scholarly journals Analysis of Laser Tracker-Based Volumetric Error Mapping Strategies for Large Machine Tools

Metals ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 757 ◽  
Author(s):  
Beñat Iñigo ◽  
Ander Ibabe ◽  
Gorka Aguirre ◽  
Harkaitz Urreta ◽  
Luis Norberto López de Lacalle
Keyword(s):  
Model Fitting ◽  
Optimization Methods ◽  
Measurement Model ◽  
Design Parameters ◽  
Laser Tracker ◽  
Volumetric Error ◽  
Machine Errors ◽  
Measurement Strategy ◽  
Milling Machines ◽  
Tool Position

The measurement and compensation of volumetric error in milling machines of medium and large size is a key aspect to meeting the precision requirements of the most demanding applications. There are several solutions for volumetric error measurement—usually based on laser or in calibrated artifacts—that offer different specifications and lead to a variety of levels of precision, complexity of implementation and automation, cost of equipment, and measurement time, amongst others. Therefore, it is essential to have tools that allow, in each case, analysis as to which is the optimal calibration strategy, providing the criteria for evaluating different measurement equipment and strategies. To respond to this need, several tools have been developed which are able to simulate the entire calibration and compensation process (machine, measurement, model adjustment, etc.) and apply optimization methods to find the best measurement strategy for each application. For a given machine architecture and expected error ranges, the compensation error for each strategy is obtained by propagating measurement uncertainties and expected machine errors through the measurement and compensation model fitting process by Monte Carlo simulations. The use of this tool will be demonstrated through the analysis of the influence of the main design parameters of a measurement strategy for the calibration of a 3-axis machine tool, based on the measurement of tool position with a laser tracker.

Life Augmentation of Turbine Exhaust System Compensators Through Integrated MADM Optimization Approach of Stress Based Fatigue Cycles

2021 ◽  
Author(s):  
Nitin D. Pagar ◽  
Amit R. Patil
Keyword(s):  
Exhaust System ◽  
Optimization Methods ◽  
Fractional Factorial ◽  
Design Parameters ◽  
Optimization Approach ◽  
Auxiliary Equipment ◽  
Maximum Expansion ◽  
Optimal Configurations ◽  
Selection Of ◽  
Turbine Exhaust

Abstract Exhaust expansion joints, also known as compensators, are found in a variety of applications such as gas turbine exhaust pipes, generators, marine propulsion systems, OEM engines, power units, and auxiliary equipment. The motion compensators employed must have accomplished the maximum expansion-contraction cycle life while imposing the least amount of stress. Discrepancies in the selecting of bellows expansion joint design parameters are corrected by evaluating stress-based fatigue life, which is challenging owing to the complicated form of convolutions. Meridional and circumferential convolution stress equations that influencing fatigue cycles are evaluated and verified with FEA. Fractional factorial Taguchi L25 matrix is used for finding the optimal configurations. The discrete design parameters for the selection of the suitable configuration of the compensators are analysed with the help of the MADM decision making techniques. The multi-response optimization methods GRA, AHP, and TOPSIS are used to determine the parametric selection on a priority basis. It is seen that weighing distribution among the responses plays an important role in these methods and GRA method integrated with principal components shows best optimal configurations. Multiple regression technique applied to these methods also shows that PCA-GRA gives better alternate solutions for the designer unlike the AHP and TOPSIS method. However, higher ranked Taguchi run obtained in these methods may enhance the suitable selection of different design configurations. Obtained PCA-GRG values by Taguchi, Regression and DOE are well matched and verified for the all alternate solutions. Further, it also shows that stress based fatigue cycles obtained in this analysis for the L25 run indicates the range varying from 1.13 × 104 cycles to 9.08 × 105 cycles, which is within 106 cycles. This work will assist the design engineer for selecting the discrete parameters of stiff compensators utilized in power plant thermal appliances.

