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Energy Efficiency State Mechanism and Identification in Milling Processes
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ScienceDirect ScienceDirect
Procedia CIRP 00 (2018) 000–000
www.elsevier.com/locate/procedia
ProcediaProcedia CIRP 00CIRP (2017) 72000–000 (2018) 1487–1492 www.elsevier.com/locate/procedia
51st CIRP Conference on Manufacturing Systems
Energy Efficiency28th State andMay Identification in Milling Processes CIRPMechanism Design Conference, 2018, Nantes, France Yun Cai , Jianjian Yuan , Hua Shaoa*, and Shuheng Liaoa architecture of A new methodology to aanalyze the afunctional physical School of Mechanical Shanghai Jiao Tong University, Dongchuan Road 800,family Shanghai 200240, China existing products forEngineering, an assembly oriented product identification a
* Corresponding author. E-mail address: [email protected]
Paul Stief *, Jean-Yves Dantan, Alain Etienne, Ali Siadat
Abstract
École Nationale Supérieure d’Arts et Métiers, Arts et Métiers ParisTech, LCFC EA 4495, 4 Rue Augustin Fresnel, Metz 57078, France
*Energy Corresponding author. Tel.: +33 3 87 37 54 E-mailprocess address:plays [email protected] efficiency state identification of30; milling an important role in energy saving efforts for manufacturing systems. It is the
useful method and evaluation strategy for energy efficiency state identification which the energy efficiency mechanism has been revealed. In this paper, the time and frequency feature has been extracted based on the experiment. It is shown that the extracted feature of energy efficiency state can reflect the energy efficiency state mechanism and the energy efficiency state can be identified by this method. Abstract © 2018 The Authors. Published by Elsevier B.V. In today’s business environment, the trend towards more product variety and customization is unbroken. Due to this development, the need of Peer-review under responsibility of the scientific committee of the 51st CIRP Conference on Manufacturing Systems. agile and reconfigurable production systems emerged to cope with various products and product families. To design and optimize production systems as well as to choose the optimal product matches, product analysis methods are needed. Indeed, most of the known methods aim to Keywords: Energy efficiency state; The feature of energy efficiency; Milling process. analyze a product or one product family on the physical level. Different product families, however, may differ largely in terms of the number and nature of components. This fact impedes an efficient comparison and choice of appropriate product family combinations for the production system. A new methodology is proposed to analyze existing products in view of their functional and physical architecture. The aim is to cluster these products in new assembly oriented product families for the optimization of existing assembly lines andmain the creation of future reconfigurable 1. Introduction together. Furthermore, the challenges towards energy assembly systems. Based on Datum Flow Chain, the physical structure of the products is analyzed. Functional subassemblies are identified, and efficient manufacturing are discussed identifying the major a functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the Energy efficiency state identification of machining barriers from both technology and cultural point of view. similarity between product families by providing design support to both, production system planners and product designers. An illustrative processes plays an important role in energy saving efforts for Eberhard Abele[5], Gülsüm Mert[6] and Jingxiang Lv[7] example of a nail-clipper is used to explain the proposed methodology. An industrial case study on two product families of steering columns of manufacturing systems. Energy efficiency state is directly paidclose the influence of energy efficiency for thyssenkrupp Presta France is then carried out to give a first industrial evaluation of theattention proposed to approach. related to cutting conditions, tool wear and machine tool machine tool. Process models focused on the relationship of © 2017 The Authors. Published by Elsevier B.V. condition. It is the useful method and evaluation strategy for removal material rate and Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018. energy consumption where specific
energy efficiency state identification which the energy
Keywords: Design method; identification efficiencyAssembly; mechanism has beenFamily revealed.
In the past decades, many efforts have been made on the modelling of machining energy consumption with the objective of lower power consumption in machining 1. Introduction processes. The energy consumption model or energy efficiency model can be grouped into two main categories: Due models to the and fastprocess development in the models domainare of system models. System to communication and an ongoing trend of digitization evaluate energy consumption of machine tools basedand on digitalization, manufacturing enterprises are facing important energy modelling of subsystems. Philipp Eberspächer[1] challenges in today’s market environments: a continuing present an approach to combine power measurements, control tendency towards reduction of consumption product development times and signals and information with simulation models shortened product lifecycles. In addition, there is an increasing to provide the operators with highly detailed power demand of customization, time in over a global consumption data and being how at it the is same distributed the competition with competitors all over the world. This components of their machine tools. The study of [2] andtrend, [3] is which inducing theSalonitis[4] development from macro to micro similar.is Konstantinos presented an overview of markets, results in diminished lot sizes due to augmenting energy efficiency approaches, focusing on both production product varieties to low-volume production) [1]. and machine tool(high-volume level and how these two can be integrated To cope with this augmenting variety as well as to be able to identify possible optimization potentials in the existing 2212-8271 ©system, 2018 The it Authors. Publishedtobyhave Elsevier B.V. knowledge production is important a precise
energy consumption(SEC) and method of parameters optimization is commonly applied. The energy monitoring method of processing is proposed by Eberhard Abele[8] and Jingxiang Lv[9]. It is objective that the energy efficiency can be estimated or predicted. The relationship between specific of the product range and characteristics manufactured and/or energy consumption and cutting rate has been discussed and assembled in this system. In this context, the main challenge in the environment effect of machining has been present by modelling and analysis is now not only to cope with single Kara[10] and Dahmus[11]. Also a side milling tests were products, a limited product rangetool or existing product conducted on a milling machine to investigate thefamilies, specific but also to be able to analyze and to compare products to define energy consumption on a variety of materials by Mohammed new product and families. It can observed that existing Sarwar[12] Vincent A. be Balogun[13]. It classical can be estimated product families are regrouped in function of clients or features. the maximum consumed energy and minimum energy However, assembly oriented product families are hardly to find. demand. Sean Humphrey[14] provided a basic introduction On the product family level, products differ mainly two into power and energy measurement for three phase in power main characteristics: (i) the number of components and (ii) the and discussed the advantages of high-frequency power type of components (e.g. mechanical, electrical, electronical). monitoring. A. Vijayaraghavan[15] proposed event stream Classical methodologies mainlyconsumption single products processing techniques to considering reduce energy for or solitary, already existing product families analyzecould the manufacturing systems. Although these methods product structure on a physical level (components level) which causes difficulties regarding an efficient definition and comparison of different product families. Addressing this
Peer-review under responsibility of the scientific committee of the 51st CIRP Conference on Manufacturing Systems.
