Energy Consumption Of Machine Tools Research Articles

Energy consumption forecast model of CNC machine tools based on support vector regression optimized by improved artificial hummingbird algorithm

With the development of the manufacturing industry, energy consumption is growing rapidly, which makes the energy crisis and environmental problems become more and more serious. CNC machine tools play an essential role and are the primary energy consumption devices in the manufacturing industry. The accurate prediction of machine tool energy consumption can provide support for energy production plans and reduce energy waste. This paper proposes a novel energy consumption prediction model based on support vector regression (SVR) optimized by an improved artificial hummingbird algorithm (IAHA). Firstly, as the artificial hummingbird algorithm (AHA) may easily get trapped in a local optimum, an improved AHA based on chaotic mapping and local backtracking exploitation strategy is proposed. The chaotic mapping is used to initialize individual positions, which is good for maintaining population diversity. The local backtracking exploitation strategy is employed to improve the local optimization ability. The effectiveness and feasibility of the IAHA algorithm have been verified through 12 benchmark functions. Then, the IAHA algorithm is employed to optimize the parameters of the SVR, and the IAHA-SVR energy consumption prediction model is established. Finally, the effectiveness and feasibility of the IAHA-SVR model are verified through a case study.

Toward cyber-physical systems for monitoring and analyzing energy consumption of machine tools

ABSTRACT This paper presents a digital twin and cyber-physical systems (CPS) environment for monitoring and analyzing the energy consumption of machine tools. Machine tools, which account for a large portion of the manufacturing industry, should be developed in response to environmental issues through carbon neutrality and digital transformation for the Fourth Industrial Revolution. The digital twin, which is at the core of CPS, must support the visualization and analysis of energy consumption issues from various perspectives, and at the same time, support multi-faceted analyses. In this paper, a digital twin that supports the monitoring and analysis of the energy consumption of machine tools and an established CPS environment are proposed. The digital twin consists of a static model containing structural information, a dynamic model expressing the behavior of the object, a visualization model containing a three-dimensional model, and a configuration model linking those three together. In addition, a CPS environment that supports this is implemented through a commercial internet-of-things platform. Lastly, the practicality of the proposed model and system are verified through case studies and a comparative analysis. This research is expected to prompt innovation in the development of cutting-edge machine tools that are more intelligent and closer to carbon neutrality.

Efficient low-carbon manufacturing for CFRP composite machining based on deep networks

ABSTRACT The drilling quality of carbon fibre reinforced polymer (CFRP) components is a key factor affecting the service life of the components, while energy saving and emission reduction in industrial production are crucial. In this study, drilling experiments were conducted on T300 plywood using a 55° coated tungsten steel drill bit, and CNN-LSTM neural network models were used to construct mapping relationships between process parameters (spindle speed, feed rate, and fibre lay-up sequence) and delamination factor and machine energy consumption. A new method of predicting the delamination factor by process parameters is proposed, and explored the optimal process parameter combinations that reduce the energy consumption of machine tools and minimise the delamination factor at the same time. The research results show that within the parameter settings, a spindle speed of 7000 r/min, a feed rate of 40 mm/min, and a lay-up sequence of [0°, 0°, −45°, 90°]6s ensure both low power consumption in the drilling process and the highest possible hole quality. This paper clearly demonstrates the feasibility of achieving low-power, high-quality drilling of CFRP through parameter optimisation, providing guidance to the manufacturing industry to improve the quality of CFRP hole-making while easing the pressure on carbon emissions.

Review on Design Research in CNC Machine Tools Based on Energy Consumption

CNC machine tools play an important role in manufacturing and are characterized by high total energy consumption and low energy efficiency. The energy consumption characteristics of the machine tool itself determine the total energy consumption and pollutant emission during its service life. Therefore, it is particularly important to design machine tools with energy consumption as the optimization target to analyze the composition of energy consumption and related characteristics, build a corresponding model based on reliability verification, guide the structural design and optimization according to the model, and ultimately use the evaluation system to evaluate and judge the overall energy consumption. In this paper, from four perspectives—the composition of the energy consumption of machine tools, modeling methods, design and optimization methods, and evaluation methods—with energy consumption optimization as the entry point, we analyze the research on CNC machine tools based on energy consumption around the world. The research results indicate that we should look forward to the role of energy consumption in the design of machine tools.