An Adjoint Based Approach for Optimal Design of Morphing SMP

2013 ◽  
Author(s):  
Shuang Wang ◽  
John C. Brigham
Keyword(s):  
Shape Change ◽  
Mechanical Energy ◽  
Optimization Methods ◽  
Design Parameters ◽  
Optimization Strategy ◽  
Thermally Activated ◽  
Gradient Based ◽  
Nonlinear Optimization Methods ◽  
Numerical Representations ◽  
Objective Functional

This work presents a strategy to identify the optimal localized activation and actuation for a morphing thermally activated SMP structure or structural component to obtain a targeted shape change or set of shape features, subject to design objectives such as minimal total required energy and time. This strategy combines numerical representations of the SMP structure’s thermo-mechanical behavior subject to activation and actuation with gradient-based nonlinear optimization methods to solve the morphing inverse problem that includes minimizing cost functions which address thermal and mechanical energy, morphing time, and damage. In particular, the optimization strategy utilizes the adjoint method to efficiently compute the gradient of the objective functional(s) with respect to the design parameters for this coupled thermo-mechanical problem.

Constrained Optimization of Multi-Degree-of-Freedom Mechanisms for Near-Time Optimal Motions

1992 ◽  
Author(s):  
Satish Sundar ◽  
Zvi Shiller
Keyword(s):  
Constrained Optimization ◽  
Design Method ◽  
Close Correlation ◽  
Optimization Methods ◽  
Pattern Search ◽  
Degree Of Freedom ◽  
Design Parameters ◽  
Maximum Acceleration ◽  
Time Optimal ◽  
Order Of Magnitude

Abstract This paper presents a design method of multi-degree-of-freedom mechanisms for near-time optimal motions. The design objective is to select system parameters, such as link lengths and actuator sizes, so as to minimize the optimal motion time of the mechanism along a given path. The exact time optimization problem is approximated by a simpler procedure that maximizes the acceleration near the end points. Representing the directions of maximum acceleration with the acceleration lines, and the reachability constraints as explicit functions of the design parameters, we transform the constrained optimization to a simpler curve fitting problem that can be formulated analytically. This allows the use of efficient gradient type optimizations, instead of the pattern search optimization that is otherwise required. Examples for optimizing the dimensions of a five-bar planar mechanism demonstrate close correlation of the approximate with the exact solutions, and an order of magnitude better computational efficiency than the previously developed unconstrained optimization methods.

Direct Optimization of Multi-Degree-of-Freedom Mechanisms for Near Time Optimal Motions Along Specified Paths

1991 ◽  
Author(s):  
Satish Sundar ◽  
Zvi Shiller
Keyword(s):  
Design Method ◽  
Close Correlation ◽  
Optimization Methods ◽  
Degree Of Freedom ◽  
Design Parameters ◽  
Direct Optimization ◽  
Fitting Procedure ◽  
Time Optimal ◽  
Approximate Cost ◽  
The Given

Abstract A design method for selecting system parameters of multi-degree-of-freedom mechanisms for near minimum time motions along specified paths is presented. The time optimization problem is approximated by a simple curve fitting procedure that fits, what we call, the acceleration lines to the given path. The approximate cost function is explicit in the design parameters, facilitating the formulation of the design problem as a constrained optimization. Examples for optimizing the dimensions of a five-bar planar mechanism demonstrate close correlation between the approximate and the exact solutions and better computational efficiency than the previous unconstrained optimization methods.

The Effect of Different Optimization Methods on Robustness for Robust Optimization Design

2014 ◽  
Vol 721 ◽  
pp. 464-467
Author(s):  
Tao Fu ◽  
Qin Zhong Gong ◽  
Da Zhen Wang
Keyword(s):  
Robust Optimization ◽  
Objective Function ◽  
Robust Design ◽  
Optimization Design ◽  
Optimization Methods ◽  
Optimization Method ◽  
Design Parameters ◽  
Hybrid Particle Swarm Optimization ◽  
Design Variables ◽  
Robust Optimization Design

In view of robustness of objective function and constraints in robust design, the method of maximum variation analysis is adopted to improve the robust design. In this method, firstly, we analyses the effect of uncertain factors in design variables and design parameters on the objective function and constraints, then calculate maximum variations of objective function and constraints. A two-level optimum mathematical model is constructed by adding the maximum variations to the original constraints. Different solving methods are used to solve the model to study the influence to robustness. As a demonstration, we apply our robust optimization method to an engineering example, the design of a machine tool spindle. The results show that, compared with other methods, this method of HPSO(hybrid particle swarm optimization) algorithm is superior on solving efficiency and solving results, and the constraint robustness and the objective robustness completely satisfy the requirement, revealing that excellent solving method can improve robustness.