2212-8271©©2017 2018The The Authors. Published by Elsevier 2212-8271 Authors. Published by Elsevier B.V. B.V. Peer-review under responsibility of scientific the scientific committee theCIRP 51stDesign CIRP Conference Conference2018. on Manufacturing Systems. Peer-review under responsibility of the committee of the of 28th 10.1016/j.procir.2018.03.115
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Yun Cai et al. / Procedia CIRP 72 (2018) 1487–1492 Author name / Procedia CIRP 00 (2018) 000–000
improve and evaluate the energy efficiency of equipment theoretically, they can not obtain the characteristic of energy efficiency state and identify energy efficiency state in real time. Moreover, it is difficult to ensure the reliability in offline modelling. So, many scholars have realized that an effective solution is on-line energy efficiency condition monitoring and identification of machining processes. Signal processing technology can be applied in the energy efficiency state identification of milling processes. With proper signal processing methods, energy efficiency features can be extracted from multi-sensors signals (force, temperature, power, acoustic emission, vibration, etc.), and energy efficiency state can be identified subsequently. Poi Voon Er[16] used a minimal series of sensors mounted at key locations of a machine to measure and infer the actual vibration spectrum at a critical point where it was not suitable to mount a sensor for continuous machine condition monitoring. Thomas Behrendt[17] proposed a modified procedure for energy consumption characteristics of the machine tools based on standardized workpieces. Hu S.H[18] established an on-line energy efficiency monitoring system for machine tools. It is focused on production processing and the energy efficiency is analyzed by power signal. Iulian Marinescu[19] proposed time–frequency analysis of acoustic emission signals to supervised cutting processing with multiple teeth cutting and detected damaged cutting state. Similarly M.S.H. Bhuiyann[20] identified chip state and Iulian Marinescu[21] detected tool and workpiece feature also based on acoustic emission. Mehdi Nouri[22] proposed the tracking force model to monitored tool wear condition in milling processing. Actually, various signal processing methods have already been applied in cutting tool condition monitoring, machine tool fault diagnosis and work-piece quality feature extraction successfully. In addition, the existing research is mainly focused on energy consumption or power condition monitoring in machining processes. However, the energy efficiency is a more complex indicator than energy consumption and power, which is usually coupled with a lot of factors like machine tool states, tool conditions, and cutting conditions. As a result, it is very difficult to identify energy efficiency state in machining processes simply by single or traditional signal processing strategies. In this paper, the model of energy efficiency state is proposed and the energy efficiency state mechanism is revealed based on conservation of energy. The relationship between signals and feature of energy efficiency state is proposed and the other state(machine-tool state and tool state) has been also separated and the time and frequency feature has been extracted based on the experiment. It is show that the extracted feature of energy efficiency state can reflect the energy efficiency state mechanism and the energy efficiency state can be identified by this method. The paper is organized as follows: in section 2, the mechanism of energy efficiency state is revealed and the relationship between signals and energy efficiency feature is proposed. In section 3, the experiment of detached state is designed and extracted the feature of energy efficiency state. Finally, in section 4, the conclusions are draw.
2. Mechanism The energy efficiency is continuously changing and it can be seen a kind of state to evaluate the energy efficiency for milling process. So extracting the feature of energy efficiency state is the purpose of evaluation. The energy efficiency of milling process can be expressed by the useful cutting energy and the total energy. Sum of the total energy is the useful cutting energy and the waste of energy. The useful cutting energy is the energy of removal material and the total energy is the input electric energy. The relationship between them is direct proportion. The energy of removal material reflects in cutting area and it is the time domain that from cutting tool touches the material to leave the material. The waste of energy contains two parts which are the waste of energy for machine-tool and the waste of energy for cutting area. The style of waste of energy is the thermal energy, audio energy, vibration energy, etc. The style of the thermal energy is the thermal energy of parts (spindle, gear, bearing, etc.) for machine tool and the specific thermal energy of cutting. And the audio or vibration energy also contains machine-tool and cutting area. The modelling of energy efficiency state can be proposed by mechanism analysis. Nomenclature
W Q E
energy efficiency the useful cutting energy the total energy the waste of energy cutting force
s vc t E1 E2 A1 qi k
the cutting length cutting speed cutting time the waste of energy for machine-tool
Fcutting
S
T
A2 qshear
the waste of energy for milling process power of machine-tool audio the thermal energy of parts the constant of thermal the thermal area of parts the temperature difference power of cutting audio
q friction
the shear energy the friction energy
Fshear
shear force
s ap
shear speed depth of cut
Z P
the volume of removal material the input power
The energy consumption of milling processes can be shown in figure 1. The AC is electric power, it is input power, which transformed to mechanical energy and thermal energy. The mechanical energy contains many types of energy. There is removal material, vibration of cutting processes and
Yun Cai et al. / Procedia CIRP 72 (2018) 1487–1492 Author name / Procedia CIRP 00 (2018) 000–000
vibration of machine tool, the intensity of audio for cutting processes and machine tool. The thermal energy contains thermal radiation of machine tool and cutting processes. The thermal energy of machine tool is the collection of all parts of it, such as axis, bearing, gear, motor, belt, sleeve and so on. Sum of the thermal energy of these parts is the thermal energy of machine tool. The thermal energy of cutting processes contains the risen temperature of workpiece, cutting tool and chip. However, the relationship between energy consumption of removal material and input energy is focused for energy efficiency state identification. The other types energy can not be ignored in this process. Next, the modelling of energy efficiency state is analysed.
