Hybrid Energy Modeling And Energy-aware Optimization Method In Machining Workshops

Energy conversation and emission reduction are the important developmental direction of science and technology in China’s manufacturing industry. Machining workshops are composed of computerized numerical control machines or manual machines for producing workpieces. Since machining is used for removing material of workpieces, it is wasteful in use of energy and quit inefficient. Machining workshops widely distributed in China have great potentials in energy savings and environmental abatement. With the new trend of global low-carbon,it is an important challenge to reduce energy consumption of machining workshops for China’s manufacturing industry. Currently, most research on energy consumption of machining workshops focuses on energy consumption of equipment, energy consumption of machining processes, etc. However, less research is conducted on energy modeling of machining workshops from system level, especially on energy-saving optimization method from the production operations. To this end, this thesis conducts the research on complex energy consumption characteristics of the machining workshop and energy-saving optimization method of task assignment.Energy consumption of machining workshops has three characteristics, multiple energy consumers, dynamically affected by the task variety of production processes and complex energy principles. Focusing on this, a framework for characterizing energy consumption of machining workshops is proposed. Taking into account the complexity of energy consumption in machining workshops, energy characteristics are described in terms of three layer of machining workshops including machine tool layer, task layer and auxiliary production layer. Furthermore, the energy consumption of machining workshops is modeled in the spatial and temporal dimensions, respectively.In machining workshops, production situation’s changes are often caused by the variance of machine tools or workpieces. Considering the multiple elements and dynamic changing production situation, a modeling method is proposed to predict the energy consumption in machining workshops using the Colored Timed Object-oriented Petri Net(CTOPN). In the method, hybrid energy characteristics are decomposed into Structure-related energy characteristics, State-related energy characteristics, Process-related energy characteristics and Assignment-related energy characteristics. The modeling method is proposed with CTOPN. The former two types of energy characteristics are modeled by constructing the structure of CTOPN and the latter two types of energy characteristic are modeled by applying colored tokens and associated attributes.The energy data of machine tool are required to support the analysis of energy characteristics in machining workshops. Focusing on this, this thesis proposes a practical method for estimating the energy consumption of numerical control(NC) machining. The correlation between NC codes and energy-consuming components of machine tools is analyzed. Each energy-consuming component is respectively estimated by considering its power characteristics and the parameters extracted form the NC codes, and then the procedure estimating energy consumption is developed by accounting from the total energy consumption of the components via the NC program.Facing on the complex energy characteristics of machining workshops, energy-saving optimization method of task assignment in machining workshops was studied from the production operation level. There is potentially a significant amount of energy savings that could be realized by selecting alternative machine tools and reducing the idle energy consumption through better scheduling. This thesis proposes an energy-saving optimization method that considers machine tool selection and operation sequence for machining workshops. A mathematical model is formulated using mixed integrated programming. A Nested Partition algorithm is utilized to solve the model. Further, focusing the energy characteristics affected by the stochastic events, this thesis proposes an energy-aware optimization method considering the new job arrival and machine tool breakdown. The Non-dominated Sorting Genetic Algorithm(NSGA-II) is applied to solve the optimization problem considering the stochastic events.