This paper proposes an approach to evaluate the performance of robot manipulator from the view of energy analysis.Based on the dynamics analysis of the manipulator,the Energy Distribution Index(EDI)is defined to depict the energy increment contribution of its subsystem to the whole manipulator.EDI is applied to the evaluation of the buffering capability of the manipulator working under unpredictable and heavy external loads.A series of buffering indices,the Static Buffering Index(SBI), Kineto-Static Buffering Index(KBI),Dynamic Buffering Index(DBI),and Global Buffering Index(GBI)are proposed to evaluate the buffering capability under different conditions.In order to acquire higher calculation accuracy,the general stiffness mapping of manipulators considering the actuator stiffness,inertia of the manipulator,damping,as well as elasticity of linkages is developed.Three different robot manipulators are studied as evaluation cases,in which the buffering structures are mechanism with variable topology,linear springs,and the elasticity of linkages respectively.The case studies show that the indices based on energy analysis have the advantage of coordinate free and are effective for buffering capability evaluation.
GLARE(glass fibre/epoxy reinforced aluminum laminate) is a member of the fiber metal laminate(FML) family,and is built up of alternating metal and fiber layers.About 500 m 2 GLARE is employed in each Airbus A380 because of the superior mechanical properties over the monolithic aluminum alloys,such as weight reduction,improved damage tolerance and higher ultimate tensile strength.Many tons of new GLARE scraps have been accumulated during the Airbus A380 manufacturing.Moreover,with the increasing plane orders of Airbus A380,more and more end-of-life(EOL) GLARE scrap will be generated after retire of planes within forty years.Thermal processing is a potential method for the material recycling and re-use from GLARE with the aim of environmental protection and economic benefits.The current study indicatdes that thermal delamination is a crucial pre-treatment step for the GLARE recycling.The decomposition behavior of the epoxy resins at elevated temperatures was investigated by using the simultaneous thermal analysis,thermogravimetry analysis(TGA) and differential scanning calorimetry(DSC).Based on the thermal analysis results,GLARE thermal delamination experiments at refined temperatures were carried out to optimize the treatment temperature and holding time.
Recycling of aluminum alloy scrap obtained from delaminated fibre metal laminates(FMLs) was studied through high temperature refining in the presence of a salt flux.The aluminum alloy scrap contains approximately mass fraction w(Cu) = 4.4%,w(Mg) = 1.1% and w(Mn) = 0.6%(2024 aluminum alloy).The main objective of this research is to obtain a high metal yield,while maintaining its original alloy compositions.The work focuses on the metal yield and quality of recycled Al alloy under different refining conditions.The NaCl-KCl salt system was selected as the major components of flux in the Al alloy recycling.Two different flux compositions were employed at NaCl to KCl mass ratios of 44:56 and 70:30 respectively,based on either the eutectic composition,or the European preference.Different additives were introduced into the NaCl-KCl system to study the effect of flux component on recycling result.Although burning and oxidation loss of the alloying elements during re-melting and refining take place as the drawbacks of conventional refining process,the problems can be solved to a large extent by using an appropriate salt flux.Experimental results indicate that Mg in the alloy gets lost when adding cryolite in the NaCl-KCl salt system,though the metal yield can reach as high as 98%.However,by adding w(MgF 2) = 5% into the NaCl-KCl salt system(instead of using cryolite) all alloying elements were well controlled to its original composition with a metal yield of almost 98%.
Feature extraction from vibration signals has been investigated extensively over the past decades as a key issue in machine condition monitoring and fault diagnosis.Most existing methods,however,assume a linear model of the underlying dynamics.In this study,the feasibility of devoting nonlinear dynamic parameters to characterizing bearing vibrations is studied.Firstly,fuzzy sample entropy (FSampEn) is formulated by defining a fuzzy membership function with clear physical meaning.Secondly,inspired by the multiscale sample entropy (multiscale SampEn) which is originally proposed to quantify the complexity of physiological time series,we placed approximate entropy (ApEn),fuzzy approximate entropy (FApEn) and the proposed FSampEn into the same multiscale framework.This led to the developments of multiscale ApEn,multiscale FApEn and multiscale FSampEn.Finally,all four multiscale entropies along with their single-scale counterparts were employed to extract discriminating features from bearing vibration signals,and their classification performance was evaluated using support vector machines (SVMs).Experimental results demonstrated that all four multiscale entropies outperformed single-scale ones,whilst multiscale FSampEn was superior to other multiscale methods,especially when analyzed signals were contaminated by heavy noise.Comparisons with statistical features in time domain also support the use of multiscale FSampEn.