1489 3
So,
t
Fcutting s Fcutting vc Pt P
n
k S T q
t 1
i
i
i
shear
(10)
q friction A1 A2
(11) Pt The feature of energy efficiency state can be extracted by Fcutting ,P or T , A,P . i 1
The form of signals is analysed and the quality of the feature should be solved in the next for feature extraction. It can be seen that the energy efficiency state is a coupled and time-changed state which consist of multi-signals. It is a linear system and conform to the relationship between input energy and output energy. The feature of energy efficiency state conforms to product in time and convolution in frequency. The model of feature in time is, (12) W t t Q t 1 E t t Q t
(13)
The model of feature in frequency is, W t Q t t
(14)
1 E t Q t t
(15)
or, W t
t
Q t d 1 E t Q t d 0
t
0
(1)
E (2) Q The feature of energy efficiency state is coupled by the signals of the energy of removal material, the waste of energy and the total energy. The energy of removal material can be expressed by the cutting force and the cutting length or cutting speed, W Fcutting s (3)
1
W Fcutting vc t
(17)
The feature of energy efficiency state can be extracted by multi-signals, the feature quality is, Time Feature SignalFcutting ,SignalP ,SignalT ,Signal A Frequency Feature
Figure 1 The mechanism of energy efficiency state for milling process (1. Machine tool, 2. Cutting tool, 3. Workpiece)
The energy efficiency can be expressed, W Q
(16)
(4)
The waste of energy (the thermal energy and audio or vibration energy) can be expressed,
The energy efficiency state always contains other states, such as machine-tool state or cutting tool state, as it is a complex and couple process for milling. Therefore, the feature of cutting process should be separated before energy efficiency feature extraction and the other feature should be separated by the signals of force, power, audio or vibration. There are two main parts feature, the error feature and cutting tool wear feature, for milling process which can be separated by designed experiment. 3. Experiment
(5)
The feature of energy efficiency state can be extracted by the designed experiment of state separated. The experimental details are shown in table 1.
E A1 ki Si Ti 1
(6)
Table 1. Experimental details.
E2 A2 qshear q friction
(7)
n
E A1 qi 1 i 1
n
i 1
Fshear s a p E2 A2 ktool Stool Ttool kchip Schip Tchip Z The input electric energy is, Q Pt
(8 ) (9)
Name
Value
Work-piece
Material, Steel 45. Size, 90×50×90mm3
Cutter
SEMX 1204 AFTN-ME12 F40M. Number of teeth, 1. Diameter, 50mm
Processing
Plane milling and no cutting fluid
Machine
X5030A and rated power 8KW
Yun Cai et al. / Procedia CIRP 72 (2018) 1487–1492 Author name / Procedia CIRP 00 (2018) 000–000
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Sensors
Force-sensor, power sensor, a-sensor, ae-sensor and thermocouple
Rotational speed
150/550/760/1500r/min
Feed rate
12/78/270mm/min
Depth of cut
0.5/1/1.5mm
Width of cut
50mm
Offset distance
0
The cutting system contains machine-tool, cutting tool, parameters and workplace. The experiment machine-tool is modified based on an old machine-tool. The spindle of machine-tool has been changed especially bearing of the output axis which aimed to the error feature of bearing can separate. The tooth of cutting tool is new and cut in only one tooth. The differences energy efficiency data is test by a large number of experiment and the cutting parameters are summarized. The sensitive parameters for energy efficiency are extracted and test again. The workpiece is C45E4. The sensors are power sensor, force sensor, audio sensor, acoustic emission sensor and temperature sensor. Figure 2 shows the experiment system.
a) The feature of high energy efficiency state
b) The feature of low energy efficiency state
c) The feature of low energy efficiency state coupled with machine-tool state Figure 3 The feature of energy efficiency state
Figure 2 The experiment system of energy efficiency state identification for milling process
The difference feature of energy efficiency state is shown in figure 3a), 3b) and 3c).
There are obviously different amplitude in time and frequency between high energy efficiency state and low energy efficiency state as shown in figure 3. The upper parts of figure 3 are energy efficiency data ( Fi / Pi ) and the frequency result, and the second parts are moving average data and its frequency result. The energy distribution of low energy efficiency state is more than high energy efficiency state in other frequency area. Comparing 3b) and 3c), there is other state feature in 3c), it is the part frequency of main spindle. It has been extracted the error feature by compared experiment of new and old machine-tool for bearing. So the bearing of spindle has been modified in order to feature extraction of waste of energy. Two thermocouples put under the new bearing and old bearing of spindle for bearing temperature monitoring. Eight thermocouples put into workpiece for milling temperature monitoring. For short and obviously, the result of low energy efficiency state is shown in figure 4. The bearing 1 is new and bearing 2 is old in figure 4. The location of thermocouple 1 to 8 is put along with the workpiece length direction. The workpiece is cut start from thermocouple 1 by cutting tool and end of 8. The risen temperature of thermocouple for all the cutting conditions has been analyzed in table 2. It is shown that, the risen temperature of old bearing is higher than new bearing,
Yun Cai et al. / Procedia CIRP 72 (2018) 1487–1492 Author name / Procedia CIRP 00 (2018) 000–000
cutting depth and rotation speed of spindle is the important factor for cutting temperature or cutting energy, the energy consumption associated with risen temperature. It is shown that the risen temperature of bearing 2 is higher than bearing 1. It means the waste of energy for thermal of old bearing is more than new bearing. The cutting thermal is changed obviously in milling process and the waste of energy is increased. It reflects energy efficiency state mechanism in milling process. So the convolution of energy efficiency state can be calculated and the feature of energy efficiency state also can be extracted. The result is shown in figure 5.
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energy efficiency model, the model of signal feature in time and the convolution in frequency) no matter how different parameter, different machine tool or different cutting tool. So the result of high energy efficiency state of these experiments is shown in figure 6.
Figure 5 The convolution of energy efficiency state. The horizontal-axis is time (s) and vertical is amplitude. The data is cutting parameters. (rotation speed of spindle/feed rate/cutting depth)
Figure 4 The relationship between temperature and time of low energy efficiency state (rotation speed of spindle: 150r/min, feed rate: 12mm/min, cutting depth: 0.5mm)
The total result of experiments is shown in figure 6. Three types of energy efficiency state have been separated by statistic analysis of data. It can be seen that not only the energy efficiency state under certain cutting condition can be judged, but also the energy efficiency state at any time can be evaluated. So the energy efficiency state for milling process can be extracted in this method.