Energy Consumption Modeling and Optimization of a Hobbing Machine Tool Considering Multi-Axis Coupling

Energy consumption of machine tools has always been a hot issue in the manufacturing sector due to the concern about climate change. To decrease the energy consumption of machine tools, this paper investigates the multi-axis coupling mechanism of a hobbing machine tool during machining from the design stage, and develops an energy consumption model under multi-axis coupling of the whole machine. Firstly, the multi-axis coupling mechanism in the actual machining process is studied. The hob speed and the feed speed of Z axis moving component will affect the hobbing force, and the component of hobbing force in each direction will affect the load and energy consumption of each axis. Meanwhile, the tracking error of each axis is reduced by a sliding mode controller. Next, a comprehensive energy consumption optimization model of a hobbing machine tool under multi-axis coupling is constructed. Furthermore, three optimization algorithms are implemented to the energy consumption model. Compared with the initial scheme, the simulation experiment results of the case-study indicate that the hobbing machine tool energy consumption is reduced by 1.19%, the steady-state error of each axis is reduced by 35.27%, 21.70%, and 36.34%, respectively, and the maximum deformation of the tool holder is reduced by 10.91%. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is dedicated to dealing with the issue of a hobbing machine tool energy consumption. Based on our previous work, the multi-axis coupling mechanism of the gear hobbing machine tool is investigated, and a comprehensive energy consumption model of the gear hobbing machine tool is proposed. The design variables of the energy consumption optimization model include the structural parameters of the Z axis moving component and the sliding mode controller parameters of the three axes (B, C, and Z axis). Three optimization algorithms are used to drive the optimization model. The simulation results verify the feasibility of the method. The control performance of the system is improved, and the mass of moving component is reduced.

A Method for Identifying Energy Consumption of Machine Tools Based on Integrated Models

The uncertainty of the dynamic change process of expenditure of energy from machine tools seriously hinders the development of an intelligent and green manufacturing industry. In response to the multifaceted impact of identification methods in the process of energy consumption change, the difficulty of extracting high-quality features, and the complexity and nonlinearity of energy consumption curves, an identification method for predicting the energy consumption of machine tools based on integrated models was proposed. Taking CNC milling as an example, the model of energy consumption based on different cutting periods was established, which was then trained and predicted by using the preprocessing signal based on Random forest (RF). Meanwhile, the RF was used to identify the cutting stages and realize the energy consumption classification prediction. The advantage and availability of this method have been demonstrated through practical cases, and it has been verified that this recognition method can accurately predict different operating states and energy consumption of each stage on machine tools.

Time Series Prediction for Energy Consumption of Computer Numerical Control Axes Using Hybrid Machine Learning Models

The prediction of energy-related time series for computer numerical control (CNC) machine tool axes is an essential enabler for the shift towards autonomous and intelligent production. In particular, a precise prediction of energy consumption is needed to determine the environmental impact of a product and the optimization of its production. For this purpose, a novel approach for predicting high-frequency time series of numerically controlled axes based on the program code to be executed is presented. The method involves simulative preprocessing of the input NC code to determine each axis’s acceleration, velocity, and process force. Combined with the material removal rate, these variables are input for a machine learning (ML) model that delivers axis-specific high-frequency time series predictions. Compared to common approaches, it is thus possible to make predictions for the variable energy consumption of machine tools for any tool path or target resolution in the time domain. Experiments show that this approach achieves a high precision when a robust learning data basis is available. For the X-, Y-, and Z-axis, errors of 0.2%, −1.09%, and 0.09% for aircut and of 0.15%, −3.55%, and 0.08% for material removal can be achieved. The potentials for further improvement are identified systematically.

A comprehensive review of machine learning techniques in computer numerical controlled machines

Machine learning (ML) is significant advancements in computer science and data processing systems that may be utilized to improve almost all technology-enabled services, goods, and industrial applications. Machine learning is a branch of computer science and artificial intelligence. It emphasizes the use of data and algorithms to mimic the learning process of machines and improve system accuracy. To extend the life of the cutting tools used in machining processes, machine learning algorithms may be used to forecast cutting forces and cutting tool wear. In order to improve productivity during the component production processes, optimized machining parameters for CNC machining operations may be obtained by applying cutting-edge machine learning algorithms. Furthermore, the appearance of Advanced machine learning algorithms can forecast and enhance machinable components to raise the caliber of machinable parts. Machine learning is applied to prediction approaches of energy consumption of CNC machine tools in order to analyses and minimize power usage during CNC machining processes. The use of machine learning and artificial intelligence systems in CNC machine tools is examined in this paper, and future research projects are also suggested in order to offer an overview of the most recent studies on these topics. As a result, the research filed can be moved forward by reviewing and analyzing recent achievements in published papers to offer innovative concepts and approaches in applications of machine learning in CNC machine tools.

