[雙語翻譯]數(shù)控加工外文翻譯--葉輪數(shù)控加工中加工時間與能耗的權衡分析

1、4500 英文單詞， 英文單詞，22500 英文字符，中文 英文字符，中文 6850 字文獻出處： 文獻出處：Arriaza O V, Kim D W, Lee D Y, et al. Trade-off analysis between machining time and energy consumption in impeller NC machining[J]. Robotics and Computer-Integrated

2、Manufacturing, 2017, 43: 164-170.Trade-off analysis between machining time and energy consumption in impeller NC machiningOscar Velásquez Arriaza, Dong-Won Kim，Dong Yoon Lee, Mohd. Azlan SuhaimiAbstractElectrical en

3、ergy is directly linked to society's prosperity across the globe; much of this due to the diverse innovations on manufacturing processes. Keeping pace with the high energy demand growth will require constant efforts

4、in terms of investment and research in order to bring new alternatives of usage. This paper outlines the application of multiple response optimization in order to analyze the trade-off be- tween machining time and energy

5、 consumption in 5-axis impeller rough machining to find a possible balance between them. It is well known that a higher speed reduces machining time but increases energy consumption, and vice versa. A quantitative form o

6、f the relationship between the involved factors was obtained by utilizing response surface methodology (RSM) together with the desirability function method. Four independent factors were selected, namely, spindle speed,

7、feed rate, depth and width of cut. The responses are the consumed energy and the machining time. The results showed that selecting an appropriate feed rate is crucial to balance the trade-offs between energy and time. Sp

8、indle speed is the major factor for energy consumption, while width of cut is the most influential factor for machining time.Keywords: Energy consumption ; Machining time ;Impeller1. IntroductionEnergy efficiency has inc

9、reasingly become a relevant issue in recent years due to the economic and environmental factors involved. Industries are aware of the importance of the energy usage because of costs savings or environmental regulations.

10、Machining is one of the major activities in manufacturing industries and it is responsible for a significant portion of the total consumed energy [1]. Performing machining processes with better energy efficiency will, th

11、erefore, significantly reduce the total industrial consumption of energy. But it is crucial to consider energy efficiency without sacrificing productivity. Energy savings up to 40% can be obtained based on the optimum ch

12、oice of cutting parameters, tools and optimum tool path design. A substantial improvement can be achieved just by adequately balancing the factors involved in the machining processes without the need of investment on new

13、 material or machinery [1]. It is also well recognized on previous researches that rough machining is an influential step for the process efficiency because it occupies most of the total machining time. The estimation of

14、 NC machining time is of importance because it provides manufacturing engineers with information to accurately predict the productivity of an NC machine [2]. But finding the characteristic behavior of the machining proce

15、ss and the factors involved is still a difficult task. Machine tools are complex and dynamic systems, there are no applicable theories to explain the behavior of these systems and, there is a diverse variety of materials

16、, machining parameters, cutting tools, machining tools that have a direct impact on the behavior analysis [3]. Significant work has been done on energy optimization; and much research has been done to approach this issue

17、 with rate, depth and width of cut. Thirdly, design of the experiments was used to arrange the experimentation setup. After performing the experiments and recollecting the data a preliminary regression model was created

18、by using the response surface methodology. This model was corrected by eliminating the unnecessary terms identified by doing an analysis of variance. After eliminating the unnecessary terms, the final regression models w

19、ere obtained for each response. The desirability function approach was applied to these models to transform the multiple response problem into a single response problem and finally find and optimal setting to optimize th

20、e objective responses. The results shown by the optimization process were validated by performing five extra experiments to test the reliability of the optimal setup to rough an impeller.The main objective for this study

21、 is to analyze a possible balance between production time and energy consumption concerning a 5-axis impeller rough machining process. This means that the optimization is performed considering different machining conditi

22、ons for the independent factors involved in the process.Fig. 1. Optimization steps for the balancing of trade-offs for roughing an impeller2.1. Response surface methodologyResponse surface methods (RSM) are often recomme

23、nded when it is necessary to examine the relationship between one or more response variables and a set of quantitative experimental variables or factors. RSM is useful when controllable factors are selected in order to i

24、dentify the optimal setting that will optimize the response. Two important models are commonly used in RSM. The first-degree model, as shown in Eq. (1)And the second degree model in Eq. (2),where y is the response under