A New Approach to Design of a Lightweight Anthropomorphic Arm for Service Applications

2015 ◽  
Vol 7 (3) ◽  
Author(s):  
Lelai Zhou ◽  
Shaoping Bai
Keyword(s):  
Design Optimization ◽  
Integrated Design ◽  
Robot Kinematics ◽  
Strength Analysis ◽  
Optimization Approach ◽  
Element Analysis ◽  
New Approach ◽  
Design Variables ◽  
Kinematic Performance ◽  
Service Application

This paper describes a new approach to the design of a lightweight robotic arm for service applications. A major design objective is to achieve a lightweight robot with desired kinematic performance and compliance. This is accomplished by an integrated design optimization approach, where robot kinematics, dynamics, drive-train design and strength analysis by means of finite element analysis (FEA) are generally considered. In this approach, kinematic dimensions, structural dimensions, and the motors and the gearboxes are parameterized as design variables. Constraints are formulated on the basis of kinematic performance, dynamic requirements and structural strength limitations, whereas the main objective is to minimize the weight. The design optimization of a five degree-of-freedom (dof) lightweight arm is demonstrated and the robot development for service application is also presented.

Reliability-based design optimization of electromagnetic shielding structure using neural networks and real-coded genetic algorithm

2014 ◽  
Vol 228 (18) ◽  
pp. 3471-3481 ◽  
Author(s):  
Heeralal Gargama ◽  
Sanjay K Chaturvedi ◽  
Awalendra K Thakur
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Design Optimization ◽  
Electromagnetic Shielding ◽  
Optimization Approach ◽  
Reliability Based Design Optimization ◽  
Reliability Based Design ◽  
Design Variables ◽  
Real Coded Genetic Algorithm

The conventional approaches for electromagnetic shielding structures’ design, lack the incorporation of uncertainty in the design variables/parameters. In this paper, a reliability-based design optimization approach for designing electromagnetic shielding structure is proposed. The uncertainties/variability in the design variables/parameters are dealt with using the probabilistic sufficiency factor, which is a factor of safety relative to a target probability of failure. Estimation of probabilistic sufficiency factor requires performance function evaluation at every design point, which is extremely computationally intensive. The computational burden is reduced greatly by evaluating design responses only at the selected design points from the whole design space and employing artificial neural networks to approximate probabilistic sufficiency factor as a function of design variables. Subsequently, the trained artificial neural networks are used for the probabilistic sufficiency factor evaluation in the reliability-based design optimization, where optimization part is processed with the real-coded genetic algorithm. The proposed reliability-based design optimization approach is applied to design a three-layered shielding structure for a shielding effectiveness requirement of ∼40 dB, used in many industrial/commercial applications, and for ∼80 dB used in the military applications.

A MultiDisciplinary Optimization Approach for Sizing Vertical Lift Aircraft

2021 ◽  
Author(s):  
Ananth Sridharan ◽  
Bharath Govindarajan
Keyword(s):  
Optimal Solution ◽  
Multidisciplinary Optimization ◽  
Shape Design ◽  
Optimization Approach ◽  
Optimization Strategy ◽  
Clock Time ◽  
Element Analysis ◽  
Analysis And Design ◽  
Design Variables ◽  
Vertical Lift

This paper presents an approach to reframe the sizing problem for vertical-lift unmanned aerial vehicles (UAVs) as an optimization problem and obtains a weight-optimal solution with up to two orders of magnitude of savings in wall clock time. Because sizing is performed with higher fidelity models and design variables from several disciplines, the Simultaneous Analysis aNd Design (SAND) approach from fixed-wing multidisciplinary optimization literature is adapted for the UAV sizing task. Governing equations and disciplinary design variables that are usually self-contained within disciplines (airframe tube sizes, trim variables, and trim equations) are migrated to the sizing optimizer and added as design variables and (in)equality constraints. For sizing consistency, the iterative weight convergence loop is replaced by a coupling variable and associated equality consistency constraint for the sizing optimizer. Cruise airspeed is also added as a design variable and driven by the sizing optimizer. The methodology is demonstrated for sizing a package delivery vehicle (a lift-augment quadrotor biplane tailsitter) with up to 39 design variables and 201 constraints. Gradient-based optimizations were initiated from different starting points; without blade shape design in sizing, all processes converged to the same minimum, indicating that the design space is convex for the chosen bounds, constraints, and objective function. Several optimization schemes were investigated by moving combinations of relevant disciplines (airframe sizing with finite element analysis, vehicle trim, and blade aerodynamic shape design) to the sizing optimizer. The biggest advantage of the SAND strategy is its scope for parallelization, and the inherent ability to drive the design away from regions where disciplinary analyses (e.g., trim) cannot find a solution, obviating the need for ad hoc penalty functions. Even in serial mode, the SAND optimization strategy yields results in the shortest wall clock time compared to all other approaches.

