Machining deformation of single-sided component based on finishing allowance optimization

Li, Xiaoyue; Li, Liang; Yang, Yan; Zhao, Guolong; He, Ning; Xiaocen, Ding; Shi, Yaowen; Fan, Longxin; Lan, Hui; Jamil, Muhammad

doi:10.1016/j.cja.2019.09.015

Cited by 30 publications

(10 citation statements)

References 37 publications

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“…By means of the three-point estimate method [24], the input variables with a high importance to the failure probability of machining distortion are obtained. Firstly, three estimate points (Xi (1) , Xi (2) , and Xi (3) ) are extracted from the input variables Xi; Secondly, standard locations and weights of three estimate points can be obtained by solving the following Eqs. ( 4)-( 9); Thirdly, the estimate values of δi are computed using Eq.…”

Section: Definition Of the Failure Probability Of Machining Distortionmentioning

confidence: 99%

“…where Xi (1) , Xi (2) , and Xi (3) represent estimate points of Xi; and 3 i X l ( ) are the standard locations of the three estimate points;…”

Section: Definition Of the Failure Probability Of Machining Distortionmentioning

confidence: 99%

“…Uncertainties occurring in the machining process of thin-walled parts will lead to uncertain performance, such as dimensional accuracy and surface finish quality. Some researchers proposed that precise settings for machining parameters [1,2], cutter path [3] and workpiece stiffness [4] are beneficial to obtain the desired performance. To evaluate the effects of uncertain factors on the machining results, uncertainty analysis has been successfully and widely applied in engineering.…”

Section: Introductionmentioning

confidence: 99%

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Influence of Uncertain Parameters on Machining Distortion of Thin-walled Parts

Qin²,

Tang³

et al. 2022

Preprint

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Thin-walled parts refer to lightweight structural parts comprised of thin plates and stiffeners. During the machining process of thin-walled parts, machining distortion often occurs due to uncertain factors such as varying stiffness, cutting force, cutting temperature, residual stress and other factors. This paper studied the minimization of the failure probability of machining distortion by controlling the uncertainties of inputs. For this, a fuzzy inference model for the machining system was proposed to determine the effects of uncertain factors on the machining distortion errors, which was composed of rule frame and result frame. In the rule frame, machining parameters, outline size, and wall thickness were used as inputs. In the result frame, linear stiffness, cutter path, as well as cutting force were taken as the input parameters. The values of machining distortion were the output, taken into a threshold function. Comprehensive matching was defined to measure the importance of uncertain inputs to outputs. Machining distortion will exceed the specification (failure) with the increase in comprehensive matching. Therefore, the comprehensive matching index evaluates the effects of the uncertainties on the machining distortion and quantify the effects of given uncertain parameters. Two engineering examples were employed to illustrate the accuracy and efficiency of the proposed approach. It revealed that the comprehensive matching of cutting force to the failure probability of machining distortion was the maximum, 0.040, which was 12 to 13 times greater than that of linear stiffness or cutter path.

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Section: Definition Of the Failure Probability Of Machining Distortionmentioning

confidence: 99%

“…where Xi (1) , Xi (2) , and Xi (3) represent estimate points of Xi; and 3 i X l ( ) are the standard locations of the three estimate points;…”

Section: Definition Of the Failure Probability Of Machining Distortionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Influence of Uncertain Parameters on Machining Distortion of Thin-walled Parts

Qin²,

Tang³

et al. 2022

Preprint

Self Cite

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“…Wang et al [ 13 ] established an analytical model for predicting the machining deformation of multi-frame components based on an energy method and minimized the deformation by optimizing the cutting parameters using a cornstarch suspension. Li et al [ 14 ] established a deformation prediction model between the initial residual stress and finishing allowance. They developed a linear-programming optimization model to optimize the overall machining deformation.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning method for machining deformation control based on meta-invariant feature space

Zhao

Liu

Zhao

et al. 2022

Vis. Comput. Ind. Biomed. Art

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Precise control of machining deformation is crucial for improving the manufacturing quality of structural aerospace components. In the machining process, different batches of blanks have different residual stress distributions, which pose a significant challenge to machining deformation control. In this study, a reinforcement learning method for machining deformation control based on a meta-invariant feature space was developed. The proposed method uses a reinforcement-learning model to dynamically control the machining process by monitoring the deformation force. Moreover, combined with a meta-invariant feature space, the proposed method learns the internal relationship of the deformation control approaches under different stress distributions to achieve the machining deformation control of different batches of blanks. Finally, the experimental results show that the proposed method achieves better deformation control than the two existing benchmarking methods.

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“…Machining distortion has become a major challenge faced by the aviation industry, which causes billions of dollars of direct economic losses and a large amount of social resources to be wasted every year [2]. The release and redistribution of initial residual stress in the blank is the main factor leading to machining distortion [3].…”

Section: Introductionmentioning

confidence: 99%

Machining Distortion Minimization of Monolithic Aircraft Parts Based on the Energy Principle

Fan

Tian

et al. 2020

Metals

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Machining distortion is a recurring problem in the machining of monolithic aircraft parts. This paper aims to study the machining distortion minimization of monolithic aircraft parts. Firstly, the energy principle of machining distortion was analyzed. Then, a rapid prediction model of the final part distortion for beam parts was proposed based on the equivalent stress, and the initial bending strain energy contained in the final part was used to characterize the bending distortion risk of the final part. Numerical simulation and milling experiments verified the effectiveness of the proposed prediction model. The relative error between the experimental and calculated results does not exceed 26.5%. Finally, the influence of initial residual stress fluctuation, part geometry and the part location on part distortion was analyzed from the energy point of view. The obtained results indicated that the expected final part distortion can be minimized by adjusting these three factors.

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Machining deformation of single-sided component based on finishing allowance optimization

Cited by 30 publications

References 37 publications

Influence of Uncertain Parameters on Machining Distortion of Thin-walled Parts

Influence of Uncertain Parameters on Machining Distortion of Thin-walled Parts

Reinforcement learning method for machining deformation control based on meta-invariant feature space

Machining Distortion Minimization of Monolithic Aircraft Parts Based on the Energy Principle

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