scholarly journals Multi-Objective Defocus Robust Source and Mask Optimization Using Sensitive Penalty

2019 ◽  
Vol 9 (10) ◽  
pp. 2151
Author(s):  
Pengzhi Wei ◽  
Yanqiu Li ◽  
Tie Li ◽  
Naiyuan Sheng ◽  
Enze Li ◽  
...  
Keyword(s):  
Gradient Descent ◽  
Process Window ◽  
Sensitivity Factor ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Lithography Process ◽  
Wide Range ◽  
Focus Plane ◽  
Technology Nodes ◽  
First Time

The continuous decrease in the size of lithographic technology nodes has led to the development of source and mask optimization (SMO) and also to the control of defocus becoming stringent in the actual lithography process. Due to multi-factor impact, defocusing is always changeable and uncertain in the real exposure process. But conventional SMO assumes the lithography system is ideal, which only compensates the optical proximity effect (OPE) in the best focus plane. Therefore, to solve the inverse lithography problem with more uniformity of pattern in different defocus variations, we proposed a defocus robust SMO (DRSMO) approach that is driven by a defocus sensitivity penalty function for the first time. This multi-objective optimization samples a wide range of defocus disturbances and it can be proceeded by the mini-batch gradient descent (MBGD) algorithm effectively. The simulation results showed that a more robust defocus source and mask can be designed through DRSMO optimization. The defocus sensitivity factor sβ maximally decreased 63.5% compared to conventional SMO, and due to the low error sensitivity and the depth of defocus (DOF), the process window (PW) was further enlarged effectively. Compared to conventional SMO, the exposure latitude (EL) maximally increased from 4.5% to 10.5% and DOF maximally increased 54.5% (EL = 5%), which proved the validity of the DRSMO method in improving the focusing performance.

Integrated Design and Multi-Objective Optimization of a Single Stage Heat-Pump Turbocompressor

2013 ◽  
Author(s):  
J. Schiffmann
Keyword(s):  
Design Optimization ◽  
High Efficiency ◽  
Integrated Design ◽  
Heat Pumps ◽  
Small Scale ◽  
Multi Objective Optimization ◽  
Design Constraints ◽  
Multi Objective ◽  
Wide Range ◽  
Standard Design

Small scale turbomachines in domestic heat pumps reach high efficiency and provide oil-free solutions which improve heat-exchanger performance and offer major advantages in the design of advanced thermodynamic cycles. An appropriate turbocompressor for domestic air based heat pumps requires the ability to operate on a wide range of inlet pressure, pressure ratios and mass flows, confronting the designer with the necessity to compromise between range and efficiency. Further the design of small-scale direct driven turbomachines is a complex and interdisciplinary task. Textbook design procedures propose to split such systems into subcomponents and to design and optimize each element individually. This common procedure, however, tends to neglect the interactions between the different components leading to suboptimal solutions. The authors propose an approach based on the integrated philosophy for designing and optimizing gas bearing supported, direct driven turbocompressors for applications with challenging requirements with regards to operation range and efficiency. Using previously validated reduced order models for the different components an integrated model of the compressor is implemented and the optimum system found via multi-objective optimization. It is shown that compared to standard design procedure the integrated approach yields an increase of the seasonal compressor efficiency of more than 12 points. Further a design optimization based sensitivity analysis allows to investigate the influence of design constraints determined prior to optimization such as impeller surface roughness, rotor material and impeller force. A relaxation of these constrains yields additional room for improvement. Reduced impeller force improves efficiency due to a smaller thrust bearing mainly, whereas a lighter rotor material improves rotordynamic performance. A hydraulically smoother impeller surface improves the overall efficiency considerably by reducing aerodynamic losses. A combination of the relaxation of the 3 design constraints yields an additional improvement of 6 points compared to the original optimization process. The integrated design and optimization procedure implemented in the case of a complex design problem thus clearly shows its advantages compared to traditional design methods by allowing a truly exhaustive search for optimum solutions throughout the complete design space. It can be used for both design optimization and for design analysis.

