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Accurate prediction on energy consumption in machining is helpful to evaluate process energy characteristics and choose process methods for energy saving. Specific energy consumption expresses the required energy consumption when cutting unit volume material. The Back Propagation (BP) neural network prediction method for specific energy consumption...
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... CNC machine tool includes the spindle drive system, feed drive system, electric control system, lighting system, cooling system, lubricating system and chip system. The spindle drive system and feed drive system are defined as machine tool transmission system. The structure of CNC machining system is shown in Fig. 1. In CNC turning and milling, the cutting elements commonly include the spindle speed, feed rate and cutting depth. Specifically, the spindle speed is used to specify the CNC machine tool spindle rotation rate in r/min; feed rate is the moving speed of the tool relative to work piece on the feed direction in mm/min; and cutting depth is ...
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Citations
... Kant et al. [74] developed an accurate predictive model of cutting energy consumption using ANN during a milling process through comparing the experimental results and analyzed the influence of machining parameters such as spindle speed, feed rate, depth of cut, and width of cut on cutting energy. Zhao et al. [75] developed a backpropagation neural networks (BPNN) prediction model for specific energy consumption which meant the required energy consumption for cutting unit volume material. Using the comparison of mean square prediction error, the highest performance structure was adopted. ...
Artificial intelligence (AI) in machine tools offers diverse advantages, including learning and optimizing machining processes, compensating errors, saving energy, and preventing failures. Various AI techniques have been proposed and applied; however, many challenges still exist that inhibit the use of AI for machining tasks. This paper deals with different types and usage of AI technologies in machining operations such as predictive modelling, parameter optimization and control, chatter stability, tool wear, and energy conservation. We discuss the challenges of AI technologies, such as data quality, transferability, explainability, and suggest future directions to overcome them.
... 1 The manufacturing fields account for over 60% of the whole industrial energy consumption, and about 70% emissions caused by environmental pollution come from manufacturing fields in China. 2,3 Similarly, the manufacturing sector accounts for about 90% of the whole industrial energy consumption, and the machining sector accounts for about 83% of the whole manufacturing energy consumption in the United States. Consequently, the urgent task of the manufacturing fields is how to reduce the energy consumption and environmental pollution to realize low carbon manufacturing. ...
The accurate energy consumption characteristics evaluation in machining is helpful to optimize processing scheme to realize low carbon manufacturing. The energy consumption characteristics evaluation method in turning is researched, which includes energy efficiency calculation and energy consumption prediction. First, the simple and accurate energy efficiency computational model in turning is put forward. Second, the energy consumption prediction empirical model and the back-propagation neural network prediction model are developed respectively. Finally, the power signal measurement system is built on computer numerical controlled lathe, and the proposed energy efficiency computational model and the energy consumption prediction methods are verified in the 45# steel semi-finish turning with hard alloy cutting tools. The results show that the proposed methods can effectively evaluate the energy consumption characteristics in turning, which can help to select the high energy efficiency processing program on the basis of ensuring processing quality.
Estimation and analysis of energy consumption for machine tool is the basis of energy efficiency improvement. To improve the accuracy of ELM algorithm in CNC machine tool energy consumption prediction, a prediction method based on an improved particle swarm optimization (CAPSO) algorithm and an extreme learning machine (ELM) is proposed. The contribution of the algorithm includes the following three aspects. First, sobol sequence is used to initialize the PSO population to make distribution of initial population more even in solution space. Second, the center wanders and boundary neighborhood updates strategy are used to improve the population quality and convergence rate of PSO. Then, to avoid the optimal local solution, the adaptive inertia weight is introduced to achieve the stochastic perturbation of the population. The performance of the algorithm is tested by ten benchmark function, indicating that the CAPSO ensures the search accuracy and improves the algorithm’s convergence rate. Finally, the CAPSO algorithm is used to optimize the weights and thresholds of an ELM, and the CAPSO-ELM cutting energy consumption prediction model is established. Case analysis and comparative experiments show that the stability, prediction accuracy and generalization ability of CAPSO-ELM model are better than those of other models.
