Modeling and Prediction of Surface Roughness in Ultra-High Precision Diamond Turning of Contact Lens Polymer Using RSM and ANN Methods

Liman, Muhammad Mukhtar; Abou-El-Hossein, Khaled; Odedeyi, Peter Babatunde

doi:10.4028/www.scientific.net/msf.928.139

Cited by 5 publications

(6 citation statements)

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“…Liman et al [93] Vibrations Signals Surface roughness prediction in turning The R 2 for the predicted surface roughness values, is found to be 65.12%. Kohli et al [94] Varying cutting parameters finished end milling…”

Section: Application Of Machine Learning Methods For the Prediction Of Surface Roughnessmentioning

confidence: 97%

Monitoring and Predicting the Surface Generation and Surface Roughness in Ultraprecision Machining: A Critical Review

et al. 2021

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The aim of manufacturing can be described as achieving the predefined high quality product in a short delivery time and at a competitive cost. However, it is unfortunately quite challenging and often difficult to ensure that certain quality characteristics of the products are met following the contemporary manufacturing paradigm, such as surface roughness, surface texture, and topographical requirements. Ultraprecision machining (UPM) requirements are quite common and essential for products and components with optical finishing, including larger and highly accurate mirrors, infrared optics, laser devices, varifocal lenses, and other freeform optics that can satisfy the technical specifications of precision optical components and devices without further post-polishing. Ultraprecision machining can provide high precision, complex components and devices with a nanometric level of surface finishing. Nevertheless, the process requires an in-depth and comprehensive understanding of the machining system, such as diamond turning with various input parameters, tool features that are able to alter the machining efficiency, the machine working environment and conditions, and even workpiece and tooling materials. The non-linear and complex nature of the UPM process poses a major challenge for the prediction of surface generation and finishing. Recent advances in Industry 4.0 and machine learning are providing an effective means for the optimization of process parameters, particularly through in-process monitoring and prediction while avoiding the conventional trial-and-error approach. This paper attempts to provide a comprehensive and critical review on state-of-the-art in-surfaces monitoring and prediction in UPM processes, as well as a discussion and exploration on the future research in the field through Artificial Intelligence (AI) and digital solutions for harnessing the practical UPM issues in the process, particularly in real-time. In the paper, the implementation and application perspectives are also presented, particularly focusing on future industrial-scale applications with the aid of advanced in-process monitoring and prediction models, algorithms, and digital-enabling technologies.

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Section: Application Of Machine Learning Methods For the Prediction Of Surface Roughnessmentioning

confidence: 97%

Monitoring and Predicting the Surface Generation and Surface Roughness in Ultraprecision Machining: A Critical Review

et al. 2021

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“…The buttons were block-mounted unto a copper arbor for machining using low-temperature optical wax. 25 The experimental tests were performed under dry cutting conditions. Experiments were planned and conducted according to BBD-based RSM using Design-Expert 7 software considering three machining parameters (i.e.…”

Section: Methodsmentioning

confidence: 99%

Fuzzy logic-based modeling and analysis of surface roughness, electrostatic charge, and material removal rate in ultrahigh precision diamond turning of rigid contact lens polymer

Liman

Abou-El-Hossein

Abdulkadir

2019

Journal of Thermoplastic Composite Materials

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Due to increasing demand for high accuracy and high-quality surface finish in optical industry, contact lens manufacturing requires reliable models for predicting surface roughness (Ra) which plays a very important role in the optical manufacturing industry. In this study, a Nanoform 250 ultra-grind turning machine was used for machining, while cutting speed, feed rate, and the depth of cut (with values selected to cover a wide range based on the literature) were considered as the machining parameters for a diamond turned rigid polymethylmethacrylate (PMMA) contact lens polymer. Turning experiments were designed and conducted according to Box–Behnken design which is a response surface methodology technique. Fuzzy logic-based artificial intelligence method was employed to develop an electrostatic charge (ESC), Ra, and material removal rate (MRR) prediction models. The accuracy and predictive ability of the fuzzy logic model was then judged by considering an average percentage error between experimental values and fuzzy logic predictions. Further, a comparative evaluation of experiments and fuzzy logic approach showed that the average errors of ESC, Ra, and MRR using fuzzy logic system were in tandem with experimental results. Hence, the developed fuzzy logic rules can be effectively utilized to predict the ESC, Ra, and MRR of a rigid PMMA contact lens polymers in automated optical manufacturing environments for high accuracy and computational cost.

