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Journal articles on the topic 'Parameter optimization'

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Author: Grafiati

Published: 4 June 2021

Last updated: 29 January 2023

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1

Rajeanderan Revichandran, Jaffar Syed Mohamed Ali, Moumen Idres, and A. K. M. Mohiuddin. "A Review of HVAC System Optimization and Its Effects on Saving Total Energy Utilization of a Building." Journal of Advanced Research in Fluid Mechanics and Thermal Sciences 93, no. 1 (March 25, 2022): 64–82. http://dx.doi.org/10.37934/arfmts.93.1.6482.

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The paper illustrates the review on the optimizations studies of HVAC systems based on three main methods – HVAC operational variables optimization, optimization of control parameters in HVAC system and parameter optimization in building models. For the HVAC system’s operational variables, the optimization process is based on the common and prescient energy utilization models. Thus, by comparing both, the non-common HVAC system models can get better output of energy reduction. Based on most of the studies, the occupancies thermal comfort requirements, are represented by the indoor air quality (IAQ) or the predicted mean vote (PMV) indexes. Comparing both requirements, the PMV index had a better overall energy reduction output of 47% and estimated annual energy reduction of 2,769 kg/year. Meanwhile, in optimization of HVAC’s control parameters, its overall aim is to achieve a better response output of the HVAC system in order to prevent energy wastage. Among this different optimization’s controller, the fuzzy logic tuning optimization has a better overall energy reduction. On the other hand, the parameter optimization in building model approach is performed before the construction of the structure itself, where multiple construction parameters are considerations in the design. In overall, when different tools for building parameter and model optimization are compared, the EXRETopt by using PMV comfort index approximately reduces 62% of the energy utilization.

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Zhong, Mei Peng. "Parameter Optimization of Compressor Based on an Ant Colony Optimization." Applied Mechanics and Materials 201-202 (October 2012): 916–19. http://dx.doi.org/10.4028/www.scientific.net/amm.201-202.916.

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A mathematical model of operation on air compressors is set up in order to improve the efficiency of air compressors. Parameter of Compressor is optimized by an Ant Colony Optimization (ACO) Particle approach. Volume and its weight of the new compressor are little, and its efficiency is high. An Ant Colony Optimization embed BLDCM module which optimizating the air compressor was put forward. Optimizated target of an Ant Colony Optimization is the efficiency of BLDCM. Optimizated variables are the diameter of low pressure cylinder, the diameter of high pressure cylinder, the journey of low pressure piston, the journey of high pressure piston and the rotate speed of BLDCM. Simulated result shows that the efficiency of BLDCM is more than that before optimizating. The test is done. The result shows that the specifc Power of air compressor is much less than before optimizating on 2.5Mpa. The result also shows that an Ant Colony Optimization which optimizating the air compressor is availability and practicality.

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3

Prinz, Astrid. "Neuronal parameter optimization." Scholarpedia 2, no. 1 (2007): 1903. http://dx.doi.org/10.4249/scholarpedia.1903.

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Ng, Chuan Huat, and Mohd Khairulamzari Hamjah. "Welding Parameter Optimization of Surface Quality by Taguchi Method." Applied Mechanics and Materials 660 (October 2014): 109–13. http://dx.doi.org/10.4028/www.scientific.net/amm.660.109.

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An experimental study of GTAW was conducted to determine the optimization of weld parameters on the droplet formation in the surface quality of weld pools. These optimization investigations consisted of welding current, welding speed and feed rate. The strength and surface quality of weld pool were measured for each specimen after the welding parameter optimizations and the effect of these parameters on droplet formation were researched. To consider these quality characteristics together in the selection of welding parameters, the Orthogonal Array of Taguchi method is adopted to analyze the effect of each welding parameter on the weld pool quality, and then to determine the welding parameters with the optimal weld pool quality.

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Niu, Xiang Jie. "The Optimization for PID Controller Parameters Based on Genetic Algorithm." Applied Mechanics and Materials 513-517 (February 2014): 4102–5. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.4102.