Dynamic Allocation of Manufacturing Resources in IoT Job Shop Considering Machine State Transfer and Carbon Emission

The optimal allocation of manufacturing resources plays an essential role in the production process. However, most of the existing resource allocation methods are designed for standard cases, lacking a dynamic optimal allocation framework for resources that can guide actual production. Therefore, this paper proposes a dynamic allocation method for discrete job shop resources in the Internet of Things (IoT), which considers the uncertainty of machine states, and carbon emission. First, a data-driven job shop resource status monitoring framework under the IoT environment is proposed, considering the real-time status of job shop manufacturing resources. A dynamic configuration mechanism of manufacturing resources based on the configuration threshold is proposed. Then, a real-time state-driven multi-objective manufacturing resource optimization allocation model is established, taking machine tool energy consumption and tool wear as carbon emission sources and combined with the maximum completion time. An improved imperialist competitive algorithm (I-ICA) is proposed to solve the model. Finally, taking an actual production process of a discrete job shop as an example, the proposed algorithm is compared with other low-carbon multi-objective optimization algorithms, and the results show that the proposed method is superior to similar methods in terms of completion time and carbon emissions. In addition, the practicability and effectiveness of the proposed dynamic resource allocation method are verified in a machine failure situation.

A novel milling parameter optimization method based on improved deep reinforcement learning considering machining cost

The cutting parameters in the part machining process have a great impact on the energy efficiency and economy of the machining system. The cutting parameters in traditional machining are often selected according to the operator's experience, which lack attentions to energy saving and economy. Therefore, a milling process parameter optimization method based on deep reinforcement learning (DRL) is proposed in this paper. Taking the machining cost composed of cutting energy efficiency and machining time cost as the optimization goal, the spindle speed and feed speed under the combination of different cutting depth and cutting width parameters are optimized. Firstly, the machine tool energy consumption model is established by back propagation neural network (BPNN) regression method to realize the continuity of machine tool energy consumption state prediction, and the processing cost model is established as the optimization objective function. Then, the process parameter optimization problem is formally expressed as a Markov decision process (MDP), and the corresponding states, actions, reward functions and constraints are defined. Finally, combined with the machine tool power consumption model and machining cost model, the simulation environment is established, and the BP-TD3 method is proposed to solve the Markov decision problem of milling parameter optimization. Taking the machining center as an example, the aluminum alloy workpiece is milled. Compared with the classical optimization algorithm, the proposed method can save 95 % optimization calculation time, and ensure that the average processing cost after optimization is close to the minimum processing cost obtained by the classical optimization algorithm.

Energy benchmark for energy-efficient path planning of the automated guided vehicle

The automated guided vehicle (AGV) is a piece of promising advanced transport equipment that has been widely used in flexible manufacturing systems to increase productivity and automation. Previous studies about the AGV focused on improving the capacities of perception, navigation, and anti-collision as well as reducing the transport time, cost, and distance, but insufficient attention was paid to the energy consumption (EC) reduction of AGV. The energy benchmark is recognised as an effective analytical methodology and management tool that can improve energy efficiency. Nonetheless, research on the energy benchmark for the AGV is lacking. To finish a transport task, many AGV path plans are feasible, and we develop an energy benchmark to evaluate each path plan and select the energy-saving one. We also establish a dynamic rating system of energy efficiency which is consistent with the energy-saving potentials of the transport task. The case study shows that the transport EC is reduced by 10.98 %, validating the proposed energy benchmark methodology. In addition, the effects of AGV path plans on the EC of machine tools at the workstations are analysed. Lastly, we explore the relationship between the energy efficiency of AGV path plans and the locations of workstations.

