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

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

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

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