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as an important research field of automatic control problems, PID parameter optimization's control effect depends on the proportional, integral and derivative values. Using trial and error testing to manually realize optimization PID parameters, the traditional ways are often time-consuming and difficult to meet the requirements of real-time control. In order to solve the problems and improve system performance, the paper proposes a PID parameter optimization strategy based on genetic algorithm. The paper establishes the PID controller parameter model through genetic algorithm, uses the PID parameters as individuals in genetic algorithm during the control process, and takes the integral function of absolute error control time as the optimization object to dynamically adjust the three PID control parameters, thus realize online optimization for PID control parameters. Simulation results show that the introduction of genetic algorithms for PID control system improves the dynamic performance, enhance system stability and operation speed, and get better control effect.

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Adil, H., A. A. Koser, M. S. Qureshi, and A. Gupta. "Sleep quality assessment by parameter optimization." Journal of Physics: Conference Series 2070, no. 1 (November 1, 2021): 012013. http://dx.doi.org/10.1088/1742-6596/2070/1/012013.

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Abstract Sleep quality measurement is a complex process requires large number of parameters to monitor sleep and sleep cycles. The Gold Standard Polysomnography (PSG) parameters are considered as standard parameters for sleep quality measurement. In the PSG process, number of monitoring parameters are involved for that large number of sensors are used which makes this process complex, expensive and obtrusive. There is need to find optimize parameters which are directly involve in providing accurate information about sleep and reduce the process complexity. Our Parameter Optimization method is based on parameter reduction by finding key parameters and their inter dependent parameters. Sleep monitoring by these optimize parameter is different from both, clinical complex (PSG) used in hospitals and commercially available devices which work on dependent and dynamic parameter sensing. Optimized parameters obtained from PSG parameters are Electrocardiogram (ECG), Electrooculogram (EOG), Electroencephalography (EEG) and Cerebral blood flow (CBF). These key parameters show close correlation with sleep and hence reduce complexity in sleep monitoring by providing simultaneous measurement of appropriate signals for sleep analysis.

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7

Kitamura, Makoto, Masahiro Umeda, Toshihiro Higuchi, Shouji Naruse, and Chuuzou Tanaka. "465. Optimization of a parameter in functional-MRI paramete." Japanese Journal of Radiological Technology 50, no. 8 (1994): 1380. http://dx.doi.org/10.6009/jjrt.kj00003326264.

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8

Vohra, Nilesh M. "Optimization of Cutting Parameter in Edm Using Taguchi Method." International Journal of Scientific Research 2, no. 1 (June 1, 2012): 90–95. http://dx.doi.org/10.15373/22778179/jan2013/32.

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9

Zala, Cedric A., and John M. Ozard. "Estimation of Geoacoustic Parameters from Narrowband Data Using a Search-Optimization Technique." Journal of Computational Acoustics 06, no. 01n02 (March 1998): 223–43. http://dx.doi.org/10.1142/s0218396x98000168.

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Geoacoustic parameters were estimated for vertical array data from the matched-field inversion benchmark data sets. Separate inversions were performed for narrowband data at 25 Hz, 50 Hz and 75 Hz, using a matching function consisting of the incoherent sum of the Bartlett outputs for the five vertical arrays at ranges of 1, 2, 3, 4 and 5 km. Parameter estimation was performed using a parabolic equation sound propagation algorithm to generate the replica fields, and a search-optimization technique to obtain estimates of the optimized parameter values. This technique involved an initial search stage in which the parameter space was sampled, and a second optimization stage in which each of a specified number of the best matches found in the search stage was used as the starting point for optimization. This approach provided multiple independent estimates of the geoacoustic parameters, and allowed assessment of the non-uniqueness of the problem and the sensitivity of the matching function to the individual parameters. A method was developed to combine the results for several frequencies to estimate the parameters. It used a weighted average with weights computed on the basis of the relative sensitivities at those frequencies; these sensitivities were estimated by the root-mean-square (RMS) gradient observed during the optimizations. Strong interdependencies among the parameters were found in the analysis, particularly between the sediment thickness and the sound speed at the bottom of the sediment. For the single-frequency matching function used here, it was observed that the inversion problems were ill-posed in that sets of parameter values from a wide region of the parameter space gave essentially perfect matches. The consistency of the parameter estimates was greatly improved by including a regularization term in the matching function. Regularized search-optimization provided an efficient method for estimating an effective geoacoustic model for acoustic field prediction.

