Multiple Performance Indices Research Articles

Advances in machine technology and improvements in Al-SiCp nano composite materials by using multi-layer coated carbide tool

The current research paper deals with the advancement of metal matrix aluminium (Al) material and reinforcement of silicon carbide (SiCp) nano material to form (Al-SiCp) nano composite. The fabrication process was completed by mixing of 1, 1.5 and 2 wt% of Silicon carbide with liquid aluminium metal. From the experimental study, it was observed that 2 wt% of SiCp when mixed with aluminium metal matrix gives the best sample results for Al-SiCp nano composite as well as it is suitable for machining process. In the current work, the machining of nano composite was carried out by using CNC lathe with multilayer coated carbide cutting insert. Three process parameters i.e. feed, depth of cut and speed at four various levels was experimentally varied by using Taguchi L 16 orthogonal array. Principal component Analysis (PCA) was used to optimized the output parameters of surface roughness of the work piece material and the tool flank wear. The optical microscopy study was conducted to find out the heat affected zone and wear of the cutting tool. Analysis of Variance (ANOVA) was also applied to determine the most influencing parameter affecting the Multiple Performance Index (MPI). Different plots like main effect plot, interaction plot, surface plot, contour plot, normal probability plot and distribution fit plot were analyzed to confirm the stability of the model.

ON THE DESIGN OF COATING SYSTEM USING NANOMECHANICAL EXPERIMENTS

The aim of this paper is to identify performance indices for the design of coating systems, using results from nanomechanical tests. The use of nanoindentation, nanoscratch and pin-on-disk experiments applied to coatings for mechanical and tribological property estimation is presented. Then, multiple performance indices, based on experimental and numerical results found in the litterature, are listed in order to help the material selection and optimization of durability and performance of coatings.

Multi-objective global optimum design of collaborative robots

Optimum design is proven significant for improving task performances of robotic manipulators under certain constraints. However, when it is utilized for collaborative robots (Cobots), there are still many challenges such as complex smooth surface links, time-varying kinematic configurations, computational expensiveness, and nonstructural parameter optimization. Therefore, based on orthogonal design experiment (ODE) and finite element substructure method (FESM), a multi-objective optimum design method of Cobots is proposed with the structural dimensions and parameterized joint components as the optimization variables and the natural frequency, the Cartesian stiffness, and the mass of the robot as optimization objectives. Firstly, to obtain multiple global performance indexes (GPIs) of robots in real-time and efficiently, the FESM model of Cobots is established which can preserve the accuracy of the finite element method (FEM) while ensuring the computational efficiency. Then, the gray relational analysis method (GRAM) is used to construct the multi-objective optimization function which includes the global first-order natural frequency index (GFNFI), the global elastic deformation index (GEDI), and the mass of robots. The ODE is constructed, and the structural dimensions and parameterized joint components are taken as influencing factors. According to the orthogonal array (OA), the degree of gray incidence under different levels of influencing factors is solved. And the optimal combination of influencing factor levels is obtained by range analysis (RA), which is used to guide the design of Cobots. Finally, a Cobot SHIR5-I is taken as an illustrative example to perform optimum design in this paper.

Using multiple performance index for best-fitted design of computer numerical control turning process

ABSTRACT Typically, the turning process to be executed using turning machine tools is designed by experienced part programmers; however, the cutting efficiency and trade-off of the dimensional accuracy in the machining process present issues that may be difficult to deal with. Therefore, this study aims at systematically realizing a best-fitted design for the computer numerical control (CNC) turning process by referring to multiple performance indices, such as machining time, machining energy, surface roughness, and radius error. First, the turning process notation and performance evaluation index are defined according to the common side-change process, tool-change process, machining process, and machining results of the turning process. Next, the turning process map is established by the defined turning process notation; here, the comparison table of the total performance index (PI) consisting of the machining time, machining energy, surface roughness, and radius error is calculated and established. Finally, the best-fitted turning process satisfying the cutting efficiency requirement or dimensional accuracy requirement is selected according to the PI comparison table. The results obtained from the experiments performed on a CNC turning machine tool demonstrate that the best-fitted turning process can shorten the machining time by 39.04% (estimated machining time) and 34.24% (actual machining time).