An Optimization Approach Toward a Robust Design of Six Degrees of Freedom Haptic Devices

2015 ◽  
Vol 137 (4) ◽  
Author(s):  
Aftab Ahmad ◽  
Kjell Andersson ◽  
Ulf Sellgren
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Degrees Of Freedom ◽  
Design Parameters ◽  
Manufacturing Cost ◽  
Optimization Approach ◽  
Performance Indices ◽  
New Approach ◽  
Haptic Devices ◽  
Six Degrees Of Freedom

This work presents an optimization approach for the robust design of six degrees of freedom (DOF) haptic devices. Our objective is to find the optimal values for a set of design parameters that maximize the kinematic, dynamic, and kinetostatic performances of a 6-DOF haptic device while minimizing its sensitivity to variations in manufacturing tolerances. Because performance indices differ in magnitude, the formulation of an objective function for multicriteria performance requirements is complex. A new approach based on Monte Carlo simulation (MCS) was used to find the extreme values (minimum and maximum) of the performance indices to enable normalization of these indices. The optimization approach presented here is formulated as a methodology in which a hybrid design-optimization approach, combining genetic algorithm (GA) and MCS, is first used. This new approach can find the numerical values of the design parameters that are both optimal and robust (i.e., less sensitive to variation and thus to uncertainties in the design parameters). In the following step, with design optimization, a set of optimum tolerances is determined that minimizes manufacturing cost and also satisfies the allowed variations in the performance indices. The presented approach can thus enable the designer to evaluate trade-offs between allowed performance variations and tolerances cost.

Flow Uniformity Characterization and Its Application in Extruder Flow Channel Design Optimization

2009 ◽  
Author(s):  
Minwu Yao ◽  
Ching-Chih Lee
Keyword(s):  
Design Optimization ◽  
Cross Section ◽  
Search Space ◽  
Flow Channel ◽  
Channel Cross Section ◽  
Optimization Approach ◽  
Flow Uniformity ◽  
Element Analysis ◽  
Automated Optimization ◽  
Optimization Loop

We are interested in developing a general design optimization loop for extruder flow channel inserts based on 3-D finite element analysis (FEA). A key step of the computational procedure is the automated flow uniformity assessment which requires a quantitative characterization of the flow uniformity at the exit opening. To this end, a non-dimensional flow uniformity parameter is proposed, which is independent of flow rate & geometric dimensions and applicable to any shape of the flow channel cross-section. For a rectangular exit opening, two additional flow uniformity parameters are used to characterize the flow distribution in the two principal directions of the exit cross-section. The proposed flow uniformity parameter was successfully used as the objective function in an automated optimization loop for geometric design of flow channel inserts. The automated optimization loop allowed us to explore the design space in a broad range. Simulation results show that flow balance is consistently improved with the expansion of the design search space. It was also found that the automated optimization loop provides a better optimal solution than those obtained from the manual optimization approach under the same pressure-drop constraint.

Multidisciplinary Design Optimization of Small Canister-Launched Space Launch Vehicle Using Genetic Algorithm

2013 ◽  
Vol 302 ◽  
pp. 583-588 ◽  
Author(s):  
Fredy M. Villanueva ◽  
Lin Shu He ◽  
Da Jun Xu
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Optimal Solution ◽  
Optimization Method ◽  
Launch Vehicle ◽  
Low Earth Orbit ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Design Variables

A multidisciplinary design optimization approach of a three stage solid propellant canister-launched launch vehicle is considered. A genetic algorithm (GA) optimization method has been used. The optimized launch vehicle (LV) is capable of delivering a microsatellite of 60 kg. to a low earth orbit (LEO) of 600 km. altitude. The LV design variables and the trajectory profile variables were optimized simultaneously, while a depleted shutdown condition was considered for every stage, avoiding the necessity of a thrust termination device, resulting in reduced gross launch mass of the LV. The results show that the proposed optimization approach was able to find the convergence of the optimal solution with highly acceptable value for conceptual design phase.

Structural Optimization of Fir-Tree Root and Groove for Turbine Blade With Superellipse and P-Norm Aggregation Function

2019 ◽  
Author(s):  
Deqi Yu ◽  
Xiaojuan Zhang ◽  
Jiandao Yang ◽  
Kai Cheng ◽  
Ming Li
Keyword(s):  
Stress Concentration ◽  
Design Optimization ◽  
Finite Size ◽  
Large Family ◽  
Peak Stress ◽  
Aggregation Function ◽  
Optimization Approach ◽  
Optimization Constraints ◽  
Design Variables ◽  
Family Of Curves

