Experiment Driven Local Optimization (EDLO) With an Application to Additive Manufacturing

2017 ◽  
Author(s):  
J. M. Hamel
Keyword(s):  
Additive Manufacturing ◽  
Objective Function ◽  
Design Optimization ◽  
Optimization Problems ◽  
Numerical Test ◽  
Local Optimization ◽  
Optimization Techniques ◽  
Decision Makers ◽  
Optimization Approach ◽  
Physical Experiments

The rise of the use of additive manufacturing processes by engineering and research enterprises has greatly increased opportunities for decision makers to quickly evaluate complex geometries and components throughout the design process. This capability makes it possible to explore design trade-off for which computational or analytical models are not readily available, or are impractical to obtain given available time and resources. However, this often means that decision makers are forced to rely primarily on physical experiments for design data, and this greatly limits opportunities for the application of design optimization techniques. To meet this challenge, a new so-called “online” surrogate based optimization approach, called Experiment Driven Local Optimization (EDLO), has been developed. This approach is focused specifically on using approximation techniques and real-time online surrogate training to solve design optimization problems where the system objective function must be evaluated using physical experiments. As a result, this approach is ideally suited to design problems focused on components that are fabricated using additive manufacturing. This approach is capable of mitigating the effects of experimental uncertainty (or noise) in a design objective function, does not require close coordination between the objective function evaluations and the optimizer, and requires as few physical experiments as possible. These capabilities have been demonstrated through the use of several numerical test problems and also through a 3D printed compliant mechanism design problem. The results produced by the EDLO approach are encouraging and show that this new technique has the potential to move decision makers working with physical experiments and additive manufacturing away from engineering intuition and towards the greater potential of design optimization techniques.

An Agent Based Optimization Approach to Manufacturing Process Planning

2001 ◽  
Author(s):  
Saurabh Deshpande ◽  
Jonathan Cagan
Keyword(s):  
Stochastic Optimization ◽  
Process Planning ◽  
Manufacturing Process ◽  
Optimization Problems ◽  
Search Space ◽  
Optimization Techniques ◽  
Process Models ◽  
Optimization Approach ◽  
Agent Based ◽  
Manufacturing Process Planning

Abstract Many optimization problems, such as manufacturing process planning optimization, are difficult problems due to the large number of potential configurations (process sequences) and associated (process) parameters. In addition, the search space is highly discontinuous and multi-modal. This paper introduces an agent based optimization algorithm that combines stochastic optimization techniques with knowledge based search. The motivation is that such a merging takes advantage of the benefits of stochastic optimization and accelerates the search process using domain knowledge. The result of applying this algorithm to computerized manufacturing process models is presented.

Feasibility Robust Optimization via Scenario Generation and Reduction

2018 ◽  
Author(s):  
Eliot Rudnick-Cohen ◽  
Jeffrey W. Herrmann ◽  
Shapour Azarm
Keyword(s):  
Robust Optimization ◽  
Optimization Problems ◽  
Computational Cost ◽  
Optimization Techniques ◽  
Scenario Generation ◽  
Test Problems ◽  
Optimization Approach ◽  
Uncertain Parameters ◽  
Worst Case ◽  
Robust Solution

Feasibility robust optimization techniques solve optimization problems with uncertain parameters that appear only in their constraint functions. Solving such problems requires finding an optimal solution that is feasible for all realizations of the uncertain parameters. This paper presents a new feasibility robust optimization approach involving uncertain parameters defined on continuous domains without any known probability distributions. The proposed approach integrates a new sampling-based scenario generation scheme with a new scenario reduction approach in order to solve feasibility robust optimization problems. An analysis of the computational cost of the proposed approach was performed to provide worst case bounds on its computational cost. The new proposed approach was applied to three test problems and compared against other scenario-based robust optimization approaches. A test was conducted on one of the test problems to demonstrate that the computational cost of the proposed approach does not significantly increase as additional uncertain parameters are introduced. The results show that the proposed approach converges to a robust solution faster than conventional robust optimization approaches that discretize the uncertain parameters.