Parametric control to a type of descriptor quasi‐linear systems based on dynamic compensator and multi‐objective optimisation

This study investigates the parametric approach for a type of descriptor quasi-linear systems by utilising dynamic compensator and multi-objective optimisation. Based on the solutions of generalised Sylvester matrix equation, the generally parameterised expressions of dynamic compensator and the left and right eigenvector matrices are both established, meanwhile, a group of arbitrary parameters are obtained. With the parametric approach, the closed-loop system can be transformed into a linear time-invariant one with an expected eigenstructure by using a group of canonical matrix pairs. Simultaneously, it also presents a novel technique to design multi-objective optimisation for descriptor quasi-linear systems. Multiple performance indexes such as low sensitivity, disturbance attenuation, robustness degree, and low gains are formulated by arbitrary parameters. Based on the above indexes, robustness criteria and low gain criteria can be expressed by a synthetic objective function which includes each performance index weighted. By utilising the degrees of freedom in arbitrary parameters, a dynamic compensator can be obtained by solving a multi-objective optimisation problem. Finally, two examples are proposed to prove that the parametric approach is effective.

Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework.

ObjectiveTuberculosis (TB) is a major public health problem in China, and contriving a long-term forecast is a useful aid for better launching prevention initiatives. Regrettably, such a forecasting method with robust and accurate performance is still lacking. Here, we aim to investigate its potential of the error-trend-seasonal (ETS) framework through a series of comparative experiments to analyze and forecast its secular epidemic seasonality and trends of TB incidence in China.MethodsWe collected the TB incidence data from January 1997 to August 2019, and then partitioning the data into eight different training and testing subsamples. Thereafter, we constructed the ETS and seasonal autoregressive integrated moving average (SARIMA) models based on the training subsamples, and multiple performance indices including the mean absolute deviation, mean absolute percentage error, root-mean-squared error, and mean error rate were adopted to assess their simulation and projection effects.ResultsIn the light of the above performance measures, the ETS models provided a pronounced improvement for the long-term seasonality and trend forecasting in TB incidence rate over the SARIMA models, be it in various training or testing subsets apart from the 48-step ahead forecasting. The descriptive results to the data revealed that TB incidence showed notable seasonal characteristics with predominant peaks of spring and early summer and began to be plunging at on average 3.722% per year since 2008. However, this rate reduced to 2.613% per year since 2015 and furthermore such a trend would be predicted to continue in years ahead.ConclusionThe ETS framework has the ability to conduct long-term forecasting for TB incidence, which may be beneficial for the long-term planning of the TB prevention and control. Additionally, considering the predicted dropping rate of TB morbidity, more particular strategies should be formulated to dramatically accelerate progress towards the goals of the End TB Strategy.

Optimal control of discrete-time switched linear systems

This paper considers the optimal control problems of switched autonomous linear systems and switched non-autonomous linear systems in discrete-time, where the objective is that of minimizing a linear quadratic performance index function or multiple linear quadratic performance index function. The subsystems switching sequence and the number of switchings are free in this paper. By employing an embedding technique, we embed the origin switched autonomous or non-autonomous linear systems into a larger family of systems. Owing to the embedding technique, the optimal control problem of the origin switched linear systems can be relaxed into a traditional optimal control problem for the larger family systems. We thus derive the optimal switching control law and the optimal control input for the bang-bang type optimal solution of the relaxed optimal control problem simultaneously. The optimal solution of the origin switched optimal control problem can be obtained by the bang-bang type optimal solution of the relaxed optimal control problem. Finally, results of numerical simulation examples are given to demonstrate the remarkable effectiveness and superiority of the developed method.

Study of Microstructure and Properties of 316L with Selective Laser Melting Based on Multivariate Interaction Influence

The selective laser melting technique is widely used in aerospace and biomedical industries, and the performance of formed 316L parts is significantly subject to the forming angle. As the selective laser melting 316L parts are constrained by multiple performance indexes, the study involves multivariate interaction influenced on the forming parameters such as the angle with the xz plane, the angle with the xy plane, laser power, scan speed, powder thickness, and hatching space on the indexes like tensile strength, density, and surface roughness with linear regression equations based on multiobjective optimization to obtain the best process parameters. The study results of microstructure performance of the formed 316L parts show that the angle with the xz plane has significant effect on the experiment indexes, while the layer thickness has the greatest effect on the indexes. After stretching, the molten pools are obviously elongated and the microstructure of the formed 316L parts is composed of equiaxed crystals and columnar crystals with a grain width of 0.28–0.4 nm. The secondary growth of the dendrites is not obvious, and the crystallinity of the selective laser melting 316L parts is not as good as the standard parts, with the microstructure showing directional solidification due to grain refinement and microscopic distortion of crystals. As the fracture has dimples, it is a ductile fracture and typical plastic fracture. The hardness near the fracture is higher than that of the substrate, whilst the indexes regarding the selective laser melting parts are higher than the ASTM‐A182 and ASTM‐F3184‐16 standards. Since the theoretical model built in this study has less error, the findings have practical engineering application value.

