Optimized Kinematic Properties for Stevenson-Type Presses With Variable Input Speed Approach

2002 ◽  
Vol 124 (2) ◽  
pp. 350-354 ◽  
Author(s):  
Hong-Sen Yan ◽  
Wei-Ren Chen
Keyword(s):  
Deep Drawing ◽  
Optimization Problems ◽  
Power Input ◽  
Dimensional Synthesis ◽  
Novel Approach ◽  
Pass Through ◽  
Output Characteristics ◽  
Motion Characteristics ◽  
Input Motion ◽  
Kinematic Properties

Traditionally, the input speed of a Stevenson-type press is constant. Here, we propose a novel approach by varying the input speed of the crank to make the ram’s motion suitable for both deep-drawing and precision-cutting. This approach uses a servomotor as the power input. By properly designing the input speed, the output motion can pass through a desired trajectory. The input motion characteristics are planned with Bezier curves. Optimization is used to improve the output characteristics. Guidelines for defining the optimization problems are discussed. Additional dimensional synthesis is also suggested for reducing the input speed variation. Design examples are given for illustration.

KINEMATIC AND DYNAMIC DESIGN OF FOUR-BAR LINKAGES WITH VARIABLE INPUT SPEED AND EXTERNAL APPLIED LOADS

2002 ◽  
Vol 26 (3) ◽  
pp. 281-309 ◽  
Author(s):  
HONG-SEN YAN ◽  
REN-CHUNG SOONG
Keyword(s):  
Dynamic Balance ◽  
Servo Control ◽  
Design Stage ◽  
Design Parameters ◽  
Dynamic Design ◽  
Novel Approach ◽  
Design Requirements ◽  
Motion Characteristics ◽  
Input Motion ◽  
The Given

A novel approach that integrates the kinematic synthesis, dynamic design and servo control in one design stage is presented for designing four-bar linkages with external applied loads. This approach satisfies kinematic design requirements and constraints and also attains trade-off of dynamic balance. By properly designing the speed trajectory of the input link, the balancing parameters of moving links and link dimensions of the given or desired mechanisms, the expected output motion characteristics and dynamic balancing performance are obtained. And, the input motion characteristics are designed with Bezier curves. Optimization is aided to find out optimal design parameters for achieving kinematic and dynamic design requirements and constraints. The speed trajectory of the input link could be generated by a servomotor. Examples are given to demonstrate the feasibility of this approach.

scholarly journals A Novel Approach for Solving Nonsmooth Optimization Problems with Application to Nonsmooth Equations

2013 ◽  
Vol 2013 ◽  
pp. 1-10
Author(s):  
Hamid Reza Erfanian ◽  
M. H. Noori Skandari ◽  
A. V. Kamyad
Keyword(s):  
Nonsmooth Optimization ◽  
Optimization Problems ◽  
Piecewise Linear ◽  
Linear Optimization ◽  
Optimal Solution ◽  
Piecewise Linear Function ◽  
Nonsmooth Equations ◽  
Smooth Functions ◽  
Generalized Derivative ◽  
Novel Approach

We present a new approach for solving nonsmooth optimization problems and a system of nonsmooth equations which is based on generalized derivative. For this purpose, we introduce the first order of generalized Taylor expansion of nonsmooth functions and replace it with smooth functions. In other words, nonsmooth function is approximated by a piecewise linear function based on generalized derivative. In the next step, we solve smooth linear optimization problem whose optimal solution is an approximate solution of main problem. Then, we apply the results for solving system of nonsmooth equations. Finally, for efficiency of our approach some numerical examples have been presented.

A Novel Approach to Designing Surrogate-assisted Genetic Algorithms by Combining Efficient Learning of Walsh Coefficients and Dependencies

2021 ◽  
Vol 1 (2) ◽  
pp. 1-23
Author(s):  
Arkadiy Dushatskiy ◽  
Tanja Alderliesten ◽  
Peter A. N. Bosman
Keyword(s):  
Boolean Functions ◽  
Surrogate Model ◽  
Optimization Problems ◽  
State Of The Art ◽  
Fitness Function ◽  
Optimal Solution ◽  
Problem Structure ◽  
Novel Approach ◽  
Efficient Learning ◽  
Nk Landscapes

