Machine Tool Power Research Articles

A rapid modelling method for machine tool power consumption using transfer learning

A rapid modelling method for machine tool power consumption using transfer learning

Data mining approach for production order identification in load profiles of machine tools: A change-point and clustering based analysis

With growing awareness of sustainable manufacturing and rising energy costs, the monitoring of energy-related quantities in production is expanded. Measurements of the power consumption of production systems are used to identify energy efficiency potentials or to allocate energy use and associated costs. However, time series of the power consumption of machine tools contain more information. Load profiles of production systems enable drawing conclusions on the specific production order carried out. Time stamps for certain machining operations can be extracted and used to link the machine tool's power consumption to a specific production order. The linkage of this data can assist the order-specific allocation of energy use or associated emissions. This paper presents a data mining approach to gain information on the executed production orders from the load profiles of a machine tool. For this purpose, load profiles of a machine tool are pre-processed and segmented based on change points. Subsequently, the resulting sequences are clustered with respect to their similarity. These clusters contain segments of machining cycles in which the same workpieces were produced. By matching the cluster sizes with the known quantity of the executed orders, associated time series segments are selected and linked to the manufacturing order. The approach is implemented in a Python framework and carried out with data from milling and turning machining centers monitored in an industrial environment. Time stamps for machining cycles can be identified reliably. No training data is required for the recognition of recurring patterns at the load profile of a machine tool, but production orders need to be distinguishable at least by workpiece quantity, which was given with all tested real data.

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.

Adapted versus Projected Machining Centers Energy Consumption for MQL Technique

Cutting fluid application is a vital technique to achieve the expected result in most machining processes. However, besides having a high cost, the cutting fluid application also represents a risk to the environment due the oil chemical characteristics. The Minimum Quantity Lubrication (MQL) is an alternative when looking for reduction of oil consumption and a more environmentally friendly process. A literature review has shown that many predictive models of machine tool power consumption do not consider the consumption of the required air compressor in MQL. This paper’s motivation was testing these models to predict the energy consumption. It was observed that the amount of consumed energy in the cutting is lower when compared to the consumption of basic components of the machining center in both cases. Using the MQL, the tool life showed a decrease. However, it allows the feed to increase and reduces the energy consumption.

Tool tip cutting specific energy prediction model and the influence of machining parameters and tool wear in milling

Most of the existing energy-consumption models of machine tools are related to specific machine components and hence cannot be applied to other machine tools with different specifications. In order to help operators optimize machining parameters for improving energy efficiency, the tool tip cutting specific energy prediction model based on machining parameters and tool wear in milling is developed, which is independent of the standby power of machine tools and the spindle no-load power. Then, the prediction accuracy of the proposed model is verified with dry milling AISI 1045 steel experiments. Finally, the influence of machining parameters and tool wear on tool tip cutting specific energy is studied. The developed model is independent of machine components, so it can reveal the influence of machining parameters and tool wear on tool tip cutting specific energy. The tool tip cutting specific energy reduces with the increase in the cutting depth, side cutting depth, feed rate, and cutting speed, while increases linearly as the tool wears gradually. The research results are helpful to formulate efficient and energy-saving processing schemes on various milling machines.

Specific energy consumption prediction model of CNC machine tools based on tool wear

ABSTRACT The global energy crisis and climate warming are becoming more and more serious. How to reduce energy consumption and environmental pollution to achieve low-carbon manufacturing is an urgent problem to solve. Tool wear leads to the increase of cutting force and cutting power of machine tools obviously. So the influence of tool wear on energy consumption of machine tools cannot be ignored. Firstly, the power of CNC machine tool in cutting stage is divided into standby power, cutting material power and spindle no-load power in the paper. Then, the specific energy consumption prediction model of CNC machine tools based on tool wear is developed. Furthermore, the proposed model is verified with dry milling 45# steel experiments, and the prediction accuracy can reach 98.2% according to material removal rate, spindle speed and tool wear. Finally, the influence of processing parameters and tool wear on specific energy consumption of machine tools is studied. The research is helpful to optimize the processing parameters and tool conditions to reduce the energy consumption of machine tools.

