Experimental study on energy consumption of computer numerical control machine tools

Machining processes are responsible for substantial environmental impacts due to their great energy consumption. Accurately characterizing the energy consumption of machining processes is a starting point to increase manufacturing energy efficiency and reduce their associated environmental impacts. The energy calculation of machining processes depends on the availability of energy supply data of machine tools. However, the energy supply can vary greatly among different types of machine tools so that it is difficult to obtain the energy data theoretically. The aim of this research was to investigate the energy characteristics and obtain the power models of computer numerical control (CNC) machine tools through an experimental study. Four CNC lathes, two CNC milling machines and one machining center were selected for experiments. Power consumption of non-cutting motions and material removal was measured and compared for the selected machine tools. Here, non-cutting motions include standby, cutting fluid spraying, spindle rotation and feeding operations of machine tools. Material removal includes turning and milling. Results show that the power consumption of non-cutting motions and milling is dependent on machine tools while the power consumption of turning is almost independent from the machine tools. The results imply that the energy saving potential of machining processes is tremendous

A framework for smart production-logistics systems based on CPS and industrial IoT

Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems

Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor

The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities

Therblig-embedded value stream mapping method for lean energy machining

To improve energy efficiency, extensive studies have focused on the cutting parameters optimization in the machining process. Actually, non-cutting activities (NCA) occur frequently during machining and this is a promising way to save energy through optimizing NCA without changing the cutting parameters. However, it is difficult for the existing methods to accurately determine and reduce the energy wastes (EW) in NCA. To fill this gap, a novel Therblig-embedded Value Stream Mapping (TVSM) method is proposed to improve the energy transparency and clearly show and reduce the EW in NCA. The Future-State-Map (FSM) of TVSM can be built by minimizing non-cutting activities and Therbligs. By implementing the FSM, time and energy efficiencies can be improved without decreasing the machining quality, which is consistent with the goal of lean energy machining. The method is validated by a machining case study, the results show that the total energy is reduced by 7.65%, and the time efficiency of the value-added activities is improved by 8.12% , and the energy efficiency of value-added activities and Therbligs are raised by 4.95% and 1.58%, respectively. This approach can be applied to reduce the EW of NCA, to support designers to design high energy efficiency machining processes during process planning

An investigation into reducing the spindle acceleration energy consumption of machine tools

Machine tools are widely used in the manufacturing industry, and consume large amount of energy. Spindle acceleration appears frequently while machine tools are working. It produces power peak which is highly energy intensive. As a result, a considerable amount of energy is consumed by this acceleration during the use phase of machine tools. However, there is still a lack of understanding of the energy consumption of spindle acceleration. Therefore, this research aims to model the spindle acceleration energy consumption of computer numerical control (CNC) lathes, and to investigate potential approaches to reduce this part of consumption. The proposed model is based on the principle of spindle motor control and includes the calculation of moment of inertia for spindle drive system. Experiments are carried out based on a CNC lathe to validate the proposed model. The approaches for reducing the spindle acceleration energy consumption were developed. On the machine level, the approaches include avoiding unnecessary stopping and restarting of the spindle, shortening the acceleration time, lightweight design, proper use and maintenance of the spindle. On the system level, a machine tool selection criterion is developed for energy saving. Results show that the energy can be reduced by 10.6% to more than 50% using these approaches, most of which are practical and easy to implement

Assessment of multi-air emissions: case of particulate matter (dust), SO2, NOx and CO2 from iron and steel industry of China

Industrial activities are generally energy and air emissions intensive, requiring bulky inputs of raw materials and fossil fuels and emitting huge waste gases including particulate matter (PM, or dust), sulphur dioxide (SO2), nitrogen oxides (NOx), carbon dioxide (CO2), and other substances, which are severely damaging the environment. Many studies have been carried out on the quantification of the concentrations of these air emissions. Although there are studies published on the co-effect of multi-air emissions, a more fair and comprehensive method for assessing the environmental impact of multi-air emissions is still lacking, which can simultaneously consider the flow rate of waste gases, the availability of emitting sources and the concentrations of all emission substances. In this work, a Total Environmental Impact Score (TEIS) approach is proposed to assess the environmental impact of the main industrial processes of an integrated iron and steel site located in the northeast of China. Besides the concentration of each air emission substance, this TEIS approach also combines the flow rate of waste gases and the availability of emitting sources. It is shown that the processes in descending order by the values of TEIS are sintering, ironmaking, steelmaking, thermal power, steel rolling, and coking, with the values of 17.57, 16.68, 10.86, 10.43, 9.60 and 9.27, respectively. In addition, a sensitivity analysis was conducted, indicating that the TEIS order is almost the same with the variation of 10% in the permissible CO2 concentration limit and the weight of each air emission substance. The effects of emitting source availability and waste gas flow rate on the TEIS cannot be neglected in the environmental impact assessment

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

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

A new method of predicting the energy consumption of additive manufacturing considering the component working state

With the increase in environmental awareness, coupled with an emphasis on environmental policy, achieving sustainable manufacturing is increasingly important. Additive manufacturing (AM) is an attractive technology for achieving sustainable manufacturing. However, with the diversity of AM types and various working states of machines’ components, a general method to forecast the energy consumption of AM is lacking. This paper proposes a new model considering the power of each component, the time of each process and the working state of each component to predict the energy consumption. Fused deposition modeling, which is a typical AM process, was selected to demonstrate the effectiveness of the proposed model. It was found that the proposed model had a higher prediction accuracy compared to the specific energy model and the process-based energy consumption model. The proposed model could be easily integrated into the software to visualize the printing time and energy consumption of each process in each component, and, further, provide a reference for coordinating the optimization of parts’ quality and energy consumptio

The Fungal CYP51s: Their Functions, Structures, Related Drug Resistance, and Inhibitors

CYP51 (Erg11) belongs to the cytochrome P450 monooxygenase (CYP) superfamily and mediates a crucial step of the synthesis of ergosterol, which is a fungal-specific sterol. It is also the target of azole drugs in clinical practice. In recent years, researches on fungal CYP51 have stepped into a new stage attributing to the discovery of crystal structures of the homologs in Candida albicans, Cryptococcus neoformans and Aspergillus fumigatus. This review summarizes the functions, structures of fungal CYP51 proteins, and the inhibitors targeting these homologs. In particular, several drug-resistant mechanisms associated with the fungal CYP51s are introduced. The sequences and crystal structures of CYP51 proteins in different fungal species are also compared. These will provide new insights for the advancement of research on antifungal agents