Table 2. Risen temperature of thermocouples (RS is rotation speed r/min, F is feed rate mm/min, MAX is the maxim risen temperature, MIN is the mini risen temperature) No.
RS
F
ap
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
150 150 150 150 150 550 550 550 550 550 550 550 550 760 760 760 760 760 760 760 760 1500 1500 1500 1500
12 12 12 78 78 12 12 12 78 78 78 270 270 12 12 12 78 78 270 78 270 12 78 78 270
1 0.5 1.5 1 1.5 1 0.5 1.5 1 0.5 1.5 1 0.5 1 0.5 1.5 1 0.5 1 1.5 0.5 1 1 1.5 1
TB1 0.57 0.49 0.33 0.15 0.11 2.03 3.02 1.82 0.61 2.08 0.97 0.12 2.13 1.95 4.11 2.21 1.04 1.75 0.92 1.33 1.47 0.70 1.59 0.92 1.82
TB2 0.62 0.57 0.35 0.17 0.14 2.09 3.17 2.95 0.73 2.13 1.10 0.15 2.27 2.00 5.19 2.23 1.54 2.46 1.10 1.72 1.51 1.22 1.75 0.96 2.07
MAX 11.28 6.60 17.43 9.74 11.19 17.59 7.09 24.25 11.16 6.26 18.03 7.53 2.62 19.22 11.59 24.88 11.80 10.09 7.16 23.12 8.55 33.30 21.95 36.07 11.19
MIN 3.44 2.45 7.30 4.28 4.75 4.81 4.67 10.11 4.45 2.67 11.71 5.30 5.50 12.08 5.45 10.60 5.81 2.53 5.32 14.99 2.25 13.31 12.69 20.06 8.82
The energy efficiency state of milling processes can be easily identified by convolution as shown in figure 5. So the result can be obtained that the energy efficiency state can be identified by energy efficiency state mechanism (combining
Figure 6 The result of high energy efficiency state identification
4. Conclusion Method of energy efficiency state identification for milling processes based on mechanism analysis in this paper. It was found that, (1) The model of energy efficiency state is proposed and the energy efficiency state mechanism is revealed based on conservation of energy. (2) The form of signals is analysed and the quality of the energy efficiency feature is proposed which is product in time and convolution in frequency. (3) The relationship between signals and feature of energy efficiency state is proposed and the other state (machine-tool state and tool state) has been also separated and the time and frequency feature has been extracted based on the experiment.
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Yun Cai et al. / Procedia CIRP 72 (2018) 1487–1492 Author name / Procedia CIRP 00 (2018) 000–000
In conclusion, the proposed method in this paper will play an important role in energy saving efforts and be beneficial for manufacturing systems evaluation. Acknowledgements The authors truly appreciate the support by the National Natural Science Foundation of China (Project grant numbers: 51575357). References [1] Philipp Eberspächer, Philipp Schraml, Jan Schlechtendahl, Alexander Verl, Eberhard Abele, A model and signal based power consumption monitoring concept for energetic optimization of machine tools, Procedia CIRP 15 (2014) 44-49. [2] R.S. Pawade, Harshad A. Sonawane, Suhas S. Joshi, An analytical model to predict specific shear energy in high-speed turning of Inconel 718, International Journal of Machine Tools & Manufacture 49 (2009) 979990. [3] A. Vijayaraghavan, D. Dornfeld, Automated energy monitoring of machine tools, CIRP Annals - Manufacturing Technology 59 (2010) 2124. [4] Konstantinos Salonitis, Peter Ball, Energy efficient manufacturing from machine tools to manufacturing, Procedia CIRP 7 (2013) 634-639. [5] Eberhard Abele, Steffen Braun, Philipp Schraml, Holistic Simulation Environment for Energy Consumption Prediction of Machine Tools, Procedia CIRP 29 (2015) 251-256. [6] Gülsüm Mert, Sebastian Waltemode, Jan C. Aurich, How services influence the energy efficiency of machine tools: A case study of a machine tool manufacturer, Procedia CIRP 29 (2015) 287-292. [7] Jingxiang Lv, Renzhong Tang, Shun Jia, Therblig-based energy supply modeling of computer numerical control machine tools, Journal of Cleaner Production 65 (2014) 168-177. [8] Eberhard Abele, Niklas Panten, Benjamin Menz, Data Collection for Energy Monitoring Purposes and Energy Control of Production Machines, Procedia CIRP 29 (2015) 299-304. [9] Jingxiang Lv, Renzhong Tang, Shun Jia, Ying Liu, Experimental study on energy consumption of computer numerical control machine tools, Journal of Cleaner Production 112 (2016) 3864-3874. [10] Jeffrey B. Dahmus and Timothy G. Gutowski, An Environment Analysis of Machining, ASME International Mechanical Engineering Congress and RD&D Expo (2004) 1-10.