A novel approach to model the energy consumption of machine tools for machining cylindrical parts

Machine tools plays a significant role in manufacturing industries and are unfortunately responsible for huge energy consumption and associated greenhouse gas emissions. In industry, the length and diameter are reduced to obtain the final dimensions of a cylindrical part. Typically, external turning operation is used to reduce the diameter of the part i.e. Constant-Material Removal Rate (CMRR) machining process and facing is conducted to reduce the length of the part i.e. Variable-Material Removal Rate (VMRR) machining process. In the past, energy evaluation models for machine tools are developed based on CMRR machining processes only, while the machining of a cylindrical part requires both CMRR and VMRR machining processes to manufacture the final product. In the present study, the energy consumption of a machine tool is divided into different energy modules: start-up, standby, spindle acceleration, idle, rapid positioning, air-cutting, CMRR machining process and VMRR machining process. Energy consumption models for each module are developed and integrated to establish the total energy consumption model for a machine tool. Experiments are conducted on a LMW-Smarturn CNC lathe machine tool in the dry and wet environment to obtain the fitting coefficients of the developed models for different energy modules. The validation test results show that the developed model's accuracy is more than 97 %. The developed model can be applied in the industry by the process planners to identify the most energy-efficient process plan before the actual machining of a cylindrical part.

Analysis of cutting technology and energy consumption model of polyethylene WPC

In order to improve the energy utilization rate and to reduce the energy consumption in the machining process of wood plastic composites (WPC), the influence relation among cutting parameters, cutting power and energy consumption is analyzed. The energy consumption of WPC in the milling process is studied in this article. The energy consumption model of the motor in the cutting process is established, and the trend of change on energy consumption of machine tool quantitatively divided into eight characteristic stages. The influence of different cutting parameters and milling methods on cutting energy consumption and cutting energy consumption per unit volume shows that feed rate, cutting speed, cutting width and cutting power feed rate, cutting speed, cutting width and cutting power change direct proportional. Feed rate, cutting width and cutting energy consumption per unit volume Feed rate, cutting width and cutting energy consumption per unit volume change inversely proportional. The change rule of cutting energy consumption of WPC is exposed. The theoretical prediction model of energy consumption is established based on the experimental data, which provides a scientific basis to realize the green processing of WPC with high efficiency and low energy consumption.

Feature-based optimization method integrating sequencing and cutting parameters for minimizing energy consumption of CNC machine tools

Feature-based optimization method integrating sequencing and cutting parameters for minimizing energy consumption of CNC machine tools

A Pareto-based hybrid iterated greedy algorithm for energy-efficient scheduling of distributed hybrid flowshop

Due to its practicality, hybrid flowshop scheduling problem (HFSP) with productivity objective has been extensively explored. However, studies on HFSP considering green objective in distributed production environment are quite limited. Moreover, the current manufacturing mode is gradually evolving toward distributed co-production mode. Thus, this paper investigated a distributed hybrid flowshop scheduling problem (DHFSP) with objectives of minimization the makespan and total energy consumption (TEC). To address this problem, this paper designed a Pareto-based multi-objective hybrid iterated greedy algorithm (MOHIG) by integrating the merits of genetic operator and iterated greedy heuristic. In this MOHIG, firstly, one cooperative initialization strategy is proposed to boost initial solutions’ quality based on the previous experience and rules. Secondly, one knowledge-based multi-objective local search method is invented to enhance the exploitation capability according to characteristics of problem. Thirdly, an energy-saving technique is developed to decrease the idle energy consumption of machine tools. Furthermore, the effectiveness of each improvement component of MOHIG is assessed by three common indicators. Finally, the proposed MOHIG algorithm is compared with other multi-objective optimization algorithms, including SPEA2, MOEA/D, and NSGAII. Experimental results indicate that the proposed MOHIG outperforms its compared algorithms in solving this problem. In addition, this research can better guide practical production in some certain environments.

Prediction of energy consumption of machine tools using multi-gene genetic programming

In the past, researchers have applied different analytical, numerical, and empirical modelling techniques to analyze energy consumption. In the present study, computational artificial intelligence-based Multi-Gene Genetic Programming is used to model the energy consumption of machine tool. The experiments were performed on a heavy-duty HMT lathe machine tool under a dry environment in the interest of sustainable machining. The Taguchi full factorial orthogonal array L27 was used to develop the experimental plan. The power consumption of the machine tool was measured using a Fluke 435 power analyzer. The dataset was split into training and testing data based on the 80–20 ratio. Further, 99.77% goodness of fit was achieved in training and 98.60% for testing the model. The adequacy of the model was tested by determining four error indices i.e. root means square error, mean absolute error, sum of squared error, and mean square error. The model is validated by conducting two hypothesis tests, t-test and f-test on predicted data. The hypothesis results confirm the model’s goodness of fit statistically, indicating that the proposed model can be easily applied in the manufacturing industry to predict energy consumption.