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INOUE, Kazuya, and Mariko SUZUKI. "PARAMETER OPTIMIZATION USING SWARM INTELLIGENCE." Journal of Japan Society of Civil Engineers, Ser. A2 (Applied Mechanics (AM)) 74, no. 2 (2018): I_33—I_44. http://dx.doi.org/10.2208/jscejam.74.i_33.

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11

VILORIA, EDGAR. "Wire bond parameter optimization study." Journal of Electronics Manufacturing 04, no. 04 (December 1994): 217–22. http://dx.doi.org/10.1142/s0960313194000237.

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12

Amyot, Joseph R., and Gerard van Blokland. "Parameter optimization with ACSL models." SIMULATION 49, no. 5 (November 1987): 213–18. http://dx.doi.org/10.1177/003754978704900505.

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A method whereby a parameter optimization program, written in FORTRAN, can be used in conjunction with ACSL (Advanced Continuous Simulation Language) models of dynamic systems is described. The optimization of a projectile's trajectory is used as an example.

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Amyot, Joseph R., and van Blokland Gerard. "Parameter Optimization with ACSL Models." SIMULATION 49, no. 5 (November 1987): 213–18. http://dx.doi.org/10.1177/003754978904900505.

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14

Hestermeyer, Thorsten, Eckehard Münch, and Erika Schäfer. "Model-Based Online Parameter Optimization." IFAC Proceedings Volumes 37, no. 14 (September 2004): 193–98. http://dx.doi.org/10.1016/s1474-6670(17)31103-5.

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15

Valente, Robertt A. F., António Andrade-Campos, José F. Carvalho, and Paulo S. Cruz. "Parameter identification and shape optimization." Optimization and Engineering 12, no. 1-2 (November 12, 2010): 129–52. http://dx.doi.org/10.1007/s11081-010-9126-y.

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16

Seo, Y. K., S. Yu, and A. Gafurov. "CV-joint remanufacturing parameter optimization." International Journal of Automotive Technology 15, no. 4 (May 28, 2014): 603–10. http://dx.doi.org/10.1007/s12239-014-0063-1.

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17

Szczerbicka, Rainer Barton, Helena. "INDUCTIVE LEARNING FOR PARAMETER OPTIMIZATION." Cybernetics and Systems 31, no. 5 (July 2000): 469–90. http://dx.doi.org/10.1080/01969720050045985.

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18

Kout, Alexander, and Heinrich Müller. "Parameter optimization for spray coating." Advances in Engineering Software 40, no. 10 (October 2009): 1078–86. http://dx.doi.org/10.1016/j.advengsoft.2009.03.001.

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19

Yum, Bong-Jin, and Sun-Woo Ko. "On parameter design optimization procedures." Quality and Reliability Engineering International 7, no. 1 (January 1991): 39–46. http://dx.doi.org/10.1002/qre.4680070110.

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20

Pan, Jeng-Shyang, Cheng Yang, Fanjia Meng, Yuxin Chen, and Zhenyu Meng. "A parameter adaptive DE algorithm on real-parameter optimization." Journal of Intelligent & Fuzzy Systems 38, no. 5 (May 29, 2020): 5775–86. http://dx.doi.org/10.3233/jifs-179665.

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21

Radke, F., and R. Isermann. "A parameter-adaptive PID-controller with stepwise parameter optimization." Automatica 23, no. 4 (July 1987): 449–57. http://dx.doi.org/10.1016/0005-1098(87)90074-4.

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22

Luan, Nguyen Duc, Nguyen Duc Minh, and Le Thi Phuong Thanh. "Multi-Objective Optimization of PMEDM Process Parameter by Topsis Method." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (June 30, 2019): 112–15. http://dx.doi.org/10.31142/ijtsrd23169.

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23

Toma, Eiji. "Optimization of the Resin Injection Molding Process using Parameter Design." Journal of the Institute of Industrial Applications Engineers 5, no. 3 (July 25, 2017): 150–55. http://dx.doi.org/10.12792/jiiae.5.150.