[11] S. Kara and W. Li, Unit process energy consumption models for removal material processes, CIRP Annals - Manufacturing Technology 60 (2011) 37–40. [12] Mohammed Sarwar, Martin Persson, Hakan Hellbergh, Julfikar Haider, Measurement of specific cutting energy for evaluating the efficiency of band sawing different workpiece materials, International Journal of Machine Tools & Manufacture 49 (2009) 958-965. [13] Vincent A. Balogun, Isuamfon F. Edem, Adefemi A. Adekunle, Paul T. Mativeng, Specific energy based evaluation of machining efficiency, Journal of Cleaner Production 116 (2016) 187-197. [14] Sean Humphrey, Helen Papadopoulos, Barbara Linke, Sindhu Maiyya, Athulan Vijayaraghavan, Robert Schmitt, Power measurement for sustainable high-performance manufacturing processes, Procedia CIRP 14 (2014) 466-471. [15] A. Vijayaraghavan, D. Dornfeld, Automated energy monitoring of machine tools, CIRP Annals Manufacturing Technology, 59 (2010) 2124. [16] Poi Voon Er, Chek Sing Teo, Kok Kiong Tan, Approach towards sensor placement, selection and fusion for real-time condition monitoring of precision machines, Mechanical Systems and Signal Processing 68-69 (2016) 105-124. [17] Thomas Behrendt, Andre´ Zein, Sangkee Min, Development of an energy consumption monitoring procedure for machine tools, CIRP Annals Manufacturing Technology, 61 (2012) 43-46. [18] Hu S.H., Liu, F., He Y., Hu T, An on-line approach for energy efficiency monitoring of machine tools, Journal of Cleaner Production, 27(2012) 133-140. [19] Iulian Marinescu, Dragos Axinte, A time–frequency acoustic emissionbased monitoring technique to identify workpiece surface malfunctions in milling with multiple teeth cutting simultaneously, International Journal of Machine Tools & Manufacture, 49 (2009) 53-65. [20] M.S.H. Bhuiyann, I.A.Choudhury, Y.Nukman, An innovative approach to monitor the chip formation effect on tool state using acoustic emission in turning, International Journal of Machine Tools & Manufacture, 58 (2012) 19-28. [21] Iulian Marinescu, Dragos A. Axinte, A critical analysis of effectiveness of acoustic emission signals to detect tool and workpiece malfunctions in milling operations, International Journal of Machine Tools & Manufacture 48 (2008) 1148-1160. [22] Mehdi Nouri, BarryK.Fussell, BethL.Ziniti, ErnstLinder, Real-time tool wear monitoring in milling using a cutting condition independent method, International Journal of Machine Tools & Manufacture, 89 (2015) 1-13.
ScienceDirect
Available online atonline www.sciencedirect.com Available at www.sciencedirect.com
ScienceDirect ScienceDirect
Procedia CIRP 00 (2018) 000–000
www.elsevier.com/locate/procedia
ProcediaProcedia CIRP 00CIRP (2017) 72000–000 (2018) 1487–1492 www.elsevier.com/locate/procedia
51st CIRP Conference on Manufacturing Systems
Energy Efficiency28th State andMay Identification in Milling Processes CIRPMechanism Design Conference, 2018, Nantes, France Yun Cai , Jianjian Yuan , Hua Shaoa*, and Shuheng Liaoa architecture of A new methodology to aanalyze the afunctional physical School of Mechanical Shanghai Jiao Tong University, Dongchuan Road 800,family Shanghai 200240, China existing products forEngineering, an assembly oriented product identification a
* Corresponding author. E-mail address: [email protected]
Paul Stief *, Jean-Yves Dantan, Alain Etienne, Ali Siadat
Abstract
École Nationale Supérieure d’Arts et Métiers, Arts et Métiers ParisTech, LCFC EA 4495, 4 Rue Augustin Fresnel, Metz 57078, France
*Energy Corresponding author. Tel.: +33 3 87 37 54 E-mailprocess address:plays [email protected] efficiency state identification of30; milling an important role in energy saving efforts for manufacturing systems. It is the
useful method and evaluation strategy for energy efficiency state identification which the energy efficiency mechanism has been revealed. In this paper, the time and frequency feature has been extracted based on the experiment. It is shown that the extracted feature of energy efficiency state can reflect the energy efficiency state mechanism and the energy efficiency state can be identified by this method. Abstract © 2018 The Authors. Published by Elsevier B.V. In today’s business environment, the trend towards more product variety and customization is unbroken. Due to this development, the need of Peer-review under responsibility of the scientific committee of the 51st CIRP Conference on Manufacturing Systems. agile and reconfigurable production systems emerged to cope with various products and product families. To design and optimize production systems as well as to choose the optimal product matches, product analysis methods are needed. Indeed, most of the known methods aim to Keywords: Energy efficiency state; The feature of energy efficiency; Milling process. analyze a product or one product family on the physical level. Different product families, however, may differ largely in terms of the number and nature of components. This fact impedes an efficient comparison and choice of appropriate product family combinations for the production system. A new methodology is proposed to analyze existing products in view of their functional and physical architecture. The aim is to cluster these products in new assembly oriented product families for the optimization of existing assembly lines andmain the creation of future reconfigurable 1. Introduction together. Furthermore, the challenges towards energy assembly systems. Based on Datum Flow Chain, the physical structure of the products is analyzed. Functional subassemblies are identified, and efficient manufacturing are discussed identifying the major a functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the Energy efficiency state identification of machining barriers from both technology and cultural point of view. similarity between product families by providing design support to both, production system planners and product designers. An illustrative processes plays an important role in energy saving efforts for Eberhard Abele[5], Gülsüm Mert[6] and Jingxiang Lv[7] example of a nail-clipper is used to explain the proposed methodology. An industrial case study on two product families of steering columns of manufacturing systems. Energy efficiency state is directly paidclose the influence of energy efficiency for thyssenkrupp Presta France is then carried out to give a first industrial evaluation of theattention proposed to approach. related to cutting conditions, tool wear and machine tool machine tool. Process models focused on the relationship of © 2017 The Authors. Published by Elsevier B.V. condition. It is the useful method and evaluation strategy for removal material rate and Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018. energy consumption where specific
energy efficiency state identification which the energy
Keywords: Design method; identification efficiencyAssembly; mechanism has beenFamily revealed.