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“…Depending on the research objectives and specific machining conditions, the appropriate method is selected. Prevalent methods include RSM, 12 MRA, [13][14][15][16][17][18] artificial intelligent (AI) algorithm application methods such as ANN, Machine Learning (ML), [19][20][21][22][23][24][25][26] GP. [27][28][29][30][31][32][33][34][35] The MRA linear models proved to be very effective in empirical statistical problems because of its simplicity.…”

Section: Introductionmentioning

confidence: 99%

“…Depending on the research objectives and specific machining conditions, the appropriate method is selected. Prevalent methods include RSM , 12 MRA , 13–18 artificial intelligent ( AI ) algorithm application methods such as ANN , Machine Learning ( ML ), 19–26 GP . 27–35…”

Section: Introductionmentioning

confidence: 99%

“…According to survey data, ANN is one of the most used AI algorithms to predict factors in machining. 19–26 ANN and GA are applied in Hasan et al 19 to predict and optimize SR values for machining mold cavity with 7075-T6 aluminum alloy. The ANN and GP methods are introduced in Garg and Tai 20 for turning AISI H11 alloys.…”

Section: Introductionmentioning

confidence: 99%

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Predictive modeling of surface roughness in hard turning with rotary cutting tool based on multiple regression analysis, artificial neural network, and genetic programing methods

Bien

2023

Proceedings of the Institution of Mechanical Engineers, Part B:

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This present paper deals with the results of setting up and evaluating the quality of surface roughness (SR) prediction models through hard turning with self-driven rotary cutting tools for 40X steel shaft with hardness 45HRC. The cutting parameters are considered to establish the SR prediction model include angle tilt of cutting tool axis, depth of cut, feed rate, and cutting speed. The predictive models (PM) are established utilizing Multi-variables Regression Analysis (MRA), Artificial Neural Network (ANN), and Genetic Programming (GP) methods. In this regard, four MRA, four ANN, and three GP structures are considered to select the most suitable model. The criteria to estimate the PM quality include Coefficient of Determination ( R2), Mean Square Error (MSE), and Mean Absolute Percent Error (MAPE). Whereby, two data sets were collected to construct regression models (RM) and to serve verification. That dataset was originated from 63 experiments (Ep) including 54 Ep for establishing PM and 9 extensive experiments (EEp) for testing PM. The prediction criteria of the MRA3 model gave the best results in four MRA models with R2 of 0.99, MSE of 0.042, and MAPE of 8.087%. The ANN1 gives the most reliable assessment criteria in four ANN models and the GP3 model gave the best in three GP models. The results of predicting the SR value between the selected MRA and ANN and GP models will be assessed in detail. Accordingly, the evaluation criteria of the ANN1 model are the best with the smallest MSE (0.032) and MAPE (7.207%). The MRA3 and GP3 have a lower confidence predictive criteria value than the ANN1 model.

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Modeling and Prediction of Surface Roughness in Ultra-High Precision Diamond Turning of Contact Lens Polymer Using RSM and ANN Methods

Cited by 5 publications

References 0 publications

Monitoring and Predicting the Surface Generation and Surface Roughness in Ultraprecision Machining: A Critical Review

Monitoring and Predicting the Surface Generation and Surface Roughness in Ultraprecision Machining: A Critical Review

Fuzzy logic-based modeling and analysis of surface roughness, electrostatic charge, and material removal rate in ultrahigh precision diamond turning of rigid contact lens polymer

Predictive modeling of surface roughness in hard turning with rotary cutting tool based on multiple regression analysis, artificial neural network, and genetic programing methods

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