Jingxiang Lv

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.

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.

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.

CPS-Based Smart Control Model for Shopfloor Material Handling

Automated guided vehicles (AGVs) have been widely used in manufacturing and supply chain management for material handling. The efficiency of the material handling process has been the bottleneck of the production manufacturing. By applying maturing technologies such as sensing, cloud computing, and wireless communication, the efficiency and the reliability of the material delivery could be enhanced. In this paper, a cyber-physical system-based smart control model for shopfloor material handling is designed. In contrast to the traditional vehicle control methods, AGVs and base stations at intersections can communicate and interact with each other and share the real-time information online. Then, the smart control model, which consists of car-following model, overtaking model, and collision warning and avoidance model, is designed and developed. The presented model is demonstrated by a set of simulations and an experiment, which proved that the overall task completion efficiency and the utilization of the road are improved.

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 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.