Analysis of Al-SiCp nano composite and study of its machining process by using coated carbide tool

The present study deals with aluminium and silicon carbide (Al-SiCp) metal matrix nano composite material. In this process, silicon carbide nano particles of weight fraction 1%, 1.5% and 2% weight are mixed with aluminium molten metal. Optical microscopy study is conducted for all the samples and it is found that there is some formation of minute cracks only for 2 wt% sample which might be due to the generation of high heat and temperature. Later, the machining of Al-SiCp workpiece has been carried out by CNC lathe using coated carbide insert. The design of experiment has been obtained by Taguchi L16 orthogonal array at three different factors (feed, speed and depth of cut) at four different levels. The response parameters i.e. flank wear of coated carbide tool and surface roughness of the Al-SiCp workpiece has been optimized by using Principal Component Analysis. After the machining operation, heat affected zone, tool wear, burr and damaged zone of the tool can be observed under Scanning Electron Microscope. Analysis of Variance confirms feed, depth of cut and cutting speed to be the most significant parameters affecting the Multiple Performance Index.

Non-Probabilistic Robust Equilibrium Optimization of Complex Uncertain Structures

Abstract Robust optimization of complex uncertain structures usually involves multiple conflicting and competing structural performance indices. Present approaches for achieving the final design of such an optimization problem always involve a decision-making process, which is a demanding task that requires the rich experience and expert skills of designers. To overcome the difficulty, an interval robust equilibrium optimization approach is proposed to find the optimal design of complex uncertain structure based on the robust equilibrium strategy for multiple conflicting and competing structural performance indices. Specifically, a new concept of closeness and crossing coefficient between interval boundaries (CCCIBs) is proposed at first, based on which the tri-dimensional violation vectors of all interval constraints can be calculated and the feasibility of a design vector can be assessed. Then, the robust equilibrium assessment of multiple objective and constraint performance indices is investigated, based on the results of which the feasible design vectors can be directly ranked according to the robust equilibrium strategy for all structural performance indices. Subsequently, the algorithm for the robust equilibrium optimization of complex uncertain structures is developed by integrating the Kriging technique and nested genetic algorithm. The validity, effectiveness, and practicability of the proposed approach are demonstrated by two illustrative examples.

Parametric control to second-order linear time-varying systems based on dynamic compensator and multi-objective optimization

This paper investigates the parametric design approach to second-order linear time-varying systems by using dynamic compensator and multi-objective optimization. On the basis of the solution to a type of second-order generalized Sylvester matrix equations, the generally completely parameterized expression of the dynamic compensator is established, meanwhile, the completely parametric forms of left and right eigenvectors are obtained, it also provides two groups of arbitrary parameters. With the parametric method, the closed-loop system can be converted into a linear constant one with desired eigenstructure. Simultaneously, it also considers a novel technique to multi-objective optimization. Multiple performance indexes such as regional pole assignment, low sensitivity, disturbance attenuation, robustness degree and low gains are formulated by arbitrary parameters. Based on the above indexes, a synthetic objective function which includes each performance index weighted is formulated to express the comprehensive performances of control system. By using the degrees of freedom in arbitrary parameters, a dynamic compensator can be established by solving a multi-objective optimization problem. Finally, an example of spacecraft rendezvous problem is presented to verify that the parametric approach is effective.

Integrated Vehicle Dynamics System through Coordinating Active Aerodynamics Control, Active Rear Steering, Torque Vectoring and Hydraulically Interconnected Suspension

This paper investigates integrated vehicle dynamics control through coordinating active aerodynamics control, active rear steering, torque vectoring and hydraulically interconnected suspension for improving the overall vehicle performance including handling, stability, and comfort. After developing each chassis control system, it is tested by various manoeuvres in order to assess each subsystem. Then, a rule-based coordinate system is proposed for integrated control of the four chassis control systems. Simulation investigation is performed to display the effectiveness of the proposed integrated vehicle dynamics system. Results demonstrate that the proposed control scheme is able to enhance the multiple performance indices of the vehicle including both the ride comfort, and the lateral stability, compared to the non-integrated control system.