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24

Jiang, Jiawei, Yanhong Wu, Hongyan Wang, Yakun Lv, Lei Qiu, and Daobin Yu. "Optimization Algorithm for Multiple Phases Sectionalized Modulation Jamming Based on Particle Swarm Optimization." Electronics 8, no. 2 (February 1, 2019): 160. http://dx.doi.org/10.3390/electronics8020160.

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Due to the difficulty in deducing the corresponding relationship between results and parameter settings of multiple phases sectionalized modulation (MPSM) jamming, a problem occurs when obtaining the optimal local suppression jamming effect, which limits the practical application of MPSM jamming. The traditional method struggles to meet the requirements by setting fixed parameters or random parameters. Therefore, an optimization algorithm for MPSM jamming based on particle swarm optimization (PSO) is proposed in this study to produce the optimal local suppression jamming effect and determine its corresponding parameter settings. First, we analyzed the relationship between the degree of mismatch and local suppression jamming effect. Then, we set appropriate fitness function and fitness value. Finally, we used PSO to calculate parameter settings of a section situation and phase situation, which minimizes the fitness function and fitness value. The optimization algorithm avoids the tremendous computation of traversing all parameter settings, is stable, the results are repeatable, and the algorithm provides the optimal local suppression jamming effect under different conditions. The simulation experiments demonstrate the feasibility and effectiveness of the optimization algorithm.

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25

Lee, Yeon-Seung, and Young-Bok Choi. "Hull Form Optimization Based on From Parameter Design." Journal of the Society of Naval Architects of Korea 46, no. 6 (December 20, 2009): 562–68. http://dx.doi.org/10.3744/snak.2009.46.6.562.

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26

Zhu, Yuanyuan, Shijie Su, Yuchen Qian, Yun Chen, and Wenxian Tang. "Parameter Optimization for Ship Antiroll Gyros." Applied Sciences 10, no. 2 (January 16, 2020): 661. http://dx.doi.org/10.3390/app10020661.

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Ship antiroll gyros are a type of equipment used to reduce ships’ roll angle, and their parameters are related to the parameters of a ship and wave, which affect gyro performance. As an alternative framework, we designed a calculation method for roll reduction rate and considered random waves to establish a gyro parameter optimization model, and we then solved it through the bacteria foraging optimization algorithm (BFOA) and pattern search optimization algorithm (PSOA) to obtain optimal parameter values. Results revealed that the two methods could effectively reduce the overall mass and floor space of the antiroll gyro and improved its antirolling effect. In addition, the convergence speed and antirolling effect of the BFOA were better than that of the PSOA.

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27

Sumata, Hiroshi, Frank Kauker, Michael Karcher, and Rüdiger Gerdes. "Covariance of Optimal Parameters of an Arctic Sea Ice–Ocean Model." Monthly Weather Review 147, no. 7 (July 1, 2019): 2579–602. http://dx.doi.org/10.1175/mwr-d-18-0375.1.

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Abstract The uniqueness of optimal parameter sets of an Arctic sea ice simulation is investigated. A set of parameter optimization experiments is performed using an automatic parameter optimization system, which simultaneously optimizes 15 dynamic and thermodynamic process parameters. The system employs a stochastic approach (genetic algorithm) to find the global minimum of a cost function. The cost function is defined by the model–observation misfit and observational uncertainties of three sea ice properties (concentration, thickness, drift) covering the entire Arctic Ocean over more than two decades. A total of 11 independent optimizations are carried out to examine the uniqueness of the minimum of the cost function and the associated optimal parameter sets. All 11 optimizations asymptotically reduce the value of the cost functions toward an apparent global minimum and provide strikingly similar sea ice fields. The corresponding optimal parameters, however, exhibit a large spread, showing the existence of multiple optimal solutions. The result shows that the utilized sea ice observations, even though covering more than two decades, cannot constrain the process parameters toward a unique solution. A correlation analysis shows that the optimal parameters are interrelated and covariant. A principal component analysis reveals that the first three (six) principal components explain 70% (90%) of the total variance of the optimal parameter sets, indicating a contraction of the parameter space. Analysis of the associated ocean fields exhibits a large spread of these fields over the 11 optimized parameter sets, suggesting an importance of ocean properties to achieve a dynamically consistent view of the coupled sea ice–ocean system.