scholarly journals Shape Design Optimization of a Robot Arm Using a Surrogate-Based Evolutionary Approach

2020 ◽  
Vol 10 (7) ◽  
pp. 2223 ◽  
Author(s):  
J. C. Hsiao ◽  
Kumar Shivam ◽  
C. L. Chou ◽  
T. Y. Kam
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Computational Cost ◽  
Shape Design ◽  
Optimization Approach ◽  
Robot Arm ◽  
Shape Design Optimization ◽  
Robot Arms ◽  
Conflicting Objectives ◽  
Computer Aided

In the design optimization of robot arms, the use of simulation technologies for modeling and optimizing the objective functions is still challenging. The difficulty is not only associated with the large computational cost of high-fidelity structural simulations but also linked to the reasonable compromise between the multiple conflicting objectives of robot arms. In this paper we propose a surrogate-based evolutionary optimization (SBEO) method via a global optimization approach, which incorporates the response surface method (RSM) and multi-objective evolutionary algorithm by decomposition (the differential evolution (DE ) variant) (MOEA/D-DE) to tackle the shape design optimization problem of robot arms for achieving high speed performance. The computer-aided engineering (CAE) tools such as CAE solvers, computer-aided design (CAD) Inventor, and finite element method (FEM) ANSYS are first used to produce the design and assess the performance of the robot arm. The surrogate model constructed on the basis of Box–Behnken design is then used in the MOEA/D-DE, which includes the process of selection, recombination, and mutation, to optimize the robot arm. The performance of the optimized robot arm is compared with the baseline one to validate the correctness and effectiveness of the proposed method. The results obtained for the adopted example show that the proposed method can not only significantly improve the robot arm performance and save computational cost but may also be deployed to solve other complex design optimization problems.

scholarly journals Robust Design Optimization and Emerging Technologies for Electrical Machines: Challenges and Open Problems

2020 ◽  
Vol 10 (19) ◽  
pp. 6653 ◽  
Author(s):  
Tamás Orosz ◽  
Anton Rassõlkin ◽  
Ants Kallaste ◽  
Pedro Arsénio ◽  
David Pánek ◽  
...  
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Emerging Technologies ◽  
Optimization Problems ◽  
Parameter Tuning ◽  
Optimization Techniques ◽  
Electrical Machines ◽  
Robust Design Optimization ◽  
Open Problems ◽  
Electrical Machinery

The bio-inspired algorithms are novel, modern, and efficient tools for the design of electrical machines. However, from the mathematical point of view, these problems belong to the most general branch of non-linear optimization problems, where these tools cannot guarantee that a global minimum is found. The numerical cost and the accuracy of these algorithms depend on the initialization of their internal parameters, which may themselves be the subject of parameter tuning according to the application. In practice, these optimization problems are even more challenging, because engineers are looking for robust designs, which are not sensitive to the tolerances and the manufacturing uncertainties. These criteria further increase these computationally expensive problems due to the additional evaluations of the goal function. The goal of this paper is to give an overview of the widely used optimization techniques in electrical machinery and to summarize the challenges and open problems in the applications of the robust design optimization and the prospects in the case of the newly emerging technologies.

Application of multi-objective optimization techniques to improve the aerodynamic performance of a tunnel ventilation jet fan

2014 ◽  
Vol 229 (1) ◽  
pp. 91-105 ◽  
Author(s):  
Joon-Hyung Kim ◽  
Jin-Hyuk Kim ◽  
Joon-Yong Yoon ◽  
Young-Seok Choi ◽  
Sang-Ho Yang
Keyword(s):  
Objective Function ◽  
Design Optimization ◽  
Optimization Model ◽  
Forward Direction ◽  
Aerodynamic Performance ◽  
Optimization Techniques ◽  
Multi Objective Optimization ◽  
Tunnel Ventilation ◽  
Multi Objective ◽  
Jet Fan