Investigation, sensitivity analysis, and multi-objective optimization of effective parameters on temperature and force in robotic drilling cortical bone

2017 ◽  
Vol 231 (11) ◽  
pp. 1012-1024 ◽  
Author(s):  
Vahid Tahmasbi ◽  
Majid Ghoreishi ◽  
Mojtaba Zolfaghari
Keyword(s):  
Sensitivity Analysis ◽  
Feed Rate ◽  
Process Temperature ◽  
Drilling Process ◽  
Drilling Speed ◽  
Multi Objective Optimization ◽  
Bone Drilling ◽  
Multi Objective ◽  
Tool Diameter ◽  
First Time

The bone drilling process is very prominent in orthopedic surgeries and in the repair of bone fractures. It is also very common in dentistry and bone sampling operations. Due to the complexity of bone and the sensitivity of the process, bone drilling is one of the most important and sensitive processes in biomedical engineering. Orthopedic surgeries can be improved using robotic systems and mechatronic tools. The most crucial problem during drilling is an unwanted increase in process temperature (higher than 47 °C), which causes thermal osteonecrosis or cell death and local burning of the bone tissue. Moreover, imposing higher forces to the bone may lead to breaking or cracking and consequently cause serious damage. In this study, a mathematical second-order linear regression model as a function of tool drilling speed, feed rate, tool diameter, and their effective interactions is introduced to predict temperature and force during the bone drilling process. This model can determine the maximum speed of surgery that remains within an acceptable temperature range. Moreover, for the first time, using designed experiments, the bone drilling process was modeled, and the drilling speed, feed rate, and tool diameter were optimized. Then, using response surface methodology and applying a multi-objective optimization, drilling force was minimized to sustain an acceptable temperature range without damaging the bone or the surrounding tissue. In addition, for the first time, Sobol statistical sensitivity analysis is used to ascertain the effect of process input parameters on process temperature and force. The results show that among all effective input parameters, tool rotational speed, feed rate, and tool diameter have the highest influence on process temperature and force, respectively. The behavior of each output parameters with variation in each input parameter is further investigated. Finally, a multi-objective optimization has been performed considering all the aforementioned parameters. This optimization yielded a set of data that can considerably improve orthopedic osteosynthesis outcomes.

Parametric optimization in modified air abrasive jet machining

2019 ◽  
Vol 15 (3) ◽  
pp. 617-629
Author(s):  
S. Rajendra Prasad ◽  
K. Ravindranath K. Ravindranath ◽  
M.L.S. Devakumar M.L.S. Devakumar
Keyword(s):  
Optimization Technique ◽  
Mesh Size ◽  
Machining Parameters ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Multi Objective ◽  
Abrasive Jet Machining ◽  
Alloy Material ◽  
Size Measure ◽  
First Time

Purpose The choice of best machining parameters is an extremely basic factor in handling of any machined parts. The purpose of this paper is to exhibit a multi-objective optimization technique; in view of weighted aggregate sum product assessment (WASPAS) technique toward upgrade the machining parameters in modified air abrasive jet machining (MAAJM) process: injecting pressure, stand-off distance (SOD), and abrasive mesh size measure with 100 rpm rotatable worktable on Nickel 233 alloy material. Three conflicting destinations, material removal rate (MRR), surface roughness (SR) and taper angles (Ta), respectively, are considered at the same time. The proposed procedure uses WASPAS, which is the examination of parametric optimization of the abrasive jet machining (AJM) process. The results was used any scopes of reactions in MAAJM process is the ideal setting of parameters are resolved through investigations represented. There is wide utilization of Nickel 233 in aviation enterprises; machining information on producing a hole utilizing MAAJM for the first time is given in this work, which will be helpful different industries. Design/methodology/approach This paper exhibits a multi-objective optimization technique; in view of WASPAS technique toward upgrade the machining parameters in MAAJM process: injecting pressure, SOD, and abrasive mesh size measure with 100 rpm rotatable worktable on Nickel 233 alloy material. Findings As an outcome of using the tool in any ranges of responses in the AJM process, the optimal setting of parameters is determined through experiments illustrated. The machining data of generating a hole using AJM are studied for the first time in this work, which will be useful for aerospace industries, where Nickel 233 is used broadly. Originality/value A new material in unconventional machining process and also a multi-objective optimization technique are adopted.