Surrogate-assisted evolutionary algorithms have the potential to be of high value for real-world optimization problems when fitness evaluations are expensive, limiting the number of evaluations that can be performed. In this article, we consider the domain of pseudo-Boolean functions in a black-box setting. Moreover, instead of using a surrogate model as an approximation of a fitness function, we propose to precisely learn the coefficients of the Walsh decomposition of a fitness function and use the Walsh decomposition as a surrogate. If the coefficients are learned correctly, then the Walsh decomposition values perfectly match with the fitness function, and, thus, the optimal solution to the problem can be found by optimizing the surrogate without any additional evaluations of the original fitness function. It is known that the Walsh coefficients can be efficiently learned for pseudo-Boolean functions with k -bounded epistasis and known problem structure. We propose to learn dependencies between variables first and, therefore, substantially reduce the number of Walsh coefficients to be calculated. After the accurate Walsh decomposition is obtained, the surrogate model is optimized using GOMEA, which is considered to be a state-of-the-art binary optimization algorithm. We compare the proposed approach with standard GOMEA and two other Walsh decomposition-based algorithms. The benchmark functions in the experiments are well-known trap functions, NK-landscapes, MaxCut, and MAX3SAT problems. The experimental results demonstrate that the proposed approach is scalable at the supposed complexity of O (ℓ log ℓ) function evaluations when the number of subfunctions is O (ℓ) and all subfunctions are k -bounded, outperforming all considered algorithms.

Design of a Two-Input Linkage-Based Motor

1996 ◽  
Author(s):  
Yassir Shanshal ◽  
Kambiz Farhang
Keyword(s):  
Design Methodology ◽  
Angular Motion ◽  
Dimensional Synthesis ◽  
Reciprocating Motion ◽  
Linkage Mechanism ◽  
Motion Synthesis ◽  
Small Motion ◽  
Motor Design ◽  
Input Motion ◽  
Approximate Equations

Abstract This paper proposes the use of a seven-bar linkage mechanism to obtain a multiply actuated motor. Design of two input mechanisms is presented involving two synthesis sub-tasks of input motion synthesis and dimensional synthesis. To this end a design methodology is presented based on the theory of small crank mechanisms. For the case of small motion, approximate equations are developed with the premise that as a result of small reciprocating motion of the input actuators, the motion of every link, with the exception of the output, is small. The motion, in turn, is expressed as a sum of an average and a small oscillatory angular motion about the average. A set of design equations are obtained from the approximate kinematic equations. The design methodology is exemplified using the synthesis of a seven-link mechanism with two translating inputs.

scholarly journals A Novel Approach to Reduce the Unicast Bandwidth of an IPTV System in a High-Speed Access Network

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
El Hassane Khabbiza ◽  
Rachid El Alami ◽  
Hassan Qjidaa
Keyword(s):  
High Speed ◽  
Access Network ◽  
Channel Change ◽  
Set Top Box ◽  
Novel Approach ◽  
Assisted Delivery ◽  
High Bandwidth ◽  
Internet Protocol Television ◽  
Unicast Traffic ◽  
Pass Through

Channel change time is a critical quality of experience (QOE) metric for IP-based video delivery systems such as Internet Protocol Television (IPTV). An interesting channel change acceleration scheme based on peer-assisted delivery was recently proposed, which consists of deploying one FCC server (Fast Channel Change Server) in the IP backbone in order to send the unicast stream to the STB (Set-Top Box) before sending the normal multicast stream after each channel change. However, deploying such a solution will cause high bandwidth usage in the network because of the huge unicast traffic sent by the FCC server to the STBs. In this paper, we propose a new solution to reduce the bandwidth occupancy of the unicast traffic, by deploying the FCC server capabilities on the user STB. This means that, after each channel change request, the STB will receive the unicast traffic from another STB instead of the central server. By using this method, the unicast traffic will not pass through the IP network; it will be a peer-to-peer communication via the Access Network only. Extensive simulation results are presented to demonstrate the robustness of our new solution.

scholarly journals A Novel Approach to the Analysis of Thermal Response Test (TRT) with Interrupted Power Input

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5033
Author(s):  
Jin Luo ◽  
Yuhao Zhang ◽  
Jiasheng Tuo ◽  
Wei Xue ◽  
Joachim Rohn ◽  
...  
Keyword(s):  
Thermal Response ◽  
Power Input ◽  
Source Model ◽  
Thermal Parameters ◽  
Time Interval ◽  
Superposition Method ◽  
Thermal Response Test ◽  
Response Test ◽  
Novel Approach

The quality of measuring datasets of the thermal response test (TRT) significantly influences the interpretation of borehole thermal parameters (BTP). A thermal response test with an unstable power input may induce an unacceptable error in the estimation of the borehole thermal parameters. This paper proposes a novel approach to treat the dataset with interrupted power input. In this approach, the test records were segmented into several subsections with a constant time interval of 100 min, 60 min, and 30 min, separately. The quality of each data section was assessed and analyzed. Then, two algorithms, including the continuous algorithm and semi-superposition algorithm, were developed. The results estimated by the linear source model (LSM) were compared with one Thermal response test datasets with a stable power input at the same testing site. It shows that the effects of power interruption during the test can be effectively mitigated by deploying both the continuous and semi-superposition methods. The lowest deviation of the calculated thermal conductivity to a thermal response test with stable power input was 2.8% in the continuous method and 0.9% using the semi-superposition method. Thus, the proposed approaches are effective measures to mitigate the effects of interrupted power input on the interpretation of the thermal properties of the ground.