This paper describes the design optimization of a tunnel ventilation jet fan through multi-objective optimization techniques. Four design variables were selected for design optimization. To analyze the performance of the fan, numerical analyses were conducted, and three-dimensional Reynolds-averaged Navier–Stokes equations with a shear stress transport turbulence model were solved. Two objective functions, the total efficiency of the forward direction and the ratio of the reverse direction outlet velocity to the forward direction outlet velocity, were employed, and multi-objective optimization was carried out to improve the aerodynamic performance. A response surface approximation surrogate model was constructed for each objective function based on numerical solutions obtained at specified design points. The non-dominated sorting genetic algorithm with a local search procedure was used for multi-objective optimization. The tradeoff between the two objectives was determined and described with respect to the Pareto-optimal solutions. Based on the analysis of the optimization results, we propose an optimization model to satisfy the objective function. Finally, to verify the performance, experiments with the base model and the optimization model were carried out.

scholarly journals Non-Convex Feasibility Robust Optimization Via Scenario Generation and Local Refinement

2019 ◽  
Vol 142 (5) ◽  
Author(s):  
Eliot Rudnick-Cohen ◽  
Jeffrey W. Herrmann ◽  
Shapour Azarm
Keyword(s):  
Robust Optimization ◽  
Optimization Problems ◽  
Computational Cost ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Scenario Generation ◽  
Test Problems ◽  
Optimization Approach ◽  
Uncertain Parameters ◽  
Worst Case

Abstract Feasibility robust optimization techniques solve optimization problems with uncertain parameters that appear only in their constraint functions. Solving such problems requires finding an optimal solution that is feasible for all realizations of the uncertain parameters. This paper presents a new feasibility robust optimization approach involving uncertain parameters defined on continuous domains. The proposed approach is based on an integration of two techniques: (i) a sampling-based scenario generation scheme and (ii) a local robust optimization approach. An analysis of the computational cost of this integrated approach is performed to provide worst-case bounds on its computational cost. The proposed approach is applied to several non-convex engineering test problems and compared against two existing robust optimization approaches. The results show that the proposed approach can efficiently find a robust optimal solution across the test problems, even when existing methods for non-convex robust optimization are unable to find a robust optimal solution. A scalable test problem is solved by the approach, demonstrating that its computational cost scales with problem size as predicted by an analysis of the worst-case computational cost bounds.

scholarly journals Weapon Target Assignment

2021 ◽  
Author(s):  
Mohammad Babul Hasan ◽  
Yaindrila Barua
Keyword(s):  
Integer Programming ◽  
Objective Function ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Nonlinear Integer Programming ◽  
Integer Programming Problem ◽  
Combinatorial Optimization Problems ◽  
Target Assignment ◽  
Minimum Number ◽  
Np Complete

This chapter is mainly based on an important sector of operation research-weapon’s assignment (WTA) problem which is a well-known application of optimization techniques. While we discuss about WTA, we need some common terms to be discussed first. In this section, we first introduce WTA problem and then we present some prerequisites such as optimization model, its classification, LP, NLP, SP and their classifications, and applications of SP. We also discuss some relevant software tools we use to optimize the problems. The weapon target assignment problem (WTA) is a class of combinatorial optimization problems present in the fields of optimization and operations research. It consists of finding an optimal assignment of a set of weapons of various types to a set of targets in order to maximize the total expected damage done to the opponent. The WTA problem can be formulated as a nonlinear integer programming problem and is known to be NP-complete. There are constraints on weapons available of various types and on the minimum number of weapons by type to be assigned to various targets. The constraints are linear, and the objective function is nonlinear. The objective function is formulated in terms of probability of damage of various targets weighted by their military value.

scholarly journals Optimization of Polymer Processing: A Review (Part II- Molding Technologies)

