Applying particle swarm optimization algorithm to roundness error evaluation based on minimum zone circle

Du, Chenglie; Luo, Chenxu; Han, Zhengtong; Zhu, Yunlong

doi:10.1016/j.measurement.2014.02.028

Cited by 41 publications

(28 citation statements)

References 15 publications

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“…It is observed that the roundness error obtained using TLBO algorithm decreases with increase in number of iterations and becomes stable after 100 iterations. It is also seen that both algorithms provide the roundness error same as obtained by Du et al (2014) and Wen et al (1999). Also, the algorithm specific parameter used in PSO such as inertia weight required a lot of trials to find the optimum values to be used for the evaluation of roundness error.…”

Section: Number Of Iterations Vs Roundness Errorsupporting

confidence: 80%

“…2 shows the minimum zone circle fitted for a roundness profile. (Du et al, 2014) Since the roundness error value being evaluated should be minimum, it becomes a minimization problem. Assuming the center of MZC as (a, b), the radial distance ri given in Eq.…”

Section: Minimum Zone Circle Methodsmentioning

confidence: 99%

“…This leads to the need for efficient algorithms in order to simplify and accelerate the computation of form errors (Muralikrishnan & Raja, 2009;Mekid & Vacharanukul, 2011). Du et al (2014) applied particle swarm optimization algorithm to evaluate roundness error based on MZC method using the data points given by Wen et al (1999). The performance of PSO algorithm was analyzed using inertia weight and two learning factors.…”

Section: Introductionmentioning

confidence: 99%

“…As this algorithm is not yet been tried to evaluate roundness error, it is attempted in this paper. The data points given by Jywe et al (1999) were used for this purpose and the results of TLBO algorithm were compared with the results of Du et al (2014).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Roundness error measurement using teaching learning based optimization algorithm and comparison with particle swarm optimization algorithm

Kumar¹,

Kumaar²,

Kameshwaranath³

et al. 2018

10.5267/j.ijdns

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Form deviation of machined components need to be controlled within the required tolerance values for proper assembly and to serve the intended functional requirements. Methods like minimum zone circle (MZC) method, minimum circumscribed circle (MCC) method, maximum inscribed circle (MIC) method and least square circle (LSC) method are used to evaluate roundness error. Roundness error evaluation includes collection of co-ordinate points on the surface of the component and measurement using any of the above methods. Since, manual measurement of roundness error from these co-ordinate points is time consuming and less accurate, use of algorithms is highly appreciated. A detailed study of various optimization techniques showed that all evolutionary and swarm intelligence-based optimization algorithms require common control parameters and algorithm specific parameters. Improper tuning of these parameters either increases the computational effort or results in local optimal solution. Teaching Learning Based Optimization (TLBO) algorithm is used in this work as it does not require any algorithm specific parameters for the evaluation of roundness error. The results obtained are then compared with the results of Particle Swarm Optimization (PSO) algorithm to know the merits and demerits of both the algorithms when used for form measurement.

show abstract

Section: Number Of Iterations Vs Roundness Errorsupporting

confidence: 80%

Section: Minimum Zone Circle Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Roundness error measurement using teaching learning based optimization algorithm and comparison with particle swarm optimization algorithm

Kumar¹,

Kumaar²,

Kameshwaranath³

et al. 2018

10.5267/j.ijdns

View full text Add to dashboard Cite

show abstract

“…Finaly, the rapid selection of iteration points with convex hull leads an efficient way to solve the minimax solution. Du [16] elaborated the standard PSO algorithm and theory analysis about the impact of value selection of some important parameters for the roundness deviation evaluation based on minimum zone circle. The above research studies reviewed have different solutions to the minimum zone circle evaluation, however, they mainly focus on the requirement that the optimization techniques is used to deal with the nonlinear problem of the MZCI.…”

Section: Introductionmentioning

confidence: 99%

Intersecting chord method for minimum zone evaluation of roundness deviation using Cartesian coordinate data

Fei

et al. 2015

Precision Engineering

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A simple method for estimating the roundness of minimum zone circle

Huang

Mei

Liu

et al. 2020

Materialwissenschaft Werkst

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With the rapid development of micromachining technology, the feature size of object parts becomes smaller whilst the required tolerance accuracy becomes higher. The geometry of these micro‐parts must be critically verified at sub‐micrometer or even nanometer accuracy in three dimensions to guarantee machining quality, such as the cylindrical parts. Therefore, the method for estimating roundness error plays an increasingly important role in machining. This paper focuses on the roundness estimation based on the minimum zone circle method in a Cartesian coordinate system. An asymptotic search method is also proposed to obtain the coordinates of the concentric centre of the minimum zone circle model and to calculate the roundness error. Comparison and simulation experiments are also conducted to test the performance of the proposed method. The results demonstrate that the proposed method is effective, reliable and can meet the requirements for roundness estimation.

show abstract

Applying particle swarm optimization algorithm to roundness error evaluation based on minimum zone circle

Cited by 41 publications

References 15 publications

Roundness error measurement using teaching learning based optimization algorithm and comparison with particle swarm optimization algorithm

Roundness error measurement using teaching learning based optimization algorithm and comparison with particle swarm optimization algorithm

Intersecting chord method for minimum zone evaluation of roundness deviation using Cartesian coordinate data

A simple method for estimating the roundness of minimum zone circle

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