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Rao, R. V., and P. J. Pawar. "Grinding process parameter optimization using non-traditional optimization algorithms." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 224, no. 6 (November 18, 2009): 887–98. http://dx.doi.org/10.1243/09544054jem1782.

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Hao, Shangqing, Xuewen Wang, Jiacheng Xie, and Zhaojian Yang. "Rigid framework section parameter optimization and optimization algorithm research." Transactions of the Canadian Society for Mechanical Engineering 43, no. 3 (September 1, 2019): 398–404. http://dx.doi.org/10.1139/tcsme-2018-0085.

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This article compares the optimization algorithms included with ANSYS Software for optimizing the dimensions of a large steel framework to minimize weight while maintaining stiffness. A finite element model of the structure was prepared, and the section parameters were optimized using the sub-problem and first-order algorithms. These reduce the weight of the structure by 33.8%. The sub-problem algorithm and the first-order algorithm are explained from the rationale, iteration method, and convergence criterion. According to the optimized result, these two algorithms were compared. The results show that the sub-problem algorithm is faster and can control the overall design space, and the first-order algorithm is more precise.

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Chenthil Jegan, Thankaraj Mariapushpam, and Durairaj Ravindran. "Electrochemical machining process parameter optimization using particle swarm optimization." Computational Intelligence 33, no. 4 (August 30, 2017): 1019–37. http://dx.doi.org/10.1111/coin.12139.

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31

Zhang, Lixiu, Da Teng, and Qiang Shen. "Parameter Optimization of Drilling Based on Biogeography-based Optimization." IOP Conference Series: Earth and Environmental Science 170 (July 2018): 022180. http://dx.doi.org/10.1088/1755-1315/170/2/022180.

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Wu, Dongmei, and Hao Gao. "An Adaptive Particle Swarm Optimization for Engine Parameter Optimization." Proceedings of the National Academy of Sciences, India Section A: Physical Sciences 88, no. 1 (December 21, 2016): 121–28. http://dx.doi.org/10.1007/s40010-016-0320-y.

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Sumata, H., F. Kauker, R. Gerdes, C. Köberle, and M. Karcher. "A comparison between gradient descent and stochastic approaches for parameter optimization of a sea ice model." Ocean Science 9, no. 4 (July 9, 2013): 609–30. http://dx.doi.org/10.5194/os-9-609-2013.

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Abstract. Two types of optimization methods were applied to a parameter optimization problem in a coupled ocean–sea ice model of the Arctic, and applicability and efficiency of the respective methods were examined. One optimization utilizes a finite difference (FD) method based on a traditional gradient descent approach, while the other adopts a micro-genetic algorithm (μGA) as an example of a stochastic approach. The optimizations were performed by minimizing a cost function composed of model–data misfit of ice concentration, ice drift velocity and ice thickness. A series of optimizations were conducted that differ in the model formulation ("smoothed code" versus standard code) with respect to the FD method and in the population size and number of possibilities with respect to the μGA method. The FD method fails to estimate optimal parameters due to the ill-shaped nature of the cost function caused by the strong non-linearity of the system, whereas the genetic algorithms can effectively estimate near optimal parameters. The results of the study indicate that the sophisticated stochastic approach (μGA) is of practical use for parameter optimization of a coupled ocean–sea ice model with a medium-sized horizontal resolution of 50 km × 50 km as used in this study.

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Huang, Shixin, Kedao Zhang, Hongmei Li, and Xiangjian Chen. "Application of Improved Monarch Butterfly Optimization for Parameters’ Optimization." Mathematical Problems in Engineering 2023 (January 11, 2023): 1–10. http://dx.doi.org/10.1155/2023/1348624.