2021 ◽  
Author(s):  
António Gaspar-Cunha ◽  
Janusz Sikora ◽  
José A. Covas
Keyword(s):  
Optimization Problems ◽  
Polymer Processing ◽  
Optimization Techniques ◽  
Function Optimization ◽  
Optimization Approach ◽  
Processing Technologies ◽  
Twin Screw Extruders ◽  
Extrusion Dies ◽  
Twin Screw ◽  
Processing Optimization

The application of optimization techniques to improve the performance of polymer processing technologies is of great practical consequence, since it may result in significant savings of materials and energy resources, assist recycling schemes and generate products with better properties. The present review aims at identifying and discussing the most important characteristics of polymer processing optimization problems in terms of the nature of the objective function, optimization algorithm, and process modelling approach that is used to evaluate the solutions and the parameters to optimize. Taking into account the research efforts developed so far, it is shown that several optimization methodologies can be applied to polymer processing with good results, without demanding important computational requirements. Also, within the field of artificial intelligence, several approaches can be reach significant success. The first part of this review demonstrated the advantages of the optimization approach in polymer processing, discussed some concepts on multi-objective optimization and reported the application of optimization methodologies in single and twin screw extruders, extrusion dies and calibrators. This second part focus on injection molding, blow molding and thermoforming technologies.

Origami Actuator Design and Networking Through Crease Topology Optimization

2015 ◽  
Vol 137 (9) ◽  
Author(s):  
Kazuko Fuchi ◽  
Philip R. Buskohl ◽  
Giorgio Bazzan ◽  
Michael F. Durstock ◽  
Gregory W. Reich ◽  
...  
Keyword(s):  
Topology Optimization ◽  
Design Optimization ◽  
Design Method ◽  
Network Connectivity ◽  
Mechanical Analysis ◽  
Optimization Techniques ◽  
Truss Structures ◽  
Optimization Approach ◽  
Design Strategies ◽  
Multiple Actuators

Origami structures morph between 2D and 3D conformations along predetermined fold lines that efficiently program the form of the structure and show potential for many engineering applications. However, the enormity of the design space and the complex relationship between origami-based geometries and engineering metrics place a severe limitation on design strategies based on intuition. The presented work proposes a systematic design method using topology optimization to distribute foldline properties within a reference crease pattern, adding or removing folds through optimization, for a mechanism design. Optimization techniques and mechanical analysis are co-utilized to identify an action origami building block and determine the optimal network connectivity between multiple actuators. Foldable structures are modeled as pin-joint truss structures with additional constraints on fold, or dihedral, angles. A continuous tuning of foldline stiffness leads to a rigid-to-compliant transformation of the local foldline property, the combination of which results in origami crease design optimization. The performance of a designed origami mechanism is evaluated in 3D by applying prescribed forces and finding displacements at set locations. A constraint on the number of foldlines is used to tune design complexity, highlighting the value-add of an optimization approach. Together, these results underscore that the optimization of function, in addition to shape, is a promising approach to origami design and motivates the further development of function-based origami design tools.

Fuzzy Optimization of Complex Systems Using Approximation Concepts

1997 ◽  
Vol 50 (11S) ◽  
pp. S97-S104 ◽  
Author(s):  
Hector A. Jensen ◽  
Abdon E. Sepulveda
Keyword(s):  
Original Problem ◽  
Optimization Problems ◽  
Fuzzy Optimization ◽  
Optimization Techniques ◽  
System Response ◽  
Optimization Approach ◽  
Design Variables ◽  
Intermediate Response ◽  
Intermediate Variables ◽  
Approximation Concepts

This paper presents a methodology for the efficient solution of fuzzy optimization problems. Design variables, as well as system parameters are modeled as fuzzy numbers characterized by membership functions. An optimization approach based on approximation concepts is introduced. High quality approximations for system response functions are constructed using the concepts of intermediate response quantities and intermediate variables. These approximations are used to replace the solution of the original problem by a sequence of approximate problems. Optimization techniques for non-differentiable problems which arise in fuzzy optimization are used to solve the approximate optimization problems. Example problems are presented to illustrate the ideas set forth.

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