Material Removal Power Research Articles

Comparison of different approaches for predicting material removal power in milling process

Comparison of different approaches for predicting material removal power in milling process

Developing a new energy performance indicator for the spindle system based on power flow analysis

The evaluation of the energy performance of spindle systems with a proper metric is extremely important for energy-efficient machine tools. However, there is a lack of appropriate index to achieve this evaluation. To close this gap, the additional power requirement, which is defined as the discrepancy between the material removal power and the power gap of the spindle system from air-cutting state to cutting state, was proposed as a new indicator based on a detailed power flow analysis. The additional power requirement universally exists in machining and results from the efficiency and power losses of the system, which makes it a good energy performance indicator candidate. A theoretical model for the additional power requirement of spindle systems was built and verified in a lathe machine, through which two causes of additional power requirement were pointed: less than 100% efficiency throughout the system and efficiency change of the spindle motor under a dynamic load and a variable speed. The first cause has a positive effect on the additional power requirement, whereas the second one generally plays a negative role. Furthermore, the additional power requirement can be negative because of the negative effect, which is different from previous studies. The close relation between the additional power requirement and the energy performance of the spindle is demonstrated by an in-depth analysis. This work is an important building block for energy-efficient machine tools.

An investigation into methods for predicting material removal energy consumption in turning

Abstract The wide use of machining processes has imposed a large pressure on environment due to energy consumption and related carbon emissions. The total power required in machining include power consumed by the machine before it starts cutting and power consumed to remove material from workpiece. Accurate prediction of energy consumption in machining is the basis for energy reduction. This paper investigates the prediction accuracy of the material removal power in turning processes, which could vary a lot due to different methods used for prediction. Three methods, namely the specific energy based method, cutting force based method and exponential function based method are considered together with model coefficients obtained from literature and experiments. The methods have been applied to a cylindrical turning of three types of workpiece materials (carbon steel, aluminum and ductile iron). Methods with model coefficients obtained from experiments could achieve a higher prediction accuracy than those from literature, which can be explained by the inability of the coefficients from literature to match the specific machining conditions. When the coefficients are obtained from literature, the prediction accuracy is largely dependent on the sources of coefficients and there is no definitive dominance of one approach over another. With model coefficients from experiments, the cutting force based model achieves the best accuracy, followed by the exponential function based method and specific energy based method. Furthermore, the power prediction methods can be used in process design stage to support energy consumption reduction of a machining process.

An improved cutting power model of machine tools in milling process

Establishing a rapid, accurate, and practical energy consumption evaluation model for machine tools is essential to save energy and increase profits in the manufacturing industry. Relationships between spindle rotation speed, cutting parameters, material removal rate, specific energy consumption, cutting power, and material removal power were experimentally analyzed, and the limitation and the limitation and differences between several machine tools’ cutting power evaluation models were discussed. Cutting parameters or material removal rate were regarded as independent variable in those models. By comparing the models’ fitting and predicted results, it draw a conclusion that the model treating cutting parameters as independent variable had greater accuracy but depends on a large quantity of experimental data. The model treating material removal rate as independent variable can also obtain a good fit and reduce the number of necessary experiments. Therefore, it is a rapid method to estimate the cutting energy consumption of machine tools for the latter model. In addition, the paper puts forward an improved cutting power model, which considers the influence of the spindle rotation speed on the material removal power during the milling process. The proposed model can predict a milling machine’s cutting power more accurately.

Empirical power-consumption model for material removal in three-axis milling

Abstract Environmental impact concerns have prompted various ecodirectives and legislation requiring manufacturing industries to take a greater role in reducing energy consumption and carbon emission. Research has indicated that monitoring and assessment of energy consumption is a basic approach that can be used to achieve energy savings. In this study, we decomposed the energy elements of a milling machine used for cutting to model additional material-removal power caused by the cutting load with respect to various process parameters. Because the material-removal power is directly related to the cutting force, current and power monitoring has been a good solution for indirect monitoring system. However, the effect of the process parameters on the material-removal power is more complex. In this study, the material-removal power consisted of ∼7.6% of the total power consumption of the machine tool, and increased with the flank wear of the tool. The difference between power under severe-wear and slight-wear conditions was empirically modeled using response surface methodology. The cutting power varied in terms of all process parameters, and its increase caused by the tool wear depended on the process conditions. Though both the material-removal power and its difference have a positive correlation with the material-removal rate, the absolute difference was not proportional to the material-removal power itself. With the constructed model, the overall energy consumption of the machine tool could be estimated more precisely, and the tool-wear states could be more accurately predicted in accordance with various process parameters.

Tribological behaviour and acoustic emissions of alumina, silicon nitride and SAE52100 under dry sliding

The friction, wear and acoustic emission behaviour of various combinations of alumina, silicon nitride, and SAE52100 steel, operating under dry sliding conditions, was investigated. A designed ball-on-flat-disc type of tribometer was used to conduct these experiments. This apparatus, equipped with a force sensor, using silicon strain gauges, measured simultaneously the normal load and friction force. Both forces were used to determine the real-time value of the dynamic coefficient of friction. The AE signal arising from the interaction of the surfaces in dynamic contact was also detected and a data acquisition system was used to gather this signal as well as the outputs from the force sensor, at high frequency. The effects of test duration, sliding speed and normal load on the above mentioned tribological parameters were evaluated. The interest of this study further extended to assess the correlations that may exist between the integrated rms acoustic signal (AE) and the friction mechanisms, wear volume, friction work as well as the material removal power. Under the specific conditions of the present experiments, no consistent relation was found between the variations of AE and corresponding dynamic coefficient of friction (COF) as function of time. The variation of COF and wear rate, obtained considering a fixed total sliding distance of 500 m, as function of a range of sliding speed (0.05–2.5 m/s) and normal load (5–40 N) are presented. It was found that the test duration has an important impact on wear results of the experiments conducted at different sliding speeds and fixed travelling distance. More expected behaviour was observed when the relationships between the AE and wear volume, friction work, and material removal power were investigated considering the data obtained at different loadings and fixed sliding speed. Some models representing interesting relationships which could be used for predicting tribological properties in the case of practical applications, similar to the tribo-systems investigated in this study, are proposed.