All articles published by are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by , including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https:///openaccess.
Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.
Editor’s Choice articles are based on recommendations by the scientific editors of journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.
Milwaukee Unveils Next Generation Of Cordless Die Grinders
By Muhammad Rizwan Awan 1, 2, * , Hernán A. González Rojas 1, Saqib Hameed 1, Fahid Riaz 3, Shahzaib Hamid 4 and Abrar Hussain 5
Cut-off operation is widely used in the manufacturing industry and is highly energy-intensive. Prediction of specific energy consumption (SEC) using data-driven models is a promising means to understand, analyze and reduce energy consumption for cut-off grinding. The present article aims to put forth a novel methodology to predict and validate the specific energy consumption for cut-off grinding of oxygen-free copper (OFC–C10100) using supervised machine learning techniques. State-of-the-art experimental setup was designed to perform the abrasive cutting of the material at various cutting conditions. First, energy consumption values were predicted on the bases of input process parameters of feed rate, cutting thickness, and cutting tool type using the three supervised learning techniques of Gaussian process regression, regression trees, and artificial neural network (ANN). Among the three algorithms, Gaussian process regression performance was found to be superior, with minimum errors during validation and testing. The predicted values of energy consumption were then exploited to evaluate the specific energy consumption (SEC), which turned out to be highly accurate, with a correlation coefficient of 0.98. The relationship of the predicted specific energy consumption (SEC) with material removal rate agrees well with the relationship depicted in physical models, which further validates the accuracy of the prediction models.
Industrial manufacturing is one of the main contributors to global warming, with a 30% share of primary energy consumption [1]. To minimize the environmental impact of the manufacturing processes, reduction of energy consumption is the priority of manufacturers [2]. Among the manufacturing processes, grinding, being a multipoint, cutting operation, is a complex and highly energy-intensive process [3]. Understanding and estimation are primary and important steps to reduce the energy consumption of machining [4]. Specific energy consumption (SEC), i.e., the energy required to remove the unit volume of the material, is an important indicator to understand and analyze the energy consumption pattern among different materials [5]. The abrasive cut-off operation, which is the thin sectioning of the metals with cutting discs, is a high-material-removal-rate grinding process and consumes a lot of energy [6, 7]. Therefore, specific energy prediction and analysis for abrasive cut-off operation are of paramount importance. The specific energy of grinding and its associated components are highly influenced by the process parameters [8]. Numerous experimental and modeling techniques have been directed to model and evaluate grinding energy and its components [5, 9, 10, 11]. However, these techniques rely on complex mathematical formulations to find the relationship between the grinding energy and the process parameters and are based on specific assumptions, which might vary during the experimental conditions.
Buy Philips 2 Jar Mixer Grinder (hl1631)
For this reason, the data-driven approach based on machine learning is the focus of the researcher because of its ability to find highly complex and nonlinear patterns in data and transform raw data into models for prediction, detection, classification, and regression [12]. Several studies have been conducted to predict the energy consumption of different machining processes based on machine learning techniques. Kant [13] developed the artificial neural network model to predict the cutting energy of carbon steel during machining using data from 27 experiments by varying process parameters (spindle speed, depth, and width of cut and feed rate). The employed model predicted the cutting energy with almost 98.5% accuracy. Similarly, Borgia et al. [14] predicted the energy consumption of milling using an artificial neural network; the employed model was able to predict the energy consumption of machining with an error of 2.46%. Bhinge et al. [15] employed a Gaussian process-based data-driven approach to predict the energy consumption function for machine tools that can be generalized over multiple process parameters and operations. Liu et al. [16] introduced the hybrid technique based on Gaussian process regression and process mechanics to predict the specific cutting energy of milling. Brillinger et al. [17] employed the machine learning algorithms of decision tree, random forest, and boosted random forest to predict the energy consumption of CNC based on real production data. Their research results revealed that random forest is the most accurate algorithm for energy prediction. Ziye and Yue bin [16] presented the hybrid approach to predict the specific cutting energy of a milling machine by integrating the data-driven machine learning model and process mechanics. Stefanio et al. [14] used ANN to analyze the influence of process parameters on the energy consumption of milling process. Similarly, Quintana et al. [2] optimized the power consumption of the milling process using ANN and determined the most effective process parameters to save energy and process cost.