In the past decades, many efforts have been made on the modelling of machining energy consumption with the objective of lower power consumption in machining 1. Introduction processes. The energy consumption model or energy efficiency model can be grouped into two main categories: Due models to the and fastprocess development in the models domainare of system models. System to communication and an ongoing trend of digitization evaluate energy consumption of machine tools basedand on digitalization, manufacturing enterprises are facing important energy modelling of subsystems. Philipp Eberspächer[1] challenges in today’s market environments: a continuing present an approach to combine power measurements, control tendency towards reduction of consumption product development times and signals and information with simulation models shortened product lifecycles. In addition, there is an increasing to provide the operators with highly detailed power demand of customization, time in over a global consumption data and being how at it the is same distributed the competition with competitors all over the world. This components of their machine tools. The study of [2] andtrend, [3] is which inducing theSalonitis[4] development from macro to micro similar.is Konstantinos presented an overview of markets, results in diminished lot sizes due to augmenting energy efficiency approaches, focusing on both production product varieties to low-volume production) [1]. and machine tool(high-volume level and how these two can be integrated To cope with this augmenting variety as well as to be able to identify possible optimization potentials in the existing 2212-8271 ©system, 2018 The it Authors. Publishedtobyhave Elsevier B.V. knowledge production is important a precise
energy consumption(SEC) and method of parameters optimization is commonly applied. The energy monitoring method of processing is proposed by Eberhard Abele[8] and Jingxiang Lv[9]. It is objective that the energy efficiency can be estimated or predicted. The relationship between specific of the product range and characteristics manufactured and/or energy consumption and cutting rate has been discussed and assembled in this system. In this context, the main challenge in the environment effect of machining has been present by modelling and analysis is now not only to cope with single Kara[10] and Dahmus[11]. Also a side milling tests were products, a limited product rangetool or existing product conducted on a milling machine to investigate thefamilies, specific but also to be able to analyze and to compare products to define energy consumption on a variety of materials by Mohammed new product and families. It can observed that existing Sarwar[12] Vincent A. be Balogun[13]. It classical can be estimated product families are regrouped in function of clients or features. the maximum consumed energy and minimum energy However, assembly oriented product families are hardly to find. demand. Sean Humphrey[14] provided a basic introduction On the product family level, products differ mainly two into power and energy measurement for three phase in power main characteristics: (i) the number of components and (ii) the and discussed the advantages of high-frequency power type of components (e.g. mechanical, electrical, electronical). monitoring. A. Vijayaraghavan[15] proposed event stream Classical methodologies mainlyconsumption single products processing techniques to considering reduce energy for or solitary, already existing product families analyzecould the manufacturing systems. Although these methods product structure on a physical level (components level) which causes difficulties regarding an efficient definition and comparison of different product families. Addressing this
Peer-review under responsibility of the scientific committee of the 51st CIRP Conference on Manufacturing Systems.
2212-8271©©2017 2018The The Authors. Published by Elsevier 2212-8271 Authors. Published by Elsevier B.V. B.V. Peer-review under responsibility of scientific the scientific committee theCIRP 51stDesign CIRP Conference Conference2018. on Manufacturing Systems. Peer-review under responsibility of the committee of the of 28th 10.1016/j.procir.2018.03.115
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improve and evaluate the energy efficiency of equipment theoretically, they can not obtain the characteristic of energy efficiency state and identify energy efficiency state in real time. Moreover, it is difficult to ensure the reliability in offline modelling. So, many scholars have realized that an effective solution is on-line energy efficiency condition monitoring and identification of machining processes. Signal processing technology can be applied in the energy efficiency state identification of milling processes. With proper signal processing methods, energy efficiency features can be extracted from multi-sensors signals (force, temperature, power, acoustic emission, vibration, etc.), and energy efficiency state can be identified subsequently. Poi Voon Er[16] used a minimal series of sensors mounted at key locations of a machine to measure and infer the actual vibration spectrum at a critical point where it was not suitable to mount a sensor for continuous machine condition monitoring. Thomas Behrendt[17] proposed a modified procedure for energy consumption characteristics of the machine tools based on standardized workpieces. Hu S.H[18] established an on-line energy efficiency monitoring system for machine tools. It is focused on production processing and the energy efficiency is analyzed by power signal. Iulian Marinescu[19] proposed time–frequency analysis of acoustic emission signals to supervised cutting processing with multiple teeth cutting and detected damaged cutting state. Similarly M.S.H. Bhuiyann[20] identified chip state and Iulian Marinescu[21] detected tool and workpiece feature also based on acoustic emission. Mehdi Nouri[22] proposed the tracking force model to monitored tool wear condition in milling processing. Actually, various signal processing methods have already been applied in cutting tool condition monitoring, machine tool fault diagnosis and work-piece quality feature extraction successfully. In addition, the existing research is mainly focused on energy consumption or power condition monitoring in machining processes. However, the energy efficiency is a more complex indicator than energy consumption and power, which is usually coupled with a lot of factors like machine tool states, tool conditions, and cutting conditions. As a result, it is very difficult to identify energy efficiency state in machining processes simply by single or traditional signal processing strategies. In this paper, the model of energy efficiency state is proposed and the energy efficiency state mechanism is revealed based on conservation of energy. The relationship between signals and feature of energy efficiency state is proposed and the other state(machine-tool state and tool state) has been also separated and the time and frequency feature has been extracted based on the experiment. It is show that the extracted feature of energy efficiency state can reflect the energy efficiency state mechanism and the energy efficiency state can be identified by this method. The paper is organized as follows: in section 2, the mechanism of energy efficiency state is revealed and the relationship between signals and energy efficiency feature is proposed. In section 3, the experiment of detached state is designed and extracted the feature of energy efficiency state. Finally, in section 4, the conclusions are draw.
2. Mechanism The energy efficiency is continuously changing and it can be seen a kind of state to evaluate the energy efficiency for milling process. So extracting the feature of energy efficiency state is the purpose of evaluation. The energy efficiency of milling process can be expressed by the useful cutting energy and the total energy. Sum of the total energy is the useful cutting energy and the waste of energy. The useful cutting energy is the energy of removal material and the total energy is the input electric energy. The relationship between them is direct proportion. The energy of removal material reflects in cutting area and it is the time domain that from cutting tool touches the material to leave the material. The waste of energy contains two parts which are the waste of energy for machine-tool and the waste of energy for cutting area. The style of waste of energy is the thermal energy, audio energy, vibration energy, etc. The style of the thermal energy is the thermal energy of parts (spindle, gear, bearing, etc.) for machine tool and the specific thermal energy of cutting. And the audio or vibration energy also contains machine-tool and cutting area. The modelling of energy efficiency state can be proposed by mechanism analysis. Nomenclature
W Q E
energy efficiency the useful cutting energy the total energy the waste of energy cutting force
s vc t E1 E2 A1 qi k
the cutting length cutting speed cutting time the waste of energy for machine-tool
Fcutting
S
T
A2 qshear
the waste of energy for milling process power of machine-tool audio the thermal energy of parts the constant of thermal the thermal area of parts the temperature difference power of cutting audio
q friction
the shear energy the friction energy
Fshear
shear force
s ap
shear speed depth of cut
Z P
the volume of removal material the input power
The energy consumption of milling processes can be shown in figure 1. The AC is electric power, it is input power, which transformed to mechanical energy and thermal energy. The mechanical energy contains many types of energy. There is removal material, vibration of cutting processes and
Yun Cai et al. / Procedia CIRP 72 (2018) 1487–1492 Author name / Procedia CIRP 00 (2018) 000–000
vibration of machine tool, the intensity of audio for cutting processes and machine tool. The thermal energy contains thermal radiation of machine tool and cutting processes. The thermal energy of machine tool is the collection of all parts of it, such as axis, bearing, gear, motor, belt, sleeve and so on. Sum of the thermal energy of these parts is the thermal energy of machine tool. The thermal energy of cutting processes contains the risen temperature of workpiece, cutting tool and chip. However, the relationship between energy consumption of removal material and input energy is focused for energy efficiency state identification. The other types energy can not be ignored in this process. Next, the modelling of energy efficiency state is analysed.
