by 26.64%. In addition, this has given insight into important design considerations for future generation of L-PBF machines

The development of a sub-atmospheric two-phase thermosyphon natural gas preheater using a lumped capacitance model and comparison with experimental results

The pre-heating of natural gas supplied to both domestic and industrial use is required to counteract the Joule–Thomson effect due to pressure reduction. Most existing pre-heaters are in the form of water bath heaters, where both the burner and exchanger are immersed in a closed water tank. These systems usually have a low efficiency, and as a result of thermal inertia have a long time lag to accommodate changes in Natural Gas (NG) mass flow rates. In this paper, the two-phase thermosyphon theory is implemented in a sub-atmospheric context to design and study a new preheating system in a transient fashion. This system is partially vacuumed (absolute pressure of 2 kPa) to lower the temperature operation range to reduce the required working fluid volume, hence reduce the required energy and improve the response time. The transient numerical model is based on a lumped capacitance method, and the full system is solved by using a fourth order Runge–Kutta method. The numerical model is validated through comparison with experimental results. Minimum efficiency of 68% has been achieved in some tests, whilst maximum efficiency of 80% in other tests. Simulations of the thermosyphon preheater system have been performed to analyse the effect of changing the working fluid volume and composition

Development of a Neural Network-Based Control System for the DLR-HIT II Robot Hand Using Leap Motion

In this paper, a neural network (NN) based adaptive controller has been successfully developed for the teleoperation of a DLR-HIT II robot hand using Leap Motion sensor. To achieve this, the coordinate positions of the human hand fingers are captured by a Leap Motion sensor. Moreover, inverse kinematics is used to transform the Cartesian position data of the fingertips into corresponding joint angles of all the five fingers, which are then directly sent to teleoperate the DLR-HIT II hand via User Datagram Protocol (UDP). In addition, a NN-based control system programmed by the MATLAB Simulink function has been investigated to enhance the overall control performance by compensating the dynamic uncertainties existing in the teleoperation system. The stability of the control system has been proved mathematically using Lyapunov stability equations. A series of experiments have been conducted to test the performance of the proposed control technique, which has been proved to be an effective teleoperation strategy for the DLR-HIT II robot hand

GE Jet Engine Bracket Challenge: A Case Study in Sustainable Design

Open crowdsourcing competitions can provide a large repository of data which can be used to achieve more sustainable product designs. This study looks at the recent General Electric challenge, a competition to minimize the mass of a titanium jet engine lifting bracket, to illustrate the benefits that can be accrued. In the light of current literature the benefits and challenges of crowdsourcing have been considered. Samples of the entrants to the challenge have been compared to identify critical characteristics for interpreting sustainable designs for additive manufacture. Focusing initially on topological optimisation and orientation of the additive manufacture build, critical features have been highlighted. The availability of many CAD designs has been most useful and has potential for future developments. Crowdsourcing as an innovation approach can also be beneficial for both companies and individuals particularly if the entries are open sourc

Computational Methodology for Optimal Design of Additive Layer Manufactured Turbine Bracket

The design of critical components for aircrafts, cars or any other kind of machinery today is typically subject to two conflicting objectives, namely the maximisation of strength and the minimisation of weight. The conflicting nature of these two objectives makes it impossible to obtain a design that is optimal for both. The most common approach aiming for a single objective optimisation problem in aerospace is to maintain the weight minimisation as the objective, whilst setting strength requirements as constraints to be satisfied. However, manufacturing methods incorporate additional restrictions for an optimal design to be considered feasible, even when satisfying all constraints in the formulation of the optimisation problem. In this context, Additive Layer Manufacturing adds remarkably higher flexibility to the manufacturability of shape designs when compared with traditional processes. It is fair to note, however, that there are still some restrictions such as the infeasibility of building unsupported layers forming angles smaller than 45 degrees with respect to the underlying one. Nowadays, it is common practice to use a set of software tools to deal with these kinds of problems, namely Computer Aided Design (CAD), Finite Element Analysis (FEA), and optimisation packages. The adequate use of these tools results in an increase in efficiency and quality of the final product. In this paper, a case study was undertaken consisting of a turbine bracket from a General Electric challenge. A computational methodology is used, which consists of a topology optimisation considering an isotropic material at first instance, followed by the manual refinement of the resulting shape taking into account the manufacturability requirements. To this end, we used SolidWorks®2013 for the CAD, Ansys Workbench®14.0 for the FEA, and HyperWorks®11 for the topology optimisation. A future methodology will incorporate the automation of the shape optimisation stage, and perhaps the inclusion of the manufacturability restriction within the optimisation formulation

The three-prong method: a novel assessment of residual stress in laser powder bed fusion

Residual stress is a major problem for most metal-based laser powder bed fusion (L-PBF) components. Residual stress can be reduced by appropriate build planning and post-process heat treatments; however, it is not always avoidable and can lead to build failures due to distortion and cracking. Accurate measurement of residual stress levels can be difficult due to high equipment set-up costs and long processing times. This paper introduces a simple but novel method of measuring residual stresses via a three-pronged cantilever component, the three-prong method (TPM). The method allows for a quick and easy characterisation of residual stress for a wide range of machine parameters, build strategies and materials. Many different cantilever designs have been used to indicate residual stress within additive manufacturing techniques. All of which share the same shortcoming that they indicate stress in one direction. If the principal component of stress is not aligned with the beam geometry, it will underestimate peak stress values. A novel three-prong design is proposed which covers two dimensions by utilising three adjoined cantilever beams, a configuration which echoes that of hole-drilling where three measurements are used to calculate the stress field around a drilled hole. Each arm of the component resembles a curved bridge-like structure; one end of each bridge is cut away from the base plate leaving the centre intact. Deformation of the beams is then measured using a co-ordinate measurement machine. Stress profiles are then estimated using finite element analysis by meshing the deflected structure and forcing it back to its original shape. In this paper, the new TPM is used to compare the residual stress levels of components built in Ti–6Al–4V with different hatch patterns, powers and exposure times

Numerical Investigation into Dynamic Loading of Rubber Compound

The present paper analyses the heat generation build(up in silicone rubber samples when subjected to dynamic cyclic loading. Material properties of the rubber were determined through thermal and mechanical experimental testing. These properties are necessary to set up the computational model. The model includes a fully coupled transient nonlinear thermo(mechanical finite element analysis. In order to validate this approach, numerical results are compared with those gathered experimentally. The numerical model developed and validated could be used to simulate various industrial applications, involving rubber parts, for efficient and sustainable desig