Similar to other machining processes, data-driven conditioning monitoring and prediction of indicators for grinding are also being extensively applied. For instance, Siamak et al. [18] predicted the surface roughness and cutting forces of grinding using process parameters and acoustic emissions signals (AE) as the input. Predicted results were in good agreement with experimental data with an accuracy of 99%. Sauter et al. [19]. employed the multiclass classification using supervised learning to predict the grinding burn based on input process measurements of acoustic emissions, spindle electric current, and power signals. Arriandiaga et al. [20] presented an ANN-based approach to predict and model the specific energy consumption of grinding using wheel characteristics and the grinding conditions [20]. Similarly, He et al. [21] developed a generic energy prediction model of machine tools using deep learning algorithms. He employed unsupervised machine learning to extract sensitive features from raw data of milling and grinding machines and then used the supervised machine learning algorithms to develop a prediction model between the extracted features and the energy consumption of machine tools.
Unlike the comprehensive studies of machine learning-based energy prediction of machining and surface grinding, none of the efforts have been directed towards machine learning or data-driven-based prediction of specific energy consumption for cut-off grinding, which is the widely used finishing operation in the industry. The recently conducted and previous studies about the abrasive cut-off operation for metals, stone machining, and concrete rely on the complex modeling of specific energy consumption in relationship with the process parameters [5, 22, 23, 24, 25]. These models are true under certain assumptions and for specific conditions and might not represent an accurate estimation of energy during data variation due to practical constraints. To fill this gap, this research aims to present a novel data-driven approach for the prediction of specific energy consumption of cut-off grinding using supervised machine learning techniques of decision trees, Gaussian process regression, and ANN. A state-of-the-art grinding machine was developed and a novel procedure was designed to perform abrasive cutting on oxygen-free copper under different cutting conditions. The developed algorithms showed very good prediction accuracy when explored and tested for the new data set.
Eureka Mignon Specialita Espresso Grinder In Chrome
The prediction of specific energy consumption (SEC) involves measurement and prediction of both cutting power (Pm) and material removal rate (Qw). Figure 1 elaborates the overall process, from experimentation to modeling and then prediction of specific energy consumption. First, abrasive metal cutting procedure is employed, as shown in Figure 2, to measure the electrical energy consumed during the process. The data of energy consumption (Ee) is collected, cleaned, and processed to apply machine learning algorithms, which provide the prediction of Ee based on input process parameters. The predicted energy is used to evaluate the mechanical cutting power (Pm) from the experimental relationship of Ee and Pm obtained through the calibration of standard grinder with dynamometer. Based on the predicted values of cutting power (Pm) and material removal rate (Qw), specific energy consumption is predicted and compared with experimental values.
To measure the electrical energy consumed (Ee) for abrasive cutting of oxygen-free copper, a state-of-the-art automated experimental setup was designed and developed. This experimental set up was automated through Arduino Uno. The abrasive metal cutting was performed by 1 × 115 mm cutting discs, which were mounted on the standard grinder. The machine is capable of cutting different material at four predefined feed rates. The downfeed
The prediction of specific energy consumption (SEC) involves measurement and prediction of both cutting power (Pm) and material removal rate (Qw). Figure 1 elaborates the overall process, from experimentation to modeling and then prediction of specific energy consumption. First, abrasive metal cutting procedure is employed, as shown in Figure 2, to measure the electrical energy consumed during the process. The data of energy consumption (Ee) is collected, cleaned, and processed to apply machine learning algorithms, which provide the prediction of Ee based on input process parameters. The predicted energy is used to evaluate the mechanical cutting power (Pm) from the experimental relationship of Ee and Pm obtained through the calibration of standard grinder with dynamometer. Based on the predicted values of cutting power (Pm) and material removal rate (Qw), specific energy consumption is predicted and compared with experimental values.
To measure the electrical energy consumed (Ee) for abrasive cutting of oxygen-free copper, a state-of-the-art automated experimental setup was designed and developed. This experimental set up was automated through Arduino Uno. The abrasive metal cutting was performed by 1 × 115 mm cutting discs, which were mounted on the standard grinder. The machine is capable of cutting different material at four predefined feed rates. The downfeed
0 komentar
Posting Komentar