Multi-Field Characterization of Single Wall Nano-Tube Composites for Hydrogen Storage

2005 ◽  
Author(s):  
John G. Michopoulos ◽  
Sam G. Lambrakos ◽  
Nick E. Tran
Keyword(s):  
Hydrogen Storage ◽  
Objective Function ◽  
Storage Systems ◽  
Chemical Reactivity ◽  
Linear Optimization ◽  
Optimization Methods ◽  
Design Parameters ◽  
Spatio Temporal ◽  
Extensive Experimental Data

The goal of the present work is three fold. Firstly to create the forward continuum model of a multi-species diffusing system under simultaneous presence of chemical reactivity and temperature as the general case of all hydrogen storage systems. Secondly, cast the problem of hydrogen storage in a pragmatic product-design context where the appropriate design parameters of the system are determined via appropriate optimization methods that utilize extensive experimental data encoding the behavior of the system. Thirdly, demonstrate this methodology on characterizing certain systemic parameters. Thus, the context of the work presented is defined by a data-driven characterization of coupled heat and mass diffusion models of hydrogen storage systems from a multiphysics perspective at the macro length scale. In particular, a single wall nanotube (SWNT) based composite is modeled by coupled partial differential equations representing spatio-temporal evolution of distributions of temperature and hydrogen concentration. Analytical solutions of these equations are adopted for an inverse analysis that defines a non-linear optimization problem for determining the parameters of the model by objective function minimization. Experimentally acquired and model produced data are used to construct the system’s objective function. Simulations to demonstrate the applicability of the methodology and a discussion of its potential extension to multi-scale and manufacturing process optimization are also presented.

scholarly journals Design of optical meta-structures with applications to beam engineering using deep learning

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Robin Singh ◽  
Anu Agarwal ◽  
Brian W. Anthony
Keyword(s):  
Neural Network ◽  
Optimization Methods ◽  
Search Space ◽  
Global Optimum ◽  
Design Parameters ◽  
Diffraction Profile ◽  
Lab On Chip ◽  
Data Driven Approach ◽  
On Chip ◽  
Conventional Optimization

AbstractNanophotonics is a rapidly emerging field in which complex on-chip components are required to manipulate light waves. The design space of on-chip nanophotonic components, such as an optical meta surface which uses sub-wavelength meta-atoms, is often a high dimensional one. As such conventional optimization methods fail to capture the global optimum within the feasible search space. In this manuscript, we explore a Machine Learning (ML)-based method for the inverse design of the meta-optical structure. We present a data-driven approach for modeling a grating meta-structure which performs photonic beam engineering. On-chip planar photonic waveguide-based beam engineering offers the potential to efficiently manipulate photons to create excitation beams (Gaussian, focused and collimated) for lab-on-chip applications of Infrared, Raman and fluorescence spectroscopic analysis. Inverse modeling predicts meta surface design parameters based on a desired electromagnetic field outcome. Starting with the desired diffraction beam profile, we apply an inverse model to evaluate the optimal design parameters of the meta surface. Parameters such as the repetition period (in 2D axis), height and size of scatterers are calculated using a feedforward deep neural network (DNN) and convolutional neural network (CNN) architecture. A qualitative analysis of the trained neural network, working in tandem with the forward model, predicts the diffraction profile with a correlation coefficient as high as 0.996. The developed model allows us to rapidly estimate the desired design parameters, in contrast to conventional (gradient descent based or genetic optimization) time-intensive optimization approaches.

