Evaluation of surface roughness based on sampling array for rotary ultrasonic machining of carbon fiber reinforced polymer composites

Shi, Hongxing; Yuan, Songmei; Li, Zhen; Song, Heng; Qian, Jianqiang

doi:10.1016/j.measurement.2019.02.002

Cited by 23 publications

(4 citation statements)

References 27 publications

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“…Kriging has been successfully used to classify and cluster data with accuracy [302]. Considering the simplicity and reduced computational time of Gaussian process and kriging, in recent years this methodology has been used extensively to model the physical properties of different polymer composites with fairly good accuracy [303][304][305][306][307][308][309][310][311][312][313][314][315][316][317].…”

Section: Gaussian Process (Gp) and Krigingmentioning

confidence: 99%

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Sharma

Mukhopadhyay

Rangappa

et al. 2022

Arch Computat Methods Eng

107

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The superior multi-functional properties of polymer composites have made them an ideal choice for aerospace, automobile, marine, civil, and many other technologically demanding industries. The increasing demand of these composites calls for an extensive investigation of their physical, chemical and mechanical behavior under different exposure conditions. Machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modeling, leading to unprecedented insights and exploration of the system properties beyond the capability of traditional computational and experimental analyses. Here we aim to abridge the findings of the large volume of relevant literature and highlight the broad spectrum potential of ML in applications like prediction, optimization, feature identification, uncertainty quantification, reliability and sensitivity analysis along with the framework of different ML algorithms concerning polymer composites. Challenges like the curse of dimensionality, overfitting, noise and mixed variable problems are discussed, including the latest advancements in ML that have the potential to be integrated in the field of polymer composites.Based on the extensive literature survey, a few recommendations on the exploitation of various ML algorithms for addressing different critical problems concerning polymer composites are provided along with insightful perspectives on the potential directions of future research.

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Section: Gaussian Process (Gp) and Krigingmentioning

confidence: 99%

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Sharma

Mukhopadhyay

Rangappa

et al. 2022

Arch Computat Methods Eng

107

View full text Add to dashboard Cite

show abstract

“…And the benefits and limitations of the machining process are discussed in Table 3. [43][44][45][46][47][48][49][50][51][52][53] The machining of the composite is directly dependent on the characteristics and relationships of the matrixes and the reinforced product.…”

Section: Processing Of Natural Fiber Reinforced Compositesmentioning

confidence: 99%

Machining parametric study on the natural fiber reinforced composites: A review

Mahakur

Bhowmik

Patowari

2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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Nowadays, the utilization of natural fiber reinforced composite has increased frequently. These natural fibers have significant features like low cost, renewable, and, more importantly, biodegradable in nature, making them to be utilized for various industrial sectors. However, the massive demand for natural fiber reinforced composites (NFRC), forces them to be machined and operated, which is required for countless areas in multiple industries like automotive, marine, aerospace and constructions. But before obtaining the final shape of any specimen, this specimen should come across numerous machining processes to get the desired shape and structure. Therefore, the present review paper focused on the various aspects during conventional and unconventional machining of the NFRC. It covers the work by exploring the influence of all input variables on the outcome produced after machining the NFRC. Various methodologies and tools are also discussed in this article for reducing the machining defects. The machining of the NFRC is found as a challenging task due to insufficient interlocking between the matrix and fibers, and minimum knowledge in machining characteristics and appropriate input parameters. Thus, this review is trying to assist the readers to grasp a basic understanding and information during the machining of the NFRC in every aspect.

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“…Eifler et al realized the performance verification of areal surface texture based on the S k -parameters associated with functional characteristic [10]. Shi et al proposed an evaluation method for 3D surface roughness based on sampling array to evaluate surface quality [11]. In addition, some statistical functions have been developed to describe the machined surface in other engineering field.…”

Section: Introductionmentioning

confidence: 99%

“…Eifler et al 18 realized the performance verification of areal surface texture based on the S k - parameters associated with functional characteristic. Shi et al 19 proposed a 3D surface roughness evaluation method of surface quality based on sampling array. Liu et al 20 evaluated the surface topography by 3D surface roughness and fractal dimension in micro-milling.…”

Section: Introductionmentioning

confidence: 99%

Characterization and functional evaluation of surface texture of micro eccentric shaft based on multi-index

Cheng

Pei

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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Micro eccentric shaft has important application in many high-tech fields because of its small specific gravity, material, and energy saving. The machined surface texture has an indispensable influence on the surface integrity and the final functional capability. However, due to the micro scale and weak rigidity, it is difficult to characterize the surface texture and evaluate the functionality by traditional quantitative parameters. In order to comprehensively realize the surface texture characterization and functional analysis, a mathematical model is established to simulate the surface texture machined with different cutting tools. Then the surface microscopic profile and functional performance of the surface texture are analyzed by amplitude distribution function (ADF) and bearing area curve (BAC), and the surface texture is also evaluated by fractal dimension, which can avoid the negative effects of scale and resolution. Furthermore, power spectrum density (PSD) is utilized to analyze the relationship between the process dynamic state and geometrical specification of the surface texture. The validity of experimental results shown that the microscopic height distribution of surface machined by flat end milling cutter tends to be more random and there are more microscopic geometric features than that of the ball end milling cutter. The machined surface obtained by the flat end milling cutter has better load bearing, wear resistance, and liquid retention capability.

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Evaluation of surface roughness based on sampling array for rotary ultrasonic machining of carbon fiber reinforced polymer composites

Cited by 23 publications

References 27 publications

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Machining parametric study on the natural fiber reinforced composites: A review

Characterization and functional evaluation of surface texture of micro eccentric shaft based on multi-index

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