Determining surface roughness of machining process types using a hybrid algorithm based on time series analysis and wavelet transform

Pour, Masoud

doi:10.1007/s00170-018-2070-2

Cited by 25 publications

(10 citation statements)

References 38 publications

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“…From the studies, it has been found that time series is most important in the metal cutting procedures, and the relationship between the indirect measurements and the roughness of the surface was found, such as using sound characteristics and the vibration characteristics [137][138][139]. The measurements carried out with the help of vibration characteristics attained the relative errors of about 15% [139], and the evaluations established on the time series analysis are also not advantageous as compared to the vision-based methods [140,141]. The surface roughness measured with the methods of CNN and GLCM has been achieved to the best precision of about 80%-90% compared to the conventional stylus-based method [142].…”

Section: Figure 38mentioning

confidence: 99%

Surface Characteristics Measurement Using Computer Vision: A Review

Hashmi¹,

Mali²,

Meena³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Computer vision provides image-based solutions to inspect and investigate the quality of the surface to be measured. For any components to execute their intended functions and operations, surface quality is considered equally significant to dimensional quality. Surface Roughness (Ra) is a widely recognized measure to evaluate and investigate the surface quality of machined parts. Various conventional methods and approaches to measure the surface roughness are not feasible and appropriate in industries claiming 100% inspection and examination because of the time and efforts involved in performing the measurement. However, Machine vision has emerged as the innovative approach to executing the surface roughness measurement. It can provide economic, automated, quick, and reliable solutions. This paper discusses the characterization of the surface texture of surfaces of traditional or non-traditional manufactured parts through a computer/machine vision approach and assessment of the surface characteristics, i.e., surface roughness, waviness, flatness, surface texture, etc., machine vision parameters. This paper will also discuss multiple machine vision techniques for different manufacturing processes to perform the surface characterization measurement.

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Section: Figure 38mentioning

confidence: 99%

Surface Characteristics Measurement Using Computer Vision: A Review

Hashmi¹,

Mali²,

Meena³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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“…A hybrid GA and intelligent search method was proposed by Salehi and Bahreininejad (2011), and it was applied to optimising machine tool, cutting tool and tool access direction for each operation. Pour (2018) proposed a hybrid algorithm based on time series analysis and wavelet transform to model surface roughness. Moreover, many efforts were also devoted to GAbased hybrid methods for optimisation of machining process.…”

Section: Related Workmentioning

confidence: 99%

A big data analytics based machining optimisation approach

Yin

Wang

2018

J Intell Manuf

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Currently, machine tool selection, cutting tool selection and machining conditions determination are not usually performed at the same time but progressively, which may lead to suboptimal or trade-off solutions. Targeting this issue, this paper proposes a big data analytics based optimisation method for enriched Distributed Process Planning by considering machine tool selection, cutting tool selection and machining conditions determination simultaneously. Within the context, the machining resources are represented by data attributes, i.e. workpiece, machining requirement, machine tool, cutting tool, machine conditions, machining process and machining result. Consequently, the problem of machining optimisation can be treated as a statistic problem and solved by a hybrid algorithm. Regarding the algorithm, artificial neural networks based models are trained by machining data and used as optimisation objectives, whereas analytical hierarchy process is adopted to decide the weights of the multi-objective optimisation; and evolutionary algorithm or swarm intelligence is proposed to perform the optimisation. Finally, the results of a simplified proof-of-concept case study are reported to validate the proposed approach, where a Deep Belief Network model was trained by a set of hypothetic data and used to calculate the fitness of a genetic algorithm.

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“…The problem of proper selection of filter length and, therefore, the problem of filter affecting the points of surface core occurs regardless of filtration type (Gaussian, spline, and coarse Gaussian) [30,[33][34][35][36][40][41][42]. The scale of that phenomenon decreases as the height of surface geometry and the area of measured surface increase.…”

Section: Introductionmentioning

confidence: 99%

“…The literature also describes a method called wavelet transform. The advantage of the method is that it can separate roughness (measurement noise and/or microroughness) of functional surfaces with a regular shape [40,41,43,44]. This property is particularly important in surface metrology, in the assessment of milling stability [45][46][47], and in other areas of technology regarding machine components [48,49].…”

Section: Introductionmentioning

confidence: 99%

The Wavelet Transform for Feature Extraction and Surface Roughness Evaluation after Micromachining

Grochała,

Grzejda,

Parus

et al. 2024

Coatings

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Miniaturization is a dominant trend in machine building which requires the use of advanced techniques of manufacturing and control. Apart from dimensional and shape precision of miniaturized components, surface geometry, particularly roughness and so-called microroughness that results from the use of advanced treatment techniques, plays an important role in correct assembly, reliable operation and durability of the whole machine. The selection of filtration method in surface geometry of micro-objects can be a substantial problem. The authors of the paper propose to use wavelet filtration in digital processing of a point cloud to remove measurement noise and not to change the surface character of the measured object. Also, the authors propose, in this paper, some criteria for selecting the number of wavelet filtration levels based on minimalization of the RMS value. It is supposed to improve the efficiency of low-pass filtration of small areas of the surface, particularly compared to traditional λs Gaussian filtration.

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Determining surface roughness of machining process types using a hybrid algorithm based on time series analysis and wavelet transform

Cited by 25 publications

References 38 publications

Surface Characteristics Measurement Using Computer Vision: A Review

Surface Characteristics Measurement Using Computer Vision: A Review

A big data analytics based machining optimisation approach

The Wavelet Transform for Feature Extraction and Surface Roughness Evaluation after Micromachining

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