Smart sequential multilateration measurement strategy for volumetric error compensation of an extra-small machine tool

2016 ◽  
Vol 43 ◽  
pp. 178-186 ◽  
Author(s):  
Fabien Ezedine ◽  
Jean-Marc Linares ◽  
Jean-Michel Sprauel ◽  
Julien Chaves-Jacob
Keyword(s):  
Machine Tool ◽  
Error Compensation ◽  
Volumetric Error ◽  
Small Machine ◽  
Measurement Strategy

Design Study of Dovetail Geometries of Turbine Blades Using Abaqus and Isight

2012 ◽  
Author(s):  
Youngwon Hahn ◽  
John I. Cofer
Keyword(s):  
Surrogate Model ◽  
High Reliability ◽  
Turbine Blades ◽  
Optimization Methods ◽  
Nonlinear Static Analysis ◽  
Design Parameters ◽  
Second Step ◽  
Nsga Ii ◽  
Python Script ◽  
Design Variables

Blades in gas and steam turbines continually face more challenging requirements for high reliability and efficiency. In order to meet these challenges in an increasingly competitive marketplace, blade design engineers are always looking for more efficient ways to design the blades in the shortest possible time and at the lowest possible cost while meeting multiple design objectives. In this paper, several design studies are performed using Abaqus and Isight to optimize the minimum contact pressure and stress around the dovetail of a typical turbine blade in order to achieved desired goals for stress levels. First, nine design parameters describing the dimensions of the dovetail are set up in a Python script which can be executed in Abaqus/CAE. The Python script generates the entire finite element model including boundary and loading conditions in Abaqus/CAE. A nonlinear static analysis considering centrifugal loading is performed in this work. After setting up the workflow using the Python script and Abaqus/CAE, Isight is used to automate the process to achieve the optimized dimensions of the dovetail. The optimization is performed in two steps. First, a surrogate model using the Optimal Latin Hypercube approximation method is created using tools in Isight. In this step, the surrogate model is used to determine the optimum values of the design variables, as well as the sensitivity of the design to the selected design variables. It also can be observed that the design is especially sensitive to five of the design variables. In the second step of the optimization, the five design variables to which the design is most sensitive are selected for further optimization by setting the other design variables to the optimized values obtained in the first step of the optimization. In this second step, several different optimization methods supported in Isight are used, including the NSGA-II non-dominated sorting genetic algorithm, Downhill Simplex, and an evolutionary optimization algorithm. Results from these methods are compared with those obtained using other common optimization methods in Isight.

A Study on Parameter Determination Method of Large-Scale and Complex Dynamic Systems With Judgment Functions (No. 1)—Theory

1983 ◽  
Vol 105 (2) ◽  
pp. 64-69
Author(s):  
Osamu Furukawa ◽  
Hideomi Ikeshoji ◽  
Satoshi Iida
Keyword(s):  
Dynamic Systems ◽  
Large Scale ◽  
Optimization Methods ◽  
Approximate Model ◽  
Difficult Problem ◽  
Parameter Determination ◽  
Design Parameters ◽  
Dynamic Relationships ◽  
Complex Mechanical Systems ◽  
Selection Of

In the design of large-scale and complex mechanical systems, determination of design parameters is a very difficult problem. This study deals with parameter satisfaction problems of large-scale, complex, and dynamic systems with judgment functions. In order to solve these problems, a new method is proposed which sequentially exchanges the original mathematical model to an analyzable approximate model by means of the identification method and which improves a lot of parameters simultaneously. First, criteria of selection of attributes to build up approximate model are clarified. Moreover, as tools for selection of attributes, two analysis charts are proposed which express dynamic relationships among the attributes. Second, a general condition to determine the structure of approximate models and a statistical method to linearize the original model with judgment functions are derived. Finally, a combinatorial method of statistical identification and parameter optimization methods are proposed. By this method, some shortcomings of sensitivity analysis, decomposition technique (enormous calculations of partial derivatives) and statistical methods such as Monte Carlo method (bad convergency of solutions) can be avoided. As a result of this, it becomes possible to search satisfactory parameters efficiently.

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