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The reasonable selection of cutting parameters in the machining process is of great significance to improve productivity, reduce production costs, and improve the quality of parts. However, due to the complexity of cutting parameter model optimization, most factories currently use experience or refer to relevant manuals to select the value of cutting parameters in production. In order to avoid and minimize abnormalities, they usually select more experienced and conservative values, and often do not select reasonable cutting parameters, which is not conducive to improving productivity, reducing production costs, and improving the quality of parts. Therefore, the research on cutting parameter optimization has important theoretical value and application value. In this paper, in order to find the optimal cutting parameters, the cutting model is solved by the improved monarch butterfly optimization (IMBO) algorithm, and the optimized cutting parameters are obtained. By establishing the mathematical model of cutting, the constraint conditions of actual machining are introduced into the model. In order to solve the model, some ideas of particle swarm optimization (PSO) and differential evolution (DE) are added to the traditional monarch butterfly optimization (MBO) algorithm. The MBO algorithm is improved to deal with multiobjective optimization problems. The IMBO algorithm is used to optimize the cutting model. The experiment shows that the optimized cutting parameters can significantly reduce production cost and maintain high production efficiency. Compared with NSGA-II algorithm and other swarm intelligence optimization algorithms, it shows that the IMBO algorithm has certain advantages in multiobjective optimization.

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Méndez, Maikel, José A. Araya, and Luís D. Sánchez. "Automated parameter optimization of a water distribution system." Journal of Hydroinformatics 15, no. 1 (September 11, 2012): 71–85. http://dx.doi.org/10.2166/hydro.2012.028.

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The hydraulic model EPANET was applied and calibrated for the water distribution system (WDS) of La Sirena, Colombia. The Parameter ESTimator (PEST) was used for parameter optimization and sensitivity analysis. Observation data included levels at water storage tanks and pressures at monitoring nodes. Adjustable parameters were grouped into different classes according to two different scenarios identified as constrained and unconstrained. These scenarios were established to evaluate the effect of parameter space size and compensating errors over the calibration process. Results from the unconstrained scenario, where 723 adjustable parameters were declared, showed that considerable compensating errors are introduced into the optimization process if all parameters were open to adjustment. The constrained scenario on the other hand, represented a more properly discretized scheme as parameters were grouped into classes of similar characteristics and insensitive parameters were fixed. This had a profound impact on the parameter space as adjustable parameters were reduced to 24. The constrained solution, even when it is valid only for the system's normal operating conditions, clearly demonstrates that Parallel PEST (PPEST) has the potential to be used in the calibration of WDS models. Nevertheless, further investigation is needed to determine PPEST's performance in complex WDS models.

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Li, Bao Dong. "Cutting Parameters Optimization Based on Radial Basis Function Neural Network and Particle Swarm Optimization." Advanced Materials Research 335-336 (September 2011): 1473–76. http://dx.doi.org/10.4028/www.scientific.net/amr.335-336.1473.

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A technique of cutting parameters optimization based on radial basis function neural networks and partical swarm optimization is presented in the paper. Taking experimental data as samples, the model between processing parameter and processing function was established based on radial basis function neural networks. Then, the cutting parameters is optimized by particle swarm optimization. With the combination of radial basis function neural network and particle swarm optimization, and making good use of the respective virtues,the model was solved.The experiment shows that the actual output as same as the predictive output and the mixes algorithm can realize optimization of cutting parameter real time in workplace.

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Li, Guang Chun, Ping Yan, and Sui Er Wang. "Study on Optimal Method for PID Parameter Based on Artificial Bee Colony Algorithm." Applied Mechanics and Materials 624 (August 2014): 454–59. http://dx.doi.org/10.4028/www.scientific.net/amm.624.454.

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The PID parameter optimization has been a hot topic of control system research. Artificial bee colony algorithm is used to optimize the PID parameters of control system, and gives the PID parameter optimization process of the traditional artificial bee colony algorithm. Aiming at the optimization problem of long time, the improved artificial bee colony algorithm was put forward. The simulation results show that the improved artificial bee colony algorithm can significantly shorten the time of PID parameter optimization, control and better optimization accuracy.

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Stori, J. A., P. K. Wright, and C. King. "Integration of Process Simulation in Machining Parameter Optimization." Journal of Manufacturing Science and Engineering 121, no. 1 (February 1, 1999): 134–43. http://dx.doi.org/10.1115/1.2830565.