ARTIFICIAL BEE COLONY ALGORITHM WITH IMPROVED EXPLORATIONS: A NOVEL APPROACH FOR NUMERICAL OPTIMIZATION

2014 ◽  
Vol 13 (02) ◽  
pp. 1450010
Author(s):  
MD. SHAFIUL ALAM ◽  
MD. MONIRUL ISLAM ◽  
KAZUYUKI MURASE
Keyword(s):  
Swarm Intelligence ◽  
Numerical Optimization ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Adaptive Strategy ◽  
Local Optima ◽  
Selection Scheme ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Novel Approach

The Artificial Bee Colony (ABC) algorithm is a recently introduced swarm intelligence algorithm that has been successfully applied on numerous and diverse optimization problems. However, one major problem with ABC is its premature convergence to local optima, which often originates from its insufficient degree of explorative search capability. This paper introduces ABC with Improved Explorations (ABC-IX), a novel algorithm that modifies both the selection and perturbation operations of the basic ABC algorithm in an explorative way. First, an explorative selection scheme based on simulated annealing allows ABC-IX to probabilistically accept both better and worse candidate solutions, whereas the basic ABC can accept better solutions only. Second, a self-adaptive strategy enables ABC-IX to automatically adapt the perturbation rate, separately for each candidate solution, to customize the degree of explorations and exploitations around it. ABC-IX is evaluated on several benchmark numerical optimization problems and results are compared with a number of state-of-the-art evolutionary and swarm intelligence algorithms. Results show that ABC-IX often performs better optimization than most other algorithms in comparison on most of the problems.

Sequential Quadratic Programming for Robust Optimization With Interval Uncertainty

2012 ◽  
Vol 134 (10) ◽  
Author(s):  
Jianhua Zhou ◽  
Shuo Cheng ◽  
Mian Li
Keyword(s):  
Quadratic Programming ◽  
Robust Optimization ◽  
Sequential Quadratic Programming ◽  
Optimization Problems ◽  
Critical Role ◽  
Interval Uncertainty ◽  
Design Solution ◽  
Novel Approach ◽  
Design Variables ◽  
Feasibility Robustness

Uncertainty plays a critical role in engineering design as even a small amount of uncertainty could make an optimal design solution infeasible. The goal of robust optimization is to find a solution that is both optimal and insensitive to uncertainty that may exist in parameters and design variables. In this paper, a novel approach, sequential quadratic programming for robust optimization (SQP-RO), is proposed to solve single-objective continuous nonlinear optimization problems with interval uncertainty in parameters and design variables. This new SQP-RO is developed based on a classic SQP procedure with additional calculations for constraints on objective robustness, feasibility robustness, or both. The obtained solution is locally optimal and robust. Eight numerical and engineering examples with different levels of complexity are utilized to demonstrate the applicability and efficiency of the proposed SQP-RO with the comparison to its deterministic SQP counterpart and RO approaches using genetic algorithms. The objective and/or feasibility robustness are verified via Monte Carlo simulations.

scholarly journals AntNet: Distributed Stigmergetic Control for Communications Networks

1998 ◽  
Vol 9 ◽  
pp. 317-365 ◽  
Author(s):  
G. Di Caro ◽  
M. Dorigo
Keyword(s):  
Adaptive Learning ◽  
Mobile Agents ◽  
Optimization Problems ◽  
State Of The Art ◽  
Routing Algorithms ◽  
Superior Performance ◽  
Experimental Conditions ◽  
Communications Networks ◽  
Novel Approach ◽  
Routing Tables

This paper introduces AntNet, a novel approach to the adaptive learning of routing tables in communications networks. AntNet is a distributed, mobile agents based Monte Carlo system that was inspired by recent work on the ant colony metaphor for solving optimization problems. AntNet's agents concurrently explore the network and exchange collected information. The communication among the agents is indirect and asynchronous, mediated by the network itself. This form of communication is typical of social insects and is called stigmergy. We compare our algorithm with six state-of-the-art routing algorithms coming from the telecommunications and machine learning fields. The algorithms' performance is evaluated over a set of realistic testbeds. We run many experiments over real and artificial IP datagram networks with increasing number of nodes and under several paradigmatic spatial and temporal traffic distributions. Results are very encouraging. AntNet showed superior performance under all the experimental conditions with respect to its competitors. We analyze the main characteristics of the algorithm and try to explain the reasons for its superiority.

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