Renzhong Tang

Therblig-based energy demand modeling methodology of machining process to support intelligent manufacturing

Energy efficiency has become an important factor that should be included in Intelligent Manufacturing due to the increasingly rising energy price and severe energy shortage issues. Energy demand modeling method is the foundation of improving the energy efficiency of manufacturing; therefore, an energy demand modeling methodology for machining processes is proposed. In this method, machining processes are divided into a series of activities, and Therblig, as one of the basic concepts of Motion study, is introduced to represent the basic energy demand unit. Moreover, a mathematical model of energy demand of machining processes is established by linking the activity and Therblig with machining state. Finally, case studies are performed to illustrate the validity and feasibility of the proposed methodology.

Estimating machining-related energy consumption of parts at the design phase based on feature technology

To overcome the difficulties in previous researches about energy-efficient design of parts, a method to estimate machining-related energy consumption of parts at the design phase is proposed. The binary tree is constructed to describe the structure of a part, and each node in the binary tree represents one feature in the part. The material embodied energy, theoretical cutting energy consumption and air-cutting energy consumption of a feature can be calculated based on its design and manufacturing parameters. At the design phase, manufacturing parameters of a feature can be obtained by the method of feature mapping from design parameters. By adding up above three types of energy consumption, total energy consumption of a feature can be calculated. Further, by adding up total energy consumption of all features in a part, the energy consumption of this part can be estimated. The proposed method was demonstrated by estimating the energy consumption of a shaft part designed by an auto parts manufacturer, and meanwhile the measured energy consumption of the shaft part was acquired by experimental measurement. The estimation accuracy is analysed and verified by comparing the estimated value and measured value.

Machining activity extraction and energy attributes inheritance method to support intelligent energy estimation of machining process

An energy-efficient intelligent manufacturing system could significantly save energy compared to traditional intelligent manufacturing systems that do not consider energy issues. Intelligent energy estimation of machining processes is the foundation of the energy-efficient intelligent manufacturing system. This paper proposes a method for machining activity extraction and energy attributes inheritance to support the intelligent energy estimation of machining processes. Fifteen machining activities and their energy attributes are defined according to their operating and energy consumption characteristics. Activities and energy attributes are extracted mainly from NC program supplemented with blank dimensional information. An effective extraction method of activities and energy attributes is the basis for the intelligent energy calculating of machining process. Based on an investigation on the extraction procedure of activities and energy attributes, energy attributes inheritance method is further discussed. Four types of energy attribute inheritance rules are summarized according to the different inheritance characteristics. Based on these four types of inheritance rules, the energy attributes can be transmitted from activity to Therblig as effective inputs of Therblig-based energy model of machining processes. The proposed methodology is finally demonstrated through two machining cases.

Resource Utilization in Cloud Manufacturing – An Energy Perspective

Living a “low-carbon” life has been widely recognized and is gradually adopted by the public. Such a trend becomes one of the main drivers for manufacturing innovations. Meanwhile, to meet the emerging requirements, such as providing highly customized product, building flexible and collaborative production, cloud manufacturing is proposed in recent years. A close examination of its environmental benefits is needed. In this paper, resource utilization is focused. In the architecture of cloud manufacturing, energy consumption is analyzed and re-evaluated systematically, including energy characteristics, added energy segments and required functions. Three key merits are identified, better resource integration and optimization, higher resource utility rate, and facilitated knowledge sharing mechanism. However, these improvements can be cancelled in an energy-unattended cloud manufacturing system, for example, ignorance of energy data or inadequate energy models. A framework is then designed for performing energy analysis in a cloud environment. Conclusions are given at the end.

Remote production dashboard: A real-time and online control technology for discrete manufacturing process based on knowledge service

A viable and affordable real-time and online control technology named remote production dashboard (RPD) for discrete manufacturing process based on knowledge service is proposed. Firstly, the basic framework of RPD consisted of production environment layer, data process layer, knowledge service layer and man-machine interaction layer is constructed. Secondly, the principles of two key technologies used in RPD including real-time manufacturing field data capture and knowledge service are presented. Thirdly, the mechanism of RPD is described in detail and two running instances are given out. Finally, a prototype system is developed and applied to an auto motor manufacturer preliminarily. The RPD system facilitates the visible management of plant shop thus enhances the operational efficiency in a real-time manner, and is proved to be a promising next generation advanced manufacturing technology.

Energy Consumption Prediction System of Mechanical Processes Based on Empirical Models and Computer-Aided Manufacturing

Energy consumption prediction at the process planning stage is the basis of mechanical process optimization aiming at energy saving and reducing carbon emission. The accuracy and efficiency of the prediction method will be the most concerning issues. This paper presents an energy consumption prediction system of mechanical processes based on empirical models and computer aided manufacturing (CAM). The system was developed based on analysis of energy-related data and data acquiring methods. The energy consumption sources of mechanical processes are divided into two parts: energy of auxiliary machine movements and intrinsic process movements. Considering data sources, there are two kinds of data acquiring methods: acquiring data from database or from CAM files. Process energy state is introduced to support calculation of energy consumption and presentation of calculation results. Example of the system was developed based on Microsoft SQL Server 2008 and UGS NX 7.0, and several examples of energy prediction of mechanical processes were also presented. The results demonstrate that the proposed system developing method is effective in predicting energy consumption of mechanical processes with high accuracy and high efficiency.

Energy modeling and a method for reducing energy loss due to cutting load during machining operations

In a typical part manufacturing system, machining operations represent a major proportion of the total energy consumption. The energy consumption (in the form of electricity power) of a machining operation can be divided into four types, that is, standby power, operational power, cutting power and power loss due to cutting load. Power loss due to cutting load includes the power loss caused by the friction of mechanical transmission and the power lost in the motor when the cutting load is applied to the spindle system. While the first three types of power consumption have been studied intensively by previous researchers, the power loss due to cutting load, which accounts for up to 20% of the cutting power consumption during machining operations, has received relatively less attention. This article proposes a novel model to characterize power loss due to cutting load, in which the power lost in the mechanical transmission and in the spindle motor are analyzed and modeled separately. Cutting tests have been carried out to validate the proposed model using two numerical control lathe machines. And a method has been developed for reducing energy loss caused by cutting load, which includes cutting force prediction, power loss due to cutting load prediction and decision making. The method was evaluated through its application in the process design for a shaft part, and the results show a significant saving of up to 70.8% of energy loss caused by cutting load.

A reinforcement learning based approach for multi-projects scheduling in cloud manufacturing

This paper discussed the multi-projects scheduling problem in Cloud Manufacturing system, where each of the projects is a set of interrelated tasks, and these projects need to be scheduled timely and carefully. However, scheduling massive projects can be challenging due to the uneven distribution of the services and the uncertain arrival of projects. Therefore, we (1) established a dual-objectives optimisation model to minimise both the total makespan and the logistical distance; (2) proposed a Reinforcement Learning based Assigning Policy (RLAP) approach to obtain non-dominated solution set; (3) designed a dynamic state representing an algorithm for agents to determine their decision environment when using RLAP. Experiment results show that RLAP can adjust the distribution of service load according to the nearby tasks, and the schedule quality is improved by and compared with NSGA-II and Q-learning, respectively. Besides, the RLAP method has the ability to schedule stochastically arriving projects.