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So,
t
Fcutting s Fcutting vc Pt P
n
k S T q
t 1
i
i
i
shear
(10)
q friction A1 A2
(11) Pt The feature of energy efficiency state can be extracted by Fcutting ,P or T , A,P . i 1
The form of signals is analysed and the quality of the feature should be solved in the next for feature extraction. It can be seen that the energy efficiency state is a coupled and time-changed state which consist of multi-signals. It is a linear system and conform to the relationship between input energy and output energy. The feature of energy efficiency state conforms to product in time and convolution in frequency. The model of feature in time is, (12) W t t Q t 1 E t t Q t
(13)
The model of feature in frequency is, W t Q t t
(14)
1 E t Q t t
(15)
or, W t
t
Q t d 1 E t Q t d 0
t
0
(1)
E (2) Q The feature of energy efficiency state is coupled by the signals of the energy of removal material, the waste of energy and the total energy. The energy of removal material can be expressed by the cutting force and the cutting length or cutting speed, W Fcutting s (3)
1
W Fcutting vc t
(17)
The feature of energy efficiency state can be extracted by multi-signals, the feature quality is, Time Feature SignalFcutting ,SignalP ,SignalT ,Signal A Frequency Feature
Figure 1 The mechanism of energy efficiency state for milling process (1. Machine tool, 2. Cutting tool, 3. Workpiece)
The energy efficiency can be expressed, W Q
(16)
(4)
The waste of energy (the thermal energy and audio or vibration energy) can be expressed,
The energy efficiency state always contains other states, such as machine-tool state or cutting tool state, as it is a complex and couple process for milling. Therefore, the feature of cutting process should be separated before energy efficiency feature extraction and the other feature should be separated by the signals of force, power, audio or vibration. There are two main parts feature, the error feature and cutting tool wear feature, for milling process which can be separated by designed experiment. 3. Experiment
(5)
The feature of energy efficiency state can be extracted by the designed experiment of state separated. The experimental details are shown in table 1.
E A1 ki Si Ti 1
(6)
Table 1. Experimental details.
E2 A2 qshear q friction
(7)
n
E A1 qi 1 i 1
n
i 1
Fshear s a p E2 A2 ktool Stool Ttool kchip Schip Tchip Z The input electric energy is, Q Pt
(8 ) (9)
Name
Value
Work-piece
Material, Steel 45. Size, 90×50×90mm3
Cutter
SEMX 1204 AFTN-ME12 F40M. Number of teeth, 1. Diameter, 50mm
Processing
Plane milling and no cutting fluid
Machine
X5030A and rated power 8KW
Yun Cai et al. / Procedia CIRP 72 (2018) 1487–1492 Author name / Procedia CIRP 00 (2018) 000–000
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Sensors
Force-sensor, power sensor, a-sensor, ae-sensor and thermocouple
Rotational speed
150/550/760/1500r/min
Feed rate
12/78/270mm/min
Depth of cut
0.5/1/1.5mm
Width of cut
50mm
Offset distance
0
The cutting system contains machine-tool, cutting tool, parameters and workplace. The experiment machine-tool is modified based on an old machine-tool. The spindle of machine-tool has been changed especially bearing of the output axis which aimed to the error feature of bearing can separate. The tooth of cutting tool is new and cut in only one tooth. The differences energy efficiency data is test by a large number of experiment and the cutting parameters are summarized. The sensitive parameters for energy efficiency are extracted and test again. The workpiece is C45E4. The sensors are power sensor, force sensor, audio sensor, acoustic emission sensor and temperature sensor. Figure 2 shows the experiment system.
a) The feature of high energy efficiency state
b) The feature of low energy efficiency state
c) The feature of low energy efficiency state coupled with machine-tool state Figure 3 The feature of energy efficiency state
Figure 2 The experiment system of energy efficiency state identification for milling process
The difference feature of energy efficiency state is shown in figure 3a), 3b) and 3c).
There are obviously different amplitude in time and frequency between high energy efficiency state and low energy efficiency state as shown in figure 3. The upper parts of figure 3 are energy efficiency data ( Fi / Pi ) and the frequency result, and the second parts are moving average data and its frequency result. The energy distribution of low energy efficiency state is more than high energy efficiency state in other frequency area. Comparing 3b) and 3c), there is other state feature in 3c), it is the part frequency of main spindle. It has been extracted the error feature by compared experiment of new and old machine-tool for bearing. So the bearing of spindle has been modified in order to feature extraction of waste of energy. Two thermocouples put under the new bearing and old bearing of spindle for bearing temperature monitoring. Eight thermocouples put into workpiece for milling temperature monitoring. For short and obviously, the result of low energy efficiency state is shown in figure 4. The bearing 1 is new and bearing 2 is old in figure 4. The location of thermocouple 1 to 8 is put along with the workpiece length direction. The workpiece is cut start from thermocouple 1 by cutting tool and end of 8. The risen temperature of thermocouple for all the cutting conditions has been analyzed in table 2. It is shown that, the risen temperature of old bearing is higher than new bearing,
Yun Cai et al. / Procedia CIRP 72 (2018) 1487–1492 Author name / Procedia CIRP 00 (2018) 000–000
cutting depth and rotation speed of spindle is the important factor for cutting temperature or cutting energy, the energy consumption associated with risen temperature. It is shown that the risen temperature of bearing 2 is higher than bearing 1. It means the waste of energy for thermal of old bearing is more than new bearing. The cutting thermal is changed obviously in milling process and the waste of energy is increased. It reflects energy efficiency state mechanism in milling process. So the convolution of energy efficiency state can be calculated and the feature of energy efficiency state also can be extracted. The result is shown in figure 5.
