Optimum Design of Local Cam Profile of a Valve Train

Qin, Wenjie; He, Jim

doi:10.1243/09544062jmes2116

Cited by 13 publications

(6 citation statements)

References 10 publications

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“…A kriging metamodel-based multi-objective optimization strategy has been employed to optimize the valve-plate shape of the axial piston pump [32]. The kriging method was also used to build a surrogate model, which presents the relationship between dynamic responses and dynamic simulation of the valve train [33]. Many of the works where the kriging model has been adopted for error calculation mainly focus on the calculation errors associated with replacing the original analytical model with the kriging-based surrogate model.…”

Section: Introductionmentioning

confidence: 99%

Improved Method for Distributed Parameter Model of Solenoid Valve Based on Kriging Basis Function Predictive Identification Program

et al. 2021

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In this paper, a method for the improvement of the calculation accuracy of the distributed parameter model (DPM) of electromagnetic devices is proposed based on the kriging basis function predictive identification program (PIP). Kriging is mainly an optimal interpolation method which uses spatial self-covariance, and takes a polynomial as the basis function. The accuracy of the kriging-based surrogate model can be improved by adjusting the related functions and hyperparameters. Based on the DPM of a solenoid valve, there exist certain errors in the estimation. They can be summarized as follows: Firstly, the estimation error of magnetic flux leakage (MFL) permeance is caused directly by the deviation of the magnetic flux tube due to the segmented magnetic field line. Secondly, the estimation error of soft magnetic resistance because of the nonlinearity of the permeability of soft magnetic material leads to the change of soft magnetic resistance alongside the magnetic flux. In this paper, an improved kriging error correction method is applied to modify the leak permeance and soft magnetic resistance calculation. The kriging basis function is adjusted to adapt to the data curve of the MFL permeance error data. The calculated MFL permeance data are compared with the error variation data to select the appropriate basis function. To improve the computational efficiency, the PIP is proposed to select the appropriate basis function. The modified MFL permeance data and soft magnetic resistance are substituted into the DPM for improving the computational accuracy and efficiency of the solenoid valve.

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Section: Introductionmentioning

confidence: 99%

Improved Method for Distributed Parameter Model of Solenoid Valve Based on Kriging Basis Function Predictive Identification Program

et al. 2021

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“…Depending on the correlation function, the model can either ''honor the data'' providing an exact interpolation of the data, or ''smooth the data'' providing an inexact interpolation. 18,19 Kriging models are extremely Fexible because a wide range of correlation functions can be chosen in the metamodel building, 19,20 which attract the interests of many authors in the field of space shuttle, 11 wing structures, 21 structural safety, 18 touch probe radius compensation for coordinate measurement in CMMs, 22 sheet-metal-forming processes, designing local cam profile, 23 robust structural design, 24,25 sensitivity of the probabilistic constraint, 26 relationship between the contact pressure and crack width, 27 and path-generating linkage. 28 In this paper, Kriging approximation method is used to evaluate position-dependent natural frequencies for a machine tool and the relative position between the tool and the workpiece during the machining process as well.…”

Section: Introductionmentioning

confidence: 99%

Dynamic optimization method with applications for machine tools based on approximation model

Ding

Cheng

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part C:

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Natural frequencies and modal shapes of machine tools have position-dependent characteristics owing to their dynamic behaviors changing with the positions of moving parts. It is time-consuming and difficult to evaluate the dynamic behaviors of machine tools and their machining accuracy at different positions. In this paper, a Kriging approximation model coupled with finite element method is proposed to substitute the dynamic equations for obtaining the position-dependent natural frequencies of a machine tool, as well as relative positions between the tool and the workpiece during the machining process. Based on the proposed method, dynamic performance optimization design of the machine tool is conducted under the condition of minimum relative positions. Three case studies are illustrated to demonstrate the implementation of the proposed method.

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“…In the view of the past studies on cam profile optimization, a number of traditional and evolutionary optimization methods have been adopted by researchers to improve the kinematic or dynamic characteristics of cam–follower mechanism. For traditional optimization methods, the golden section method, 12 Kriging method, 13 and Lagrange multipliers method 14 were applied in the optimum design of cam profile. On the other hand, some scholars eagerly focused on genetic algorithm, 15 particle swarm technique, 16 and multiobjective optimization 17 to deal with the optimization problem recently.…”

Section: Introductionmentioning

confidence: 99%

Cam profile optimization for the delivery system of an offset press

Ouyang

Wang

Huang

2016

Proceedings of the Institution of Mechanical Engineers, Part C:

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In this paper, a relatively new strategy known as unified optimization is applied to the cam profile optimization for the delivery system of an offset press by integrating the procedure of single objective optimization with ADAMS software. The proposed approach mainly consists of two parts that includes a single objective optimization procedure and a multibody dynamic model of cam–follower mechanism. For the procedure of single objective optimization, the design process starts from defining the follower acceleration profile by using a modified trapezoidal curve, then genetic algorithm is adopted to determine the evaluating indexes for the kinematic behavior of cam–follower mechanism in multiobjective optimization. Subsequently, sequential quadratic programming, which deals well with equality and inequality constrains, is selected as single objective optimized algorithm in this part. On the other hand, the dynamic simulation developed by ADAMS software is carried out to investigate the dynamic characteristics of cam–follower mechanism. Finally, an optimization cycle, also known as iterative process, is proposed to implement the procedure of single objective optimization and dynamic simulation alternately to improve the kinematic and dynamic characteristics of cam–follower mechanism fully. The cam profile optimization method presented in this paper provides a new tool for cam designers to avoid the undesirable impact and follower jump.

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Optimum Design of Local Cam Profile of a Valve Train

Cited by 13 publications

References 10 publications

Improved Method for Distributed Parameter Model of Solenoid Valve Based on Kriging Basis Function Predictive Identification Program

Improved Method for Distributed Parameter Model of Solenoid Valve Based on Kriging Basis Function Predictive Identification Program

Dynamic optimization method with applications for machine tools based on approximation model

Cam profile optimization for the delivery system of an offset press

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