Modelling of spindle energy consumption in CNC milling

In manufacturing industries, machine tools are frequently used and required a lot of energy to work. Spindle acceleration is a common process when machine tools are in use. It generates a high-energy intensive power peak. The total energy consumption of machine tools in the machining process is strongly affected by these high-power peaks of short duration. Many researchers have overlooked the energy consumption of spindle acceleration resulting into inaccuracies in the prediction of overall energy consumption of machine tools. Therefore, the present study aims to develop a model to predict the spindle acceleration energy consumption of computer numerical control (CNC) milling machines. The proposed model is based on the principle of spindle motor control and includes the computation of moment of inertia of the spindle drive system. To validate the effectiveness of the proposed model, machining experiments are carried out on a CNC milling machine. Without performing time-consuming experiments, the proposed models can be utilized to estimate the power, time, and energy consumption of spindle acceleration. The proposed model helps to determine total energy consumption during machining process correctly.

Modelling the effect of slide table mass on machine tool energy consumption: The role of lightweighting

Much of the environmental footprint of a machine tool is due to the energy consumed during its use phase. A promising strategy for reducing this energy consumption is to minimize the mass of components through changes in the design of the machine tool. This paper explores the lightweighting of the machine slides (or tables) to achieve energy savings. An energy model is developed which considers the table and workpiece mass, the cutting force, and the motor that drives the table. A case study is carried out on a three-axis vertical milling machine to explore the energy savings that can be realized via lightweighting a slide table. The model is used to compare the energy consumption of a solid table and a lightweight table. Results show a potential energy savings of up to 38% in terms of the energy required to move the table. The model is verified using experimental data already found in the literature. It was observed that the model has a lower prediction capability when the table speed and power demand are very low but as the speed and power increase the prediction capability increases significantly.

A Novel CNC Milling Energy Consumption Prediction Method Based on Program Parsing and Parallel Neural Network

Accurate and rapid prediction of the energy consumption of CNC machining is an effective means to realize the lean management of CNC machine tools energy consumption as well as to achieve the sustainable development of the manufacturing industry. Aiming at the drawbacks of existing CNC milling energy consumption prediction methods in terms of efficiency and precision, a novel milling energy consumption prediction method based on program parsing and parallel neural network is proposed. Firstly, the relationship between CNC program and energy consumption of CNC machine tool is analyzed. Based on the structural characteristics of the CNC program, an automatic parsing algorithm for the CNC program is proposed. Moreover, based on the improved parallel neural network, the mapping relationship between the energy consumption parameters of each CNC instruction and the milling energy consumption is constructed. Finally, the proposed method is compared with the literature to verify the superiority of the proposed method in terms of prediction efficiency and accuracy, and the practicability of the method is verified through the case study. The proposed method lays the foundation for efficient and low-consumption process planning and energy efficiency improvement of machine tools and is conducive to the sustainable development of the environment.

Building models for coupled thermo elastic problems in different software architectures using a new model exchange format

AbstractTo reduce the energy consumption of machine tools while preserving the productivity and quality, the deformation caused by thermal loads must be taken into account. Two approaches to reach this goal are the integration of model predictive error correction algorithms into the machine controller and a machine design that mitigates unwanted thermo‐elastic behaviour.Both approaches demand a profound understanding of the machine behaviour under different thermal load scenarios by simulation. The computation of representative distortions requires accurate models of the machine components, relevant physical phenomena and appropriate numerical methods, ranging from adaptive finite element discretization to model order reduction methods. In most cases, these methods and models are developed independently by different scientific groups, whereas the total goal of an improved operation of the machine tool demands the joint knowledge of all teams within the same machine model.This contribution, introduces an open source format for a seamless model exchange between the scientific groups. It bases on the standardized and machine readable JSON format. The JSON scheme comprises of all necessary model information. The main use cases, benefits and drawbacks are outlined. The file structure is briefly described. Design decisions and problems are discussed. After that, a work flow using the new format to calculate the thermal behaviour of a machine tool is shown. Different modelling approaches to calculate the thermal field are compared. Finally the next development steps and further use cases for the format will be outlined.