Abstract Due to its finite size and the large centrifugal load, the fir-tree root is highly stressed, which leads to the possible early failure of the gas turbine and steam turbine. To find an optimized fir-tree root is an important issue for the design of the turbine structures. In this paper, a superellipse-based design optimization approach is proposed for the fir-tree root. Rather than the straight line and arc used in literature, the combination of the superellipse curve and line are employed to characterize the fir-tree root since the superellipse curve represents a large family of curves with limited parameters, which makes the design optimization easy and economic. For the design optimization, the objective function is to minimize the peak stress, which is a typical min-max problem with possible severe iterative oscillation and subsequent convergence difficulty. To avoid this problem, a P-norm aggregation function is proposed. The superellipse parameters are defined as design variables, while the stress concentration factor and the stress at root neck are specified as optimization constraints. With the P-series fir-tree root design as example, it is proved that our approach is effective to find the optimized configuration with better stress distribution and lower stress concentration.

scholarly journals Robust Design Optimization with Penalty Function for Electric Oil Pumps with BLDC Motors

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 153 ◽  
Author(s):  
Keun-Young Yoon ◽  
Soo-Whang Baek
Keyword(s):  
Optimal Design ◽  
Design Optimization ◽  
Robust Design ◽  
Penalty Function ◽  
Block Diagram ◽  
Robust Design Optimization ◽  
Bldc Motor ◽  
Control Block ◽  
Element Analysis ◽  
Design Variables

In this paper, we propose and evaluate a robust design optimization (RDO) algorithm for the shape of a brushless DC (BLDC) motor used in an electric oil pump (EOP). The components of the EOP system and the control block diagram for driving the BLDC motor are described. Although the conventional deterministic design optimization (DDO) method derives an appropriate combination of design goals and target performance, DDO does not allow free searching of the entire design space because it is confined to preset experimental combinations of parameter levels. To solve this problem, we propose an efficient RDO method that improves the torque characteristics of BLDC motors by considering design variable uncertainties. The dimensions of the stator and the rotor were selected as the design variables for the optimal design and a penalty function was applied to address the disadvantages of the conventional Taguchi method. The optimal design results obtained through the proposed RDO algorithm were confirmed by finite element analysis, and the improvement in torque and output performance was confirmed through experimental dynamometer tests of a BLDC motor fabricated according to the optimization results.

scholarly journals A Sequential Linearization Approach for Solving Mixed-Discrete Nonlinear Design Optimization Problems

1991 ◽  
Vol 113 (3) ◽  
pp. 325-334 ◽  
Author(s):  
Han Tong Loh ◽  
P. Y. Papalambros
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Continuous Variables ◽  
New Approach ◽  
Design Variables ◽  
Nonlinear Design ◽  
Discrete Problems ◽  
Computationally Intensive ◽  
Discrete Values ◽  
Sequential Linearization

Design optimization models of often contain variables that must take only discrete values, such as standard sizes. Nonlinear optimization problems with a mixture of discrete and continuous variables are very difficult, and existing algorithms are either computationally intensive or applicable to models with special structure. A new approach for solving nonlinear mixed-discrete problems with no particular structure is presented here, motivated by its efficiency for models with extensive monotonicities of the problem’s objective and constraint functions with respect to the design variables. It involves solving a sequence of mixed-discrete linear approximations of the original nonlinear model. In this article, a review of previous approaches is followed by description of the resulting algorithm, its convergence properties and limitations. Several illustrative examples are given. A sequel article presents a detailed algorithmic implementation and extensive computational results.

scholarly journals Automatic Strain Gauge Balance Design Optimization Approach and Implementation Based on Integration of Software

2020 ◽  
Vol 20 (1) ◽  
pp. 22-34
Author(s):  
Guangwei Xiang ◽  
Peng Mi ◽  
Guoqing Yi ◽  
Chao Wang ◽  
Wei Liu
Keyword(s):  
Design Optimization ◽  
Design Process ◽  
Network Architecture ◽  
High Performance ◽  
Lightweight Design ◽  
Optimization Approach ◽  
Optimization Strategy ◽  
Balance Design ◽  
Design Variables ◽  
Tunnel Strain

AbstractThe traditional wind tunnel strain balance design cycle is a manual iterative process. With the experience and intuition of the designer, one solution that meets the design requirements can be selected among a small number of design solutions. This paper introduces a novel software integration-based automatic balance design optimization system (ABDOS) and its implementation by integrating professional design knowledge and experience, stepwise optimization strategy, CAD-CAE software, self-developed scripts and tools. The proposed two-step optimization strategy includes the analytical design process (ADP) and the finite element method design process (FEDP). The built-in optimization algorithm drives the design variables change and searches for the optimal structure combination meeting the design objectives. The client-server based network architecture enables local lightweight design input, task management, and result output. The high-performance server combines all design resources to perform all the solution calculations. The development of more than 10 balances that have been completed and a case study show that this method and platform significantly reduce the time for design evaluation and design-analysis-redesign cycles, assisting designers to comprehensively evaluate and improve the performance of the balance.

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