Modeling and prediction of cutting forces during the turning of red brass (C23000) using ANN and regression analysis

Hanief, M.; Wàňi, M.F.; Charoo, M.S.

doi:10.1016/j.jestch.2016.10.019

Cited by 45 publications

(20 citation statements)

References 12 publications

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“…ANN, multiple regression approaches and ANOVA were used. It is evident from the analysis of variance in this article that the regression method is able to forecast the cutting forces with a higher accuracy [36] which supports the present study. An optimal renewable energy model, OREM for India was evolved for the year 2020-2021 to meet the increasing energy requirements [37].…”

Section: Discussionsupporting

confidence: 87%

Short-Term Forecasting of Total Energy Consumption for India-A Black Box Based Approach

Rahman

Iniyan

2018

Energies

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Continual energy availability is one of the prime inputs requisite for the persistent growth of any country. This becomes even more important for a country like India, which is one of the rapidly developing economies. Therefore electrical energy’s short-term demand forecasting is an essential step in the process of energy planning. The intent of this article is to predict the Total Electricity Consumption (TEC) in industry, agriculture, domestic, commercial, traction railways and other sectors of India for 2030. The methodology includes the familiar black-box approaches for forecasting namely multiple linear regression (MLR), simple regression model (SRM) along with correlation, exponential smoothing, Holt’s, Brown’s and expert model with the input variables population, GDP and GDP per capita using the software used are IBM SPSS Statistics 20 and Microsoft Excel 1997–2003 Worksheet. The input factors namely GDP, population and GDP per capita were taken into consideration. Analyses were also carried out to find the important variables influencing the energy consumption pattern. Several models such as Brown’s model, Holt’s model, Expert model and damped trend model were analysed. The TEC for the years 2019, 2024 and 2030 were forecasted to be 1,162,453 MW, 1,442,410 MW and 1,778,358 MW respectively. When compared with Population, GDP per capita, it is concluded that GDP foresees TEC better. The forecasting of total electricity consumption for the year 2030–2031 for India is found to be 1834349 MW. Therefore energy planning of a country relies heavily upon precise proper demand forecasting. Precise forecasting is one of the major challenges to manage in the energy sector of any nation. Moreover forecasts are important for the effective formulation of energy laws and policies in order to conserve the natural resources, protect the ecosystem, promote the nation’s economy and protect the health and safety of the society.

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Section: Discussionsupporting

confidence: 87%

Short-Term Forecasting of Total Energy Consumption for India-A Black Box Based Approach

Rahman

Iniyan

2018

Energies

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“…Agustina et al [ 17 ] observed that for the aluminum alloy A97075, the most significant cutting parameter is feed rate, followed by the depth of cut and spindle speed. Similarly, studies related to the turning of other materials such as AISI 4340 steel [ 41 ], AISI 4140 steel [ 42 ], red brass [ 43 ], Inconel 718 [ 44 ], also showed that feed rate and depth of cut have a significant effect on cutting forces while cutting speed is found to be insignificant.…”

Section: Resultsmentioning

confidence: 99%

Experimental Investigation and Statistical Evaluation of Optimized Cutting Process Parameters and Cutting Conditions to Minimize Cutting Forces and Shape Deviations in Al6026-T9

et al. 2020

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Precise, economical and sustainable cutting operations are highly desirable in the advanced manufacturing environment. For this aim, the present study investigated the influence of cutting parameters (i.e., the cutting speed (c), feed rate (f), depth of cut (d) and positive rake angle (p)) and sustainable cutting conditions (dry and minimum quantity lubricant (MQL)) on cutting forces (i.e., feed force (Ff), tangential forces (Ft), radial force (Fr) and resultant cutting forces (Fc) and shape deviations (i.e., circularity and cylindricity) of a 6026-T9 aluminum alloy. The type of lubricant and insert used are virgin olive oil and uncoated tungsten carbide tool. Turning experiments were performed on a TAKISAWA TC-1 CNC lathe machine and cutting forces were measured with the help of a Kistler 9257B dynamometer. Shape deviations were evaluated by means of a Tesa Micro-Hite 3D DCC 474 coordinate measuring machine (CMM). Experimental runs were planned based on Taguchi mixture orthogonal array design L16. Analysis of variance (ANOVA) was performed to study the statistical significance of cutting parameters. Taguchi based signal to noise (S/N) ratios are applied for optimization of single response, while for optimization of multiple responses Taguchi based signal to noise (S/N) ratios coupled with multi-objective optimization on the basis of ratio analysis (MOORA) and criteria importance through inter-criteria correlation (CRITIC) are employed. ANOVA results revealed that feed rate, followed by a depth of cut, are the most influencing and contributing factors for all components of cutting forces (Ff, Ft, Fr, and Fc) and shape deviations (circularity and cylindricity). The optimized cutting parameters obtained for multi responses are c = 600 m/min, f = 0.1 mm/rev, d = 1 mm and p = 25°, while for cutting conditions, MQL is optimal.