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In recent years, simulation tools have proven valuable for the prediction of machining state variables over a wide range of operating parameters. Such simulation packages, however, are seldom an integral part of machining parameter optimization modules. This paper proposes a methodology for incorporating simulation feedback to fine-tune analytic models during the optimization process. Through a limited number of calls to the computationally expensive simulation tools, process parameters may be generated that satisfy the design constraints within the accuracy of the simulation predictions, while providing an efficient balance among parameters arising from the functional form of the optimization model. The following iterative algorithm is presented: (i) a non-linear programming (NLP) optimization technique is used to select process parameters based on closed-form analytical constraint equations relating to critical design requirements, (ii) the simulation is executed using these process parameters, providing predictions of the critical state variables. (iii) Constraint equation parameters are dynamically adapted using the feedback provided by the simulation predictions. This sequence is repeated until local convergence between the simulation and constraint equation predictions has been achieved. A case study in machining parameter optimization for peripheral finish milling operations is developed in which constraints on the allowable form error,Δ and the peripheral surface roughness, Ra, drive the process parameter selection for a cutting operation intended to maximize the material removal rate. Results from twenty machining scenarios are presented, including five workpiece/tool material combinations at four levels of precision. Achieving agreement (within a 5% deviation tolerance) between the simulation and constraint equation predictions required an average of 5 simulation execution cycles (maximum of 8), demonstrating promise that simulation tools can be efficiently incorporated into parameter optimization processes.

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Lv, Ying Hui, Xia Ting Feng, and Jun Yan Liu. "A New Optimization Algorithm for Identification of Material Parameter." Advanced Materials Research 217-218 (March 2011): 1108–12. http://dx.doi.org/10.4028/www.scientific.net/amr.217-218.1108.

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It has gained some popularity that optimization methods are used for identification of material parameters, furthermore because of non-linear relationship between identified parameters and foregone information, mostly parameter identification problem must be expressed in terms of a global optimization problem. In order to solve successfully non-linear parameter identification problem, a new global optimization algorithm, which is based on the general dynamic canonical descent method, is proposed. The results in numerical experiments and engineering application both show that the proposed method will be robust one in the field of non-linear parameter identification.

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Choi, Tae Jong, Chang Wook Ahn, and Jinung An. "An Adaptive Cauchy Differential Evolution Algorithm for Global Numerical Optimization." Scientific World Journal 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/969734.

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Adaptation of control parameters, such as scaling factor (F), crossover rate (CR), and population size (NP), appropriately is one of the major problems of Differential Evolution (DE) literature. Well-designed adaptive or self-adaptive parameter control method can highly improve the performance of DE. Although there are many suggestions for adapting the control parameters, it is still a challenging task to properly adapt the control parameters for problem. In this paper, we present an adaptive parameter control DE algorithm. In the proposed algorithm, each individual has its own control parameters. The control parameters of each individual are adapted based on the average parameter value of successfully evolved individuals’ parameter values by using the Cauchy distribution. Through this, the control parameters of each individual are assigned either near the average parameter value or far from that of the average parameter value which might be better parameter value for next generation. The experimental results show that the proposed algorithm is more robust than the standard DE algorithm and several state-of-the-art adaptive DE algorithms in solving various unimodal and multimodal problems.

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Rawal, Manjunath R., Vaibhav Varane, and Rakesh R. Kolhapure. "Process Parameter Optimization for Resistance Spot Welding using Response Surface Methdology." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 1078–82. http://dx.doi.org/10.31142/ijtsrd23151.

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Suksut, Keerachart, Nuntawut Kaoungku, Nittaya Kerdprasop, and Kittisak Kerdprasop. "Parameter Optimization with Restarting Genetic Algorithm for the Forest Type Classification." International Journal of Machine Learning and Computing 7, no. 6 (December 2017): 213–17. http://dx.doi.org/10.18178/ijmlc.2017.7.6.649.

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Onuki, Ryosuke, Satoshi Kitayama, and Koetsu Yamazaki. "CO-JP-5 Optimization of Process Parameter in Plastic Injection Molding." Proceedings of Mechanical Engineering Congress, Japan 2012 (2012): _CO—JP—5–1—_CO—JP—5–4. http://dx.doi.org/10.1299/jsmemecj.2012._co-jp-5-1.

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DAI, Chunhui, Xinyu WEI, Suxia HOU, yun TAI, and Fuyu ZHAO. "ICONE19-43021 PARAMETER DESIGN AND OPTIMIZATION OF TIGHT-LATTICE ROD BUNDLES." Proceedings of the International Conference on Nuclear Engineering (ICONE) 2011.19 (2011): _ICONE1943. http://dx.doi.org/10.1299/jsmeicone.2011.19._icone1943_8.