Multi-Objective Optimization of Microturning Process Parameters Using Particle Swarm Technique

2013 ◽  
Author(s):  
Nithin Tom Mathew ◽  
Kanthababu Mani
Keyword(s):  
Tool Wear ◽  
Process Parameters ◽  
Removal Rate ◽  
Computational Cost ◽  
Particle Swarm ◽  
Depth Of Cut ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Confirmation Test ◽  
First Time

In this work, for the first time an attempt has been made to carry out multi-objective optimization for tool based microturning process parameters using particle swarm optimization (PSO) technique. The input microturning process parameters considered are speed, feed and depth of cut. The output parameters considered are material removal rate (MRR), surface roughness (Ra) and tool wear (TW). The significant parameters are identified individually using ANOVA and main effect plots. However, it is observed that the main goal of the manufacturers is to produce high quality products in shorter interval of time. In order to meet the above objective, multi-objective optimization is carried out to achieve simultaneously higher MRR, low Ra and low TW using PSO. From the PSO analysis, it is observed that the combination of microturning parameters such as speed (18.25 m/min), feed (9.31 μm/rev) and depth of cut (14.61 μm) results in high MRR, low Ra and low tool wear. The PSO analysis indicates that it is a promising optimization algorithm due to its simplicity, low computational cost and good performance. A confirmation test was carried out to validate the predicted results.

Use Multi-Objective Optimization for Designing Cloaks With Acoustic and Elastic Materials

2012 ◽  
Author(s):  
Chunyan Bao ◽  
Liang-Wu Cai
Keyword(s):  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Frequency Range ◽  
Elastic Materials ◽  
Optimization Scheme ◽  
Multi Objective ◽  
Wide Range ◽  
Optimization Schemes ◽  
Elastic Layers

The various optimization schemes have been recently shown to be a capable tool for designing acoustic cloaks based on Cummer-Schurig prescription without requiring material singularity. However, when using an optimization scheme to optimize a cloak at one specified frequency, sometimes it is observed that the cloaking effect deteriorate at other frequencies. This paper explores the use of multi-objective optimization approach such that the cloaking performance is maintained over a wide range of frequencies. Two examples are presented. The first cloak is comprised of all aocustic layers, and the second cloak is comprised of a mixture of acoustic and elastic layers. The results show the effectiveness of the multi-objective optimization approach on maintaining cloaking performance over a specified frequency range.

Multi-Objective Optimization of Piezoelectric Actuator Placement for Shape Control of Plates Using Genetic Algorithms

2009 ◽  
Vol 131 (9) ◽  
Author(s):  
Rajesh Kudikala ◽  
Deb Kalyanmoy ◽  
Bishakh Bhattacharya
Keyword(s):  
Optimization Algorithm ◽  
Shape Control ◽  
Optimization Problem ◽  
Piezoelectric Actuators ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Input Control ◽  
Wide Range ◽  
Conflicting Objectives ◽  
Lag Model

Shape control of adaptive structures using piezoelectric actuators has found a wide range of applications in recent years. In this paper, the problem of finding optimal distribution of piezoelectric actuators and corresponding actuation voltages for static shape control of a plate is formulated as a multi-objective optimization problem. The two conflicting objectives considered are minimization of input control energy and minimization of mean square deviation between the desired and actuated shapes with constraints on the maximum number of actuators and maximum induced stresses. A shear lag model of the smart plate structure is created, and the optimization problem is solved using an evolutionary multi-objective optimization algorithm: nondominated sorting genetic algorithm-II. Pareto-optimal solutions are obtained for different case studies. Further, the obtained solutions are verified by comparing them with the single-objective optimization solutions. Attainment surface based performance evaluation of the proposed optimization algorithm has been carried out.

scholarly journals Generalized active disturbance rejection control for the boiler-turbine unit using multi-objective optimization and extended state observer