Towards less energy intensive heavy-duty machine tools: Power consumption characteristics and energy-saving strategies

Energy conservation in manufacturing sector has received growing attention in an increasingly more carbon-restrained world due to the great concerns over climate change. The heavy-duty machine tool is usually the critical equipment in a factory, which consumes much more power than normal-sized machine tool but received less attention regarding energy saving. In this research, a generic power consumption model was developed from three hierarchies (i.e. system boundary definition, generic power consumption framework and detailed power consumption), which integrates the design parameters of the machine tool thus enabling the prediction of the power consumption even when the machine tool is physically unavailable. In addition, the relations between the power consumed in air-cutting and cutting states were also discussed, which can significantly simplify the study on machine tool power characteristics. The proposed model was verified by experiments and five power consumption characteristics of the tested heavy-duty machine tools were summarised. Additionally, four strategies for designing an energy-efficient machine tool and four tactics for using an existing machine tool more energy efficiently were proposed. This study represents part of a major comprehensive energy conservation research programme for heavy-duty machine tools, which aims to find solutions to improved energy efficiency for the manufacturing industry.

Energy Consumption Characteristics and Influence on Surface Quality in Milling

The global energy crisis and climate change are becoming increasingly severe. Therefore, how to reduce energy consumption in machining and improve surface quality of workpiece is an urgent problem to be solved. The energy consumption characteristics and the influence of cutting parameters on surface quality in 45# steel milling are studied in detail. Firstly, the machine tool power equation and the specific energy consumption model in cutting stage are developed, so the machine tool energy consumption can be predicted accurately with the material removal volume in milling. Through the analysis of machine tool specific energy consumption and surface roughness, the grey correlation analysis method is used to optimize the processing parameters in milling. The research is helpful to select the processing parameters to reduce energy consumption and improve surface quality simultaneously.

Optimum Swept Angle Estimation based on the Specific Cutting Energy in Milling AISI 1045 Steel Alloy

Mechanical machining processes are common manufacturing strategies to re-shape materials to desired specification. The mechanistic approach has revealed the mechanics of the machining processes with various parameters determined. The aim of this work is to investigate the impact of swept angle optimization and their influence on the specific cutting energy in milling AISI 1045 steel alloy. This is achieved by varying the step over at different feed rate values in order to determine the optimization criterion for machining. It was observed that an optimum swept angle of 31.8o was appropriate in the elimination of ploughing effect and reducing the specific cutting energy to an optimised minimum value. However, higher swept angle of 41.4o increases the specific cutting energy with a higher machine tool power. This is attributable to the reduction in the cycle time caused by shorter toolpath length. The results obtained further elucidate the knowledge base for the determinations of optimum parameters for sustainable machining and resource efficiency of manufactured products.

Research on Power Supply and Control System of WEDM Cutting of Stress-notches in Fracture Splitting Processing of Connecting Rod

Stress-notches cutting is one of the key processes in fracture splitting processing of connecting rod. This paper has put forward a new process of WEDM notches cutting, with the advantages of free threading, low cost, high efficiency and convenient maintenance. In order to obtain stable cutting quality and higher processing efficiency, studies are carried out on the machine tool power supply and control system. Higher-voltage circuit for the pulse power supply is design ed in the form of switching power supply with dual-controller, which brings the advantages of higher degree of integration, less weight, smaller volume, and cost reduction. Moreover, the output voltage is continuously adjustable and has the function of voltage regulator, thus better processing quality can be achieved. The control system is designed to obtain constant depth of notches while cutting, regardless of various sizes and tolerances of inner holes of the connecting rods, thus it can eliminate the negative influences on fracture splitting process caused by inconsistency of stress notches. Experimental results show that the presented WEDM power supply and control system can fully meet the requirements of notches cutting in fracture splitting processing of connecting rod.

Energy Reduction of Machine Tools

Machine tools, which are used in factories for long hours and many years, have a great influence on power consumption of the factories. Therefore, reducing machine tool power consumption is one of the important subjects for today’s machine tool manufacturers. In this paper, we will report on our ongoing efforts to improve energy efficiency of machine tools.

Integration of Tool Geometry in Prediction of Cutting Forces During Milling of Hard Materials

Knowledge of the cutting forces owing to a predictive model is very interesting with respect to the choice of a machine tool power, the cutting tools, the optimization of cutting conditions for a given machining operation or the control of the occurrence of vibrations. It could also be helpful in online wear monitoring during milling. As this technique is usually based on a limit level of the cutting forces, it could allow the number of long and expensive tests to be limited and the best tool geometry to obtain quasi-constant and low cutting forces, which lead to a reduced tool wear, and consequently, a better tool-life, to be found. The objective of this study is the prediction of the effects of the rake and helix angles on the cutting force variations, and to integrate it into a cutting relation. Tests were made on X38 CrMoV 5 (AISI H11) die and mould steel, with solid carbide milling cutters of various rake angles. A specific configuration has been used to create obliquity. Cutting forces were measured for various feed rates. The results have improved the cutting force model, making it possible to choose the optimal rake and helix angles in a particular machining operation.

