Energy Consumption Prediction of a CNC Machining Process With Incomplete Data

Energy consumption prediction of a CNC machining process is important for energy efficiency optimization strategies.To improve the generalization abilities,more and more parameters are acquired for energy prediction modeling.While the data collected from workshops may be incomplete because of misoperation,unstable network connections,and frequent transfers,etc.This work proposes a framework for energy modeling based on incomplete data to address this issue.First,some necessary preliminary operations are used for incomplete data sets.Then,missing values are estimated to generate a new complete data set based on generative adversarial imputation nets(GAIN).Next,the gene expression programming(GEP)algorithm is utilized to train the energy model based on the generated data sets.Finally,we test the predictive accuracy of the obtained model.Computational experiments are designed to investigate the performance of the proposed framework with different rates of missing data.Experimental results demonstrate that even when the missing data rate increases to 30%,the proposed framework can still make efficient predictions,with the corresponding RMSE and MAE 0.903 k J and 0.739 k J,respectively.

Integrated Optimization of Structure and Control Parameters for the Height Control System of a Vertical Spindle Cotton Picker

Vertical picking method is a predominate method used to harvest cotton crop.However,a vertical picking method may cause spindle bending of the cotton picker if spindles collide with stones on the cotton field.Thus,how to realize a precise height control of the cotton picker is a crucial issue to be solved.The objective of this study is to design a height control system to avoid the collision.To design it,the mathematical models are established first.Then a multi-objective optimization model represented by structure parameters and control parameters is proposed to take the pressure of chamber without piston,response time and displacement error of the height control system as the opti-mization objectives.An integrated optimization approach that combines optimization via simulation,particle swarm optimization and simulated annealing is proposed to solve the model.Simulation and experimental test results show that the proposed integrated optimization approach can not only reduce the pressure of chamber without piston,but also decrease the response time and displacement error of the height control system.

LaBr_(3)(Ce)-CsI(Tl)紧凑式叠层反康伽马探测器研制

本工作研制了一套由圆柱形LaBr_(3)(Ce)主晶体和井形CsI(Tl)次晶体组成的叠层伽马探测器,该探测器主要用于鉴别来自LaBr_(3)(Ce)主晶体的康普顿散射光子并进行反符合测量。两种不同的晶体耦合到同一个光电倍增管上。入射辐射沉积在每一层探测器的能量,可以通过PMT给出的信号脉冲形状差异进行识别。利用Geant4模拟结果,验证了脉冲形状甄别性能和康普顿散射抑制效果。利用^(60)Co伽马源对探测器进行了实验测试,测试结果表明,该探测器本底抑制系数为2.33(1),达到了康普顿本底抑制两倍以上的预期目标。

Energy saving design of the machining unit of hobbing machine tool with integrated optimization

The machining unit of hobbing machine tool accounts for a large portion of the energy consumption during the operating phase.The optimization design is a practical means of energy saving and can reduce energy consumption essentially.However,this issue has rarely been discussed in depth in previous research.A comprehensive function of energy consumption of the machining unit is built to address this problem.Surrogate models are established by using effective fitting methods.An integrated optimization model for reducing tool displacement and energy consumption is developed on the basis of the energy consumption function and surrogate models,and the parameters of the motor and structure are considered simultaneously.Results show that the energy consumption and tool displacement of the machining unit are reduced,indicating that energy saving is achieved and the machining accuracy is guaranteed.The influence of optimization variables on the objectives is analyzed to inform the design.

Energy efficient cutting parameter optimization

Mechanical manufacturing industry consumes substantial energy with low energy efficiency. Increasing pressures from energy price and environmental directive force mechanical manufacturing industries to implement energy efficient technologies for reducing energy consumption and improving energy efficiency of their machining processes. In a practical machining process, cutting parameters are vital variables set by manufacturers in accordance with machining requirements of workpiece and machining condition. Proper selection of cutting parameters with energy consideration can effectively reduce energy consumption and improve energy efficiency of the machining process. Over the past 10 years, many researchers have been engaged in energy efficient cutting parameter optimization, and a large amount of literature have been published. This paper conducts a comprehensive literature review of current studies on energy efficient cutting parameter optimization to fully understand the recent advances in this research area. The energy consumption characteristics of machining process are analyzed by decomposing total energy consumption into electrical energy consumption of machine tool and embodied energy of cutting tool and cutting fluid. Current studies on energy efficient cutting parameter optimization by using experimental design method and energy models are reviewed in a comprehensive manner. Combined with the current status, future research directions of energy efficient cutting parameter optimization are presented.

A novel approach for remanufacturing process planning considering uncertain and fuzzy information

Remanufacturing,as one of the optimal disposals of end-of-life products,can bring tremendous economic and ecological benefits.Remanufacturing process planning is facing an immense challenge due to uncertainties and fuzziness of recoverable products in damage conditions and remanufacturing quality requirements.Although researchers have studied the influence of uncertainties on remanufacturing process planning,very few of them comprehensively studied the interactions among damage conditions and quality requirements that involve uncertain,fuzzy information.Hence,this challenge in the context of uncertain,fuzzy information is undertaken in this paper,and a method for remanufacturing process planning is presented to maximize remanufacturing efficiency and minimize cost.In particular,the characteristics of uncertainties and fuzziness involved in the remanufacturing processes are explicitly analyzed.An optimization model is then developed to minimize remanufacturing time and cost.The solution is provided through an improved Takagi-Sugeno fuzzy neural network(T-S FNN)method.The effectiveness of the proposed approach is exemplified and elucidated by a case study.Results show that the training speed and accuracy of the improved T-S FNN method are 23.5%and 82.5%higher on average than those of the original method,respectively.

ON CORE STRUCTURE PROPERTIES AND PEIERLS STRESS OF DISSOCIATED SUPERDISLOCATIONS IN ALUMINIDES:NiAl AND FeAl

The study of dislocation properties in B2 structure intermetallics NiAl and FeAl is crucial to understand their mechanical behaviors. In this paper, the core structure and Peierls stress of collinear dissociated （111）{110} edge superdislocations in NiAl and FeAl are investigated with the modified P-N dislocation equation. The generalized stacking fault energy curve along （111） direction in {110} slip plane contains two modification factors that can assure the antiphase energy and the unstable stacking fault energy to change independently. The results show that the core width of superpartials decreases with the increasing unstable stacking fault energy, and increases with the increasing antiphase boundary energy. The calculated Peierls stress of （111）{ 110） edge superdislocations in NiAl and FeAl are 475 MPa and 3042 MPa, respectively. The values of Peierls stress in NiAl is in accordance in magnitude with the experimental and the molecular statics simulations results.