2019 ◽  
Vol 272 ◽  
pp. 01002
Author(s):  
Jianzhong Zhu ◽  
Xiao Wu ◽  
Jiong Shen ◽  
Meihong Wang
Keyword(s):  
Control Strategy ◽  
Disturbance Rejection ◽  
Turbine Unit ◽  
Extended State Observer ◽  
Fuel Quality ◽  
Multi Objective Optimization ◽  
Extended State ◽  
Multi Objective ◽  
Active Disturbance Rejection ◽  
Wide Range

This paper proposes a generalized active disturbance rejection controller (GADRC) based hierarchical control structure for the boilerturbine unit. In the lower layer, a multivariable extended state observer (MESO) is developed to estimate the values of the lumped disturbances caused by modelling mismatches, fuel quality variation and wide range load variation. The influence of the disturbances is then compensated at the input side as a feedforward control. In the upper layer, the multi-objective optimization is devised to obtain the set-points by removing the plant behaviour variation from the optimized model in a feasible way. The lowpass filter acting on the lumped disturbances is designed to bridge the gap between the lower and upper layer. The impact of the feedthrough item is approximated by a first-order system and a two degree-of-freedom (2-DOF) control strategy is established to illustrate the set-point tracking and disturbance rejection properties of the controller. Simulation studies on a 1000MWe coal-fired ultra-supercritical boiler-turbine unit demonstrate that the proposed control strategy can achieve a satisfactory performance in cases of fuel quality variations, model-plant mismatches and wide range load variation.

scholarly journals Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons

2019 ◽  
Author(s):  
Elisabetta Iavarone ◽  
Jane Yi ◽  
Ying Shi ◽  
Bas-Jan Zandt ◽  
Christian O’Reilly ◽  
...  
Keyword(s):  
Experimental Data ◽  
Action Potentials ◽  
Dendritic Structure ◽  
Cellular Level ◽  
Multi Objective Optimization ◽  
Tonic Firing ◽  
Multi Objective ◽  
Wide Range ◽  
Low Threshold ◽  
Thalamocortical System

AbstractSomatosensory thalamocortical (TC) neurons from the ventrobasal (VB) thalamus are central components in the flow of sensory information between the periphery and the cerebral cortex, and participate in the dynamic regulation of thalamocortical states including wakefulness and sleep. This property is reflected at the cellular level by the ability to generate action potentials in two distinct firing modes, called tonic firing and low-threshold bursting. Although the general properties of TC neurons are known, we still lack a detailed characterization of their morphological and electrical properties in the VB thalamus. The aim of this study was to build biophysically-detailed models of VB TC neurons explicitly constrained with experimental data from rats. We recorded the electrical activity of VB neurons (N = 49) and reconstructed morphologies in 3D (N = 50) by applying standardized protocols. After identifying distinct electrical types, we used a multi-objective optimization to fit single neuron electrical models (e-models), which yielded multiple solutions consistent with the experimental data. The models were tested for generalization using electrical stimuli and neuron morphologies not used during fitting. A local sensitivity analysis revealed that the e-models are robust to small parameter changes and that all the parameters were constrained by one or more features. The e-models, when tested in combination with different morphologies, showed that the electrical behavior is substantially preserved when changing dendritic structure and that the e-models were not overfit to a specific morphology. The models and their analysis show that automatic parameter search can be applied to capture complex firing behavior, such as co-existence of tonic firing and low-threshold bursting over a wide range of parameter sets and in combination with different neuron morphologies.Author summaryThalamocortical neurons are one of the main components of the thalamocortical system, which are implicated in key functions including sensory transmission and the transition between brain states. These functions are reflected at the cellular level by the ability to generate action potentials in two distinct modes, called burst and tonic firing. Biophysically-detailed computational modeling of these cells can provide a tool to understand the role of these neurons within thalamocortical circuitry. We started by collecting single cell experimental data by applying standardized experimental procedures in brain slices of the rat. Prior work has demonstrated that biological constraints can be integrated using multi-objective optimization to build biologically realistic models of neuron. Here, we employ similar techniques as those previously employed, but extend them to capture the multiple firing modes of thalamic neurons. We compared the model results with additional experimental data test their generalization and quantitatively reject those that deviated significantly from the experimental variability. These models can be readily integrated in a data-driven pipeline to reconstruct and simulate circuit activity in the thalamocortical system.

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