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energy efficiency model, the model of signal feature in time and the convolution in frequency) no matter how different parameter, different machine tool or different cutting tool. So the result of high energy efficiency state of these experiments is shown in figure 6.
Figure 5 The convolution of energy efficiency state. The horizontal-axis is time (s) and vertical is amplitude. The data is cutting parameters. (rotation speed of spindle/feed rate/cutting depth)
Figure 4 The relationship between temperature and time of low energy efficiency state (rotation speed of spindle: 150r/min, feed rate: 12mm/min, cutting depth: 0.5mm)
The total result of experiments is shown in figure 6. Three types of energy efficiency state have been separated by statistic analysis of data. It can be seen that not only the energy efficiency state under certain cutting condition can be judged, but also the energy efficiency state at any time can be evaluated. So the energy efficiency state for milling process can be extracted in this method.
Table 2. Risen temperature of thermocouples (RS is rotation speed r/min, F is feed rate mm/min, MAX is the maxim risen temperature, MIN is the mini risen temperature) No.
RS
F
ap
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
150 150 150 150 150 550 550 550 550 550 550 550 550 760 760 760 760 760 760 760 760 1500 1500 1500 1500
12 12 12 78 78 12 12 12 78 78 78 270 270 12 12 12 78 78 270 78 270 12 78 78 270
1 0.5 1.5 1 1.5 1 0.5 1.5 1 0.5 1.5 1 0.5 1 0.5 1.5 1 0.5 1 1.5 0.5 1 1 1.5 1
TB1 0.57 0.49 0.33 0.15 0.11 2.03 3.02 1.82 0.61 2.08 0.97 0.12 2.13 1.95 4.11 2.21 1.04 1.75 0.92 1.33 1.47 0.70 1.59 0.92 1.82
TB2 0.62 0.57 0.35 0.17 0.14 2.09 3.17 2.95 0.73 2.13 1.10 0.15 2.27 2.00 5.19 2.23 1.54 2.46 1.10 1.72 1.51 1.22 1.75 0.96 2.07
MAX 11.28 6.60 17.43 9.74 11.19 17.59 7.09 24.25 11.16 6.26 18.03 7.53 2.62 19.22 11.59 24.88 11.80 10.09 7.16 23.12 8.55 33.30 21.95 36.07 11.19
MIN 3.44 2.45 7.30 4.28 4.75 4.81 4.67 10.11 4.45 2.67 11.71 5.30 5.50 12.08 5.45 10.60 5.81 2.53 5.32 14.99 2.25 13.31 12.69 20.06 8.82
The energy efficiency state of milling processes can be easily identified by convolution as shown in figure 5. So the result can be obtained that the energy efficiency state can be identified by energy efficiency state mechanism (combining
Figure 6 The result of high energy efficiency state identification
4. Conclusion Method of energy efficiency state identification for milling processes based on mechanism analysis in this paper. It was found that, (1) The model of energy efficiency state is proposed and the energy efficiency state mechanism is revealed based on conservation of energy. (2) The form of signals is analysed and the quality of the energy efficiency feature is proposed which is product in time and convolution in frequency. (3) The relationship between signals and feature of energy efficiency state is proposed and the other state (machine-tool state and tool state) has been also separated and the time and frequency feature has been extracted based on the experiment.
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In conclusion, the proposed method in this paper will play an important role in energy saving efforts and be beneficial for manufacturing systems evaluation. Acknowledgements The authors truly appreciate the support by the National Natural Science Foundation of China (Project grant numbers: 51575357). References [1] Philipp Eberspächer, Philipp Schraml, Jan Schlechtendahl, Alexander Verl, Eberhard Abele, A model and signal based power consumption monitoring concept for energetic optimization of machine tools, Procedia CIRP 15 (2014) 44-49. [2] R.S. Pawade, Harshad A. Sonawane, Suhas S. Joshi, An analytical model to predict specific shear energy in high-speed turning of Inconel 718, International Journal of Machine Tools & Manufacture 49 (2009) 979990. [3] A. Vijayaraghavan, D. Dornfeld, Automated energy monitoring of machine tools, CIRP Annals - Manufacturing Technology 59 (2010) 2124. [4] Konstantinos Salonitis, Peter Ball, Energy efficient manufacturing from machine tools to manufacturing, Procedia CIRP 7 (2013) 634-639. [5] Eberhard Abele, Steffen Braun, Philipp Schraml, Holistic Simulation Environment for Energy Consumption Prediction of Machine Tools, Procedia CIRP 29 (2015) 251-256. [6] Gülsüm Mert, Sebastian Waltemode, Jan C. Aurich, How services influence the energy efficiency of machine tools: A case study of a machine tool manufacturer, Procedia CIRP 29 (2015) 287-292. [7] Jingxiang Lv, Renzhong Tang, Shun Jia, Therblig-based energy supply modeling of computer numerical control machine tools, Journal of Cleaner Production 65 (2014) 168-177. [8] Eberhard Abele, Niklas Panten, Benjamin Menz, Data Collection for Energy Monitoring Purposes and Energy Control of Production Machines, Procedia CIRP 29 (2015) 299-304. [9] Jingxiang Lv, Renzhong Tang, Shun Jia, Ying Liu, Experimental study on energy consumption of computer numerical control machine tools, Journal of Cleaner Production 112 (2016) 3864-3874. [10] Jeffrey B. Dahmus and Timothy G. Gutowski, An Environment Analysis of Machining, ASME International Mechanical Engineering Congress and RD&D Expo (2004) 1-10.
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