Cooperative Optimal Controller and Its Application to Activated Sludge Process.

With the increasing complexity and scale of activated sludge process (ASP), it is quite challenging to coordinate the performance indices with different time scales. To address this problem, a cooperative optimal controller (COC) is proposed to improve the operation performance in this paper. First, a cooperative optimal scheme is developed for designing the control system, where the different time-scale performance indices are formulated by two levels. Second, a data-driven surrogate-assisted optimization (DDSAO) algorithm is provided to optimize the cooperative objectives, where a surrogate model is established for evaluating the feasibility of optimal solutions based on the minimum squared error. Third, an adaptive predictive control strategy is investigated to derive the control laws for improving the tracking control performance. Finally, the proposed COC is tested on benchmark simulation model No. 1 (BSM1). The results demonstrate that the proposed COC is able to coordinate the multiple time-scale performance indices and achieve the competitive optimal control performance.

Formation flight design for a LISA-like gravitational wave observatory via Cascade optimization

Laser Interferometer Space Antenna (LISA) is a project to detect and measure gravitational waves. The project has three spacecraft flying in a formation of near equilateral triangle in a heliocentric orbit trailing Earth. Many sources of perturbations cause the configuration to deviate from the nominal. This paper studies the formation design problem for a LISA-like mission by considering ephemeris-based dynamics. This type of mission is well-known for addressing several strict mission requirements under the realistic dynamics. The problem is formulated as optimizing multiple mission performance indices. It is observed that some indices are correlated with each other, whereas some indices have different sensitivities with respect to the semi-major axis. Therefore, the problem is transformed into a two-step cascade single-objective optimization, in which the optimal solution of the first optimization problem is fed to the second optimization as initial value. In addition, the major perturbing celestial bodies are picked up to make a simplified but accurate enough dynamics to speed up the optimization. Numerical examples verify the analysis and show the effectiveness of the optimization procedure. The influences of mission lifetime and spatial scales on the optimal solutions are also presented.

Determining suitable region wind speed probability distribution using optimal score-radar map

Determination of wind speed probability distribution is of considerable importance to the accurate wind energy assessment. This paper proposes an optimal score-radar map for determining the suitable region wind speed probability distribution from various candidate parametric distributions. Five typical fitting performance indices are calculated and transformed into standard scores. Optimal weight coefficients are obtained to consolidate the five standard scores into an integrated score, which minimizes the differences between multiple fitting performance indices. The optimal score-radar map consists of integrated scores visually presents the suitability of candidate parametric distribution to numerous wind farms, which enables easy, quick, and explicit determination of suitable candidate. The applicability, efficiency, and verification of the proposed map are demonstrated using ten years of collected wind speed data form sixty-six wind farms at North Dakota. The results show that the mixture Generalized Extreme Value-Weibull distribution has the minimum area of optimal score-radar map and presents satisfactory suitability to North Dakota. Also, such distribution accurately characterizes the mean value, standard deviation, and skewness of collected wind speed data. The wind energy of North Dakota is investigated using such distribution. An ideal wind farm and preferable wind turbine are found.

Automated Recognition of Epileptic EEG States Using a Combination of Symlet Wavelet Processing, Gradient Boosting Machine, and Grid Search Optimizer.

Automatic recognition methods for non-stationary electroencephalogram (EEG) data collected from EEG sensors play an essential role in neurological detection. The integrated approaches proposed in this study consist of Symlet wavelet processing, a gradient boosting machine, and a grid search optimizer for a three-class classification scheme for normal subjects, intermittent epilepsy, and continuous epilepsy. Fourth-order Symlet wavelets are adopted to decompose the EEG data into five frequencies sub-bands, such as gamma, beta, alpha, theta, and delta, whose statistical features were computed and used as classification features. The grid search optimizer is used to automatically find the optimal parameters for training the classifier. The classification accuracy of the gradient boosting machine was compared with that of a conventional support vector machine and a random forest classifier constructed according to previous descriptions. Multiple performance indices were used to evaluate the proposed classification scheme, which provided better classification accuracy and detection effectiveness than has been recently reported in other studies on three-class classification of EEG data.