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“…However, the processing of graded material remained a big unknown, and presented a challenge for researchers and industry around the world. An investigation by Hanief et al [134] into red brass machining was intended to develop a model to study the influence of cutting parameters (speed, depth of cut, and feed rate) on the cutting forces during turning operations using high speed steel tools. Experiments were carried out on a basis of full factorial design methodology to increase the reliability and confidence limit of the data.…”

Section: Intelligent Manufacturingmentioning

confidence: 99%

“…There are many AI algorithms for machine health monitoring and other machine tool applications: The second-order recurrent neural networks (RNN) method for the learning and extraction of finite for intelligent tool wear estimation [129], optical image scattering and hybrid artificial intelligence techniques for intelligent tool wear identification [130], a pattern recognition method for identifying the designed strength of concrete by evidence accumulation [131], adaptive neuro-fuzzy inference and hybrid systems, in an ANN approach, to the modeling and optimization of ultrasonic welding (USW) process parameters [132], an NN method for the prediction of cutting forces [133], an ANN and regression analysis scheme for the modeling and prediction of cutting forces [134], an ANN method for the intelligent control of a three-joint robotic manipulator [135], an ANN approach using the genetic algorithm for intelligent fixture design [136], a survey of CPS architecture used in Industry 4.0-based manufacturing systems [137], a review of CPS in advanced manufacturing [138], hierarchical hybrid models for face-detection and face-recognition [139], machine learning for the design and evaluation of obstacle detection in a transportation CPS [140], machine learning algorithms for CPS, decision sciences and data products [141], an ANN scheme for diagnosis, classification and prognosis of rotating machines [142], an ANN method for the prediction of the remaining useful life in rotating machines [143], wavelet packet decomposition, Fourier transform and ANN methods used in the classification of faults and the prediction of the degradation of components and machines in a manufacturing system [144], a recursive least-squares (RLS) approach to the behavior of rolling element bearings [145], an NN method for the prognosis of remaining bearing life [146], a model-based, data-driven (or both), prediction method for remaining useful life (RUL) [147], condition-based machinery maintenance (CBM), diagnostics and prognostics [148], a dynamic fault isolation scheme for the prognosis of hybrid systems [149], an LSTM based encoder-decoder for multi-sensor prognostics [150], an NN method for intelligent pressure sensors [151], a functional link ANN approach for the modeling of an intelligent pressure sensor [152]<...>…”

Section: Brief Introductionmentioning

confidence: 99%

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

2018

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This paper offers a review of the artificial intelligence (AI) algorithms and applications presently being used for smart machine tools. These AI methods can be classified as learning algorithms (deep, meta-, unsupervised, supervised, and reinforcement learning) for diagnosis and detection of faults in mechanical components and AI technique applications in smart machine tools including intelligent manufacturing, cyber-physical systems, mechanical components prognosis, and smart sensors. A diagram of the architecture of AI schemes used for smart machine tools has been included. The respective strengths and weaknesses of the methods, as well as the challenges and future trends in AI schemes, are discussed. In the future, we will propose several AI approaches to tackle mechanical components as well as addressing different AI algorithms to deal with smart machine tools and the acquisition of accurate results.Keywords: artificial intelligence; smart machine tools; learning algorithms; intelligent manufacturing; fault diagnosis and prognosis Brief IntroductionWe believe that a new epoch of the "Industrial Internet of Things (IIoT) plus artificial intelligence (AI)", characterized by big machinery data, data-driven techniques, ubiquitous networks, mass innovation, automatic intelligence, cross-border integration, and shared services, has arrived [1-3]. The fast development and combination of new AI and energy technologies, for materials, bioscience, the Internet, and new-generation information exchange, is a fundamental part of this new epoch. This will, in turn, permit game-changing transformation of models, ecosystems, and means in the light of their application to national security, well-being, and the economy. The main objective is a review and summary of recent achievement in data-based techniques, especially for complicated industrial applications, offering reference for further study from both an academic and practical point of view. Yin et al.[1] describes a brief evolutionary overview of data-based techniques over the last two decades. Recent development of modern industrial applications is presented mainly from the perspectives of monitoring and control. Their methodology, based on process measurements and model-data integrated techniques, will be introduced in the next study. Jeschke et al. [2] developed the core system science needed to enable the development of complex IIoT/manufacturing cyber-physical systems (CPS). Moreover, readers can learn the current state of IIoT and the concept of cybermanufacturing from this book. In 2014, Lund et al. [3] described the central issues contributing to, and characterizing, the worldwide and regional growth of the IoT. Besides, researchers can utilize the trend analysis of IoT their region markets in the future.There are many AI algorithms for machine health monitoring and other machine tool applications: The second-order recurrent neural networks (RNN) method for the learning and extraction of finite

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Modeling and prediction of cutting forces during the turning of red brass (C23000) using ANN and regression analysis

Cited by 45 publications

References 12 publications

Short-Term Forecasting of Total Energy Consumption for India-A Black Box Based Approach

Short-Term Forecasting of Total Energy Consumption for India-A Black Box Based Approach

Experimental Investigation and Statistical Evaluation of Optimized Cutting Process Parameters and Cutting Conditions to Minimize Cutting Forces and Shape Deviations in Al6026-T9

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

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