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Seppänen, Henri. "Parameter Optimization for Unstable Pin Bonding." International Symposium on Microelectronics 2020, no. 1 (September 1, 2020): 000160–64. http://dx.doi.org/10.4071/2380-4505-2020.1.000160.

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Abstract Plastic frame power modules with press-fitted or molded pins create challenging bonding conditions. Pin stability variations, from relatively stable to unstable within a single power module, makes it difficult to find process parameters that fit to all pins equally well. Also, tight space, full utilization of the pin surface for bonding and a large number of pins per module makes clamping solutions difficult or impractical. Therefore, we used process optimization to find optimal process parameters for the unstable pin bonds and active process control feature to reduce deformation variance in bonding. The study revealed the importance of the cleaning phase optimization for both sides of the pin stability variations. We found that ensuring a good cleaning phase, typically within first 10ms of the process on the unstable pins, significantly improved the quality of the bonds. Unstable pins tended to lift after bonding with traditional parameters, but demonstrated good shear strength with optimized parameters. Active process control ensured that all bonds reached optimal deformation, regardless of the pin stability. Generally, the best approach to reach good bond quality is to ensure an optimal bonding environment, including clean and stable bond pads. However, when it is not possible or practical to stabilize the bond pad, this study shows that carefully executed process optimization combined with the active process control can lead to robust bonding on unstable pins.

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Li, Rui, Peng Fei Du, Si Min Zhou, and Tai Xiong Zheng. "Parameter Optimization Experiment of Magnetorheological Bearing." Applied Mechanics and Materials 457-458 (October 2013): 597–601. http://dx.doi.org/10.4028/www.scientific.net/amm.457-458.597.

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The active bearings with variable damping and stiffness are important for vibration isolation in wide frequency excitation. A model of vertical isoaltion system via magnetorheological (MR) bearings was built. Based on analyzing the characteristics of MR grease and MR elastomer, an MR parallel-isolator was designed to instead the passive bearing.Then an adaptive particle swarm optimization (PSO) was adopted to adjust the parameters of MR bearing. A vertical MR isoaltion experiment was proposed. Results demonstrate that MR bearings have better performance than passive bearing in concurrently restraining force transmissibility and vibration acceleration.

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ITAMI, Teturo. "Quantum Tunneling Parameter in Global Optimization." Transactions of the Society of Instrument and Control Engineers 46, no. 6 (2010): 336–45. http://dx.doi.org/10.9746/sicetr.46.336.

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Lee, Jaehyun, In Soo Ko, Jang-Hui Han, Juho Hong, Haeryong Yang, Chang Ki Min, and Heung-Sik Kang. "Parameter Optimization of PAL-XFEL Injector." Journal of the Korean Physical Society 72, no. 10 (May 2018): 1158–65. http://dx.doi.org/10.3938/jkps.72.1158.

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Wen, X. C., and W. Y. Leong. "Hidden Defects Diagnosis Using Parameter Optimization." Applied Mechanics and Materials 152-154 (January 2012): 1691–97. http://dx.doi.org/10.4028/www.scientific.net/amm.152-154.1691.

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In this paper, the issue of composite defects diagnosis by applying the support vector machine (SVM) was addressed. The component analysis was performed initially to extract the features and to reduce the dimensionality of original data features. Kernel parameters selection of support vector machine which has great influence on the performance of defects classification has been discussed in this work. Precisely, we focus on wavelet transform to extract the feature from the original signals, adopt component analysis to do feature selection and apply support vector machine to classify the defects. This paper exploits the parameter optimization procedure to ensure the generalization ability of SVM. The result shows that multi-class SVM produces promising results and has the potential for use in fault diagnosis.

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Ahmmed, Tanvir, Irin Akhter, S. M. Rezaul Karim, and F. A. Sabbir Ahamed. "Genetic Algorithm Based PID Parameter Optimization." American Journal of Intelligent Systems 10, no. 1 (December 16, 2020): 8–13. http://dx.doi.org/10.5923/j.ajis.20201001.02.

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