Parametric Control to Second-Order Quasi-Linear Systems Based on Dynamic Compensator and Multi-Objective Optimization

This paper presents the parametric design approach to the second-order quasi-linear (SQ) systems by using dynamic compensator and multi-objective optimization. Based on the solutions to a type of the second-order generalized Sylvester equations (SGSEs), the generally parametric form of the dynamic compensator is established, the completely parameterized expressions of left and right eigenvector matrices are obtained, and it also provides two groups of arbitrary parameter matrices. With the parametrized method, the closed-loop system is converted into a linear constant one. Simultaneously, it also investigates a novel technique to multi-objective design and optimization. Multiple performance indexes, such as regional pole assignment, low sensitivity, disturbance attenuation, robustness degree, and low gains, are formulated by arbitrary parameters. Based on the above indexes, robustness criteria and low gain criteria can be expressed by a synthetic objective function which includes each performance index weighted. By using the degrees of freedom (DOFs) in arbitrary parameters, a dynamic compensator can be established by solving a multi-objective optimization problem. Finally, the spacecraft rendezvous problem is proposed to verify the feasibility and effectiveness of the parametrized approach.

Integrated assessment of a sustainable microgrid for a remote village in hilly region

The complexity in the design of sustainable microgrids based on renewable energy sources have increased recently due to the involvement of multiple performance indices, scenarios, and stakeholders. At disintegrated level such as rural villages in developing nations, the issue has become more severe concerning the involvement of social and cultural characteristics making the problem multidimensional with multiple objectives. Thus, a proper method which can simultaneously consider the various criteria and differing views of the multiple actors involved in varying scenarios is required. In this paper, a bi-level framework based on the integration of decision making and optimization tool for the design of a rural microgrid with a perspective of sustainable development considering the annual load growth is presented. The first design level utilizes the decision analysis models to determine the best possible energy alternatives for the microgrid design considering the number of key performance indices formulating different scenarios and acts as input to the second level of the framework. A multi-objective computational optimization tool is utilized in the second level to carry out the feasibility analysis of best energy alternative in order to obtain an optimal size with a lower cost accounting yearly load growth. The proposed framework is then implemented to design a sustainable microgrid for an unelectrified village in the hilly region. Most importantly the microgrid design based on the proposed methodology for the target site utilizes the available local resources such as solar, hydro with the dam, hydrokinetics based on river water streams, etc. as the primary energy generating sources with a diesel generator as back up along with battery and pump-hydro storage as storage systems. A total of 23 alternatives depending upon the different combination of energy sources and storage is constituted in a centralized off-grid system. Moreover, four primary performance criteria (social, technical, economic and environment) with 20 sub-criteria in 29 different scenarios are considered for evaluating the microgrid which is highly unlikely to be observed in literature so far. A discursive result illustrating the various profiles (generation and storage system) of the microgrid based on daily, monthly and yearly basis accounting the load growth is also presented.

Research of Forward Design Method of Contact Dynamic Seal

The development of forward design of contact dynamic seals is an inevitable way to realize the independent development of core key components based on the research results of existing rubber and plastic seals. This paper proposes a multi-index comprehensive evaluation system for sealing performance. Based on the requirements of multiple performance indexes of dynamic sealing systems, a comprehensive evaluation of multiple index functions is constructed to provide a comprehensive evaluation of the performance of rubber and plastic seals. Based on the specific design examples of air contact seals, the design process of a certain seal is explored. Finally, the article elaborates on the future work of the forward design method and its development direction.

Failure tolerance strategy of space manipulator for large load carrying tasks

During the execution of large load carrying tasks in long term service, there is a notable risk of space manipulator suffering from locked-joint failure, thus space manipulator should be with enough failure tolerance performance. A research on evaluating failure tolerance performance and re-planning feasible task trajectory for space manipulator performing large load carrying tasks is conducted in this paper. The effects of locked-joint failure on critical performance(reachability and load carrying capacity) of space manipulator are analyzed at first. According to the requirements of load carrying tasks, we further propose a new concept of failure tolerance workspace with load carrying capacity(FTWLCC) to evaluate failure tolerance performance, and improve the classic A* algorithm to search the feasible task trajectory. Through the normalized FTWLCC and the improved A* algorithm, the reachability and load carrying capacity of the degraded space manipulator are evaluated, and the reachable and capable trajectory can be obtained. The establishment of FTWLCC provides a novel idea that combines mathematical statistics with failure tolerance performance to illustrate the distribution of load carrying capacity in three-dimensional space, so multiple performance indices can be analyzed simultaneously and visually. And the full consideration of all possible failure situations and motion states makes FTWLCC and improved A* algorithm be universal and effective enough to be appropriate for random joint failure and variety of requirement of large load carrying tasks, so they can be extended to other types of manipulators.