Intégration de la géométrie d'outil dans la prédiction des efforts de coupe en fraisage de matériaux durs

The knowledge of cutting forces thanks to a, predictive model is very interesting for the choice of a machine tool power, the cutting tools, the optimization of cutting conditions or the control of the occurence of vibrations. It could also be helpful in on-line wear monitoring during milling. This could allow to decrease the number of tests, and to determine the best tool geometry, in relation with a limit level of forces. The objective of the study is the prediction of effects of rake and helix angles on cutting forces variations, and to integrate it in a cutting relation. Tests were made on X 38CrMoV 5 (AISI H11) die and mold steel, with solid carbide milling cutters of various rake angles and straight flutes. A specific configuration has been used to create obliquity. Cutting forces were measured for various feed rates. The results have improved the cutting force model, and allowed to apply it to real milling configurations.

MODELLING OF TOOL GEOMETRY IN PREDICTION OF CUTTING FORCES DURING MILLING OF HARD MATERIALS

ABSTRACT The choice of a machine tool power, cutting tools, or optimization of cutting conditions, and also information of some technical data for CAD/CAM systems, could be facilitated by the knowledge of cutting forces during a machining operation. It could also be helpful to control the occurrence of vibrations or to improve tool-life. The objective of this study is to predict the effects of rake and helix angles on the cutting forces variations during milling of hard materials and to integrate it into a cutting relation, so that it could be possible to limit the experimental testing while providing a good idea of the cutting forces level. Tests were made on X38 CrMoV 5 (AISI H11) die and mould steel, with solid carbide milling cutters of various rake and helix angles. Cutting forces were measured for various feed rates. A comparison between modelling and experiment is presented.

Investigation of the cutting parameters depending on process sound during turning of AISI 304 austenitic stainless steel

In this work, determination of the best suitable cutting conditions and cutting parameters during machining of AISI 304 stainless steels has been aimed by taking into consideration process sound. For determination of the best cutting parameters in the stainless steels machining, the samples which were prepared to be used in the experiment, 200 mm in length and 30 mm in diameter, were machined in a CNC turning centre. Each part of samples was machined through the 150 mm length and the depth of cut was 2.5 mm. Turning tests were performed with three different feed rates (0.2, 0.25, 0.3 mm/rev) at each cutting speed, 120, 135, 150, 165, 180 m/min. During experiments, process sounds were recorded by a computer incorporating a microphone. The best cutting speed and feed rate were determined according to flank wear, built up edge, chip form, surface roughness of the machined samples and machine tool power consumption. The ideal cutting parameters and cutting process sounds obtained were compared. In this way, the best cutting parameters could be determined depending on the sound. Finally, cutting speed of 165 m/min and feed rate of 0.25 mm/rev gave the best results and the analyses of the process sound confirmed these values.

Investigation of the cutting parameters depending on process sound during turning of AISI 304 austenitic stainless steel

In this work, determination of the best suitable cutting conditions and cutting parameters during machining of AISI 304 stainless steels has been aimed by taking into consideration process sound. For determination of the best cutting parameters in the stainless steels machining, the samples which were prepared to be used in the experiment, 200 mm in length and 30 mm in diameter, were machined in a CNC turning centre. Each part of samples was machined through the 150 mm length and the depth of cut was 2.5 mm. Turning tests were performed with three different feed rates (0.2, 0.25, 0.3 mm/rev) at each cutting speed, 120, 135, 150, 165, 180 m/min. During experiments, process sounds were recorded by a computer incorporating a microphone. The best cutting speed and feed rate were determined according to flank wear, built up edge, chip form, surface roughness of the machined samples and machine tool power consumption. The ideal cutting parameters and cutting process sounds obtained were compared. In this way, the best cutting parameters could be determined depending on the sound. Finally, cutting speed of 165 m/min and feed rate of 0.25 mm/rev gave the best results and the analyses of the process sound confirmed these values.

An Optimum Two-tool Solution for Milling 2½D Features from Technological and Geometric Viewpoints

This paper describes a procedure to determine the optimum pair of tools that can machine a milling feature with soft and/or hard boundaries. The optimum cutting conditions, as well as the actual distances traversed by the two tools, are used in the determination of the total machining cost. In addition to technological constraints such as machine tool power, geometrical constraints including minimum concave radius, bottleneck width and entry distance are determined from the Voronoi diagram. The paper also describes a novel method to determine the stock machined by the larger tool. An example is included to illustrate the method.

Torsional vibrations of machine tool power drives as influenced by dampers and flywheels

Torsional vibrations of machine tool power drives as influenced by dampers and flywheels