Vibration Analysis and Energy Efficiency in Interrupted Face Milling Processes

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ScienceDirect Procedia CIRP 29 (2015) 245 – 250

The 22nd CIRP Conference on Life Cycle Engineering

Vibration analysis and energy efficiency in interrupted face milling processes Hugo M. B. de Carvalhoa,b*, Jefferson de Oliveira Gomesb, Marco Antonio Schmidta, Vitor L.C. Brandãoa a Renault of Brazil, Av. Renault, 1300, São José dos Pinhais, 83070-970, Brazil Technological Institute of Aeronautics, Praça Marechal Eduardo Gomes, 50 – Vila das Acácias, São José dos Campos, 12.228-970, Brazil

b

* Corresponding author. Tel.: + 554191071312.E-mail address:[email protected]

Abstract The planning of a face milling process determines how the milled part is produced: an incorrect or incomplete planning process will result in a loss of productivity. This paper proposes that loss of productivity be avoided by including vibration analysis and consideration of energy efficiency in the development of face milling processes for interrupted cutting. A new methodology was developed for inclusion of vibration analysis and consideration of energy efficiency in the design of face milling processes. A case study was examined in which the new methodology was applied to the milling process for an automotive engine block.

© 2015 The Authors. Published by Elsevier B.V. B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the International Scientific Committee of the Conference “22nd CIRP conference on Life Cycle Peer-review Engineering.under responsibility of the scientific committee of The 22nd CIRP conference on Life Cycle Engineering Keywords: Energy efficiency; machining; face milling; vibration analysis

1. Introduction Interest in energy efficiency has been growing over the past 50 years, due to increases in energy consumption, raw materials, environmental pollution, and the scarcity of natural resources. This scenario has represented a major challenge to companies and societies. Industrial manufacturing by companies is one of the main activities that require large amounts of energy resources to transform and create products. Worldwide, this sector represents 28% of energy consumption [1]. Machining is one of the production processes performed with computer numerical control (CNC) machine tools and powered by electricity. According to Behrendt et al. [2], in 2010, the electricity consumption of these machine tools represented approximately 31% of the total US consumption of energy. Analyses of the energy consumption of machine tools by Gonzalez [3] found that 56% of energy consumption is due to machining, 25% due to positioning operations, and 19% is due to energy being expended by auxiliary devices that

are always on. The design of machine tools and machining processes obviously influence these levels of consumption. A substantial amount of research on cutting conditions has been conducted to seek ways to reduce energy consumption in machining processes or related themes to energy consumption. Chen et al. [4] and Hinduja and Sandiford [5] created models and methodologies for selection of cutting conditions in machining, based on minimizing the costs of turning and milling processes. Narita et al. [6] described a methodology for reducing the environmental impact of a machining process. Chapman [7] suggested that the energy used in a machining process can be reduced by studying each aspect of the process in detail. Mantivenga and Rajemi [8] stated that optimisation should be performed to select the combination of cutting depth (ap), feed (f), and cutting speed (v) that minimises energy consumption and process restriction. However, previous research on the sustainability of machining with a focus on economy has not defined a methodology for reducing energy consumption that is based on analysis of the natural frequencies of cutting tools.

2212-8271 © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of The 22nd CIRP conference on Life Cycle Engineering doi:10.1016/j.procir.2015.02.165

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Furthermore, in the factory, when a new machining process is proposed by a supplier, it is often the supplier who specifies the feed and cutting speed parameters, based on previous experience in use of the cutting tool with other pieces and machines. Therefore, the first choice of parameters is made subjectively, without an analysis of the possible behaviour of the machine tool and workpiece system. This may result in incorrect choices for the values of cutting parameters, especially with respect to roughness, which can cause vibration during machining. Machining vibration increases energy consumption because if we work with the tool within its natural frequency of vibration, there is greater variation in the cutting force. Blocking this vibration results in an increase in the material removal rate and thus an decrease in the consumption of electricity. The objective of this study was to demonstrate the application of a methodology for vibration analysis and consideration of energy efficiency to a face milling process to achieve lower power consumption and higher productivity. Face milling was chosen as the process to be studied because it often exhibits problems with vibration during machining. 2. Method The main task in the planning process is to identify each step of the manufacturing process. It is then necessary to define the machining parameters. Inaccuracies in defining the steps may result in a loss of quality and increased costs for the manufactured product. Normally, the parameters are defined on the basis of experience or manual use of the cutting tool. The parameters are defined to achieve the cycle time of the manufacturing line. As a result, all subsequent correction processes require additional time and money. In the face milling process, the cutting parameters that need to be set are the cutting depth (ap), the feed (f), and the cutting speed (v). Measurements of machine tools used in face milling processes show that the power consumption of the spindle accounts for nearly 80% of the total consumption. The energy consumption due to the feed force and feed depth account for 10% of the total consumption. The parameters of the machining process must be analysed to develop a method to increase the energy efficiency of the process. The cutting speed determines the chip removal rate, as well as the power required for machining for a given feed, cutting depth, and tool geometry. An increase in the area of the cutting section, feed, cutting depth, or tool geometry results in an increase in the cutting power required. A common mistake is to suppose that power reduction always results in reduction in the energy consumption of the face milling process. A reduction in cutting power combined with an increase in the manufacturing time of the part causes an increase in energy consumption. This occurs because, depending on the machine, the largest amount of electrical power is consumed by auxiliary devices associated with the machine. Depending on the process, when there is a reduction in cutting power, there is only a 15% to 25% reduction in the total electrical power consumption of the machine. These

values differ from those reported by Gonzalez [3]. Thus, it is necessary to reduce the manufacturing time for a part by reducing the total machining time. The total energy consumed is equal to the sum of the energy consumed by machining and the energy consumed by machine-related devices, as shown in equation 1.

Etotal

P

machining

 Pmachine. ˜ tmanufacturing

(1)

where: x tmanufacturing = total time of manufacture of the part. This time is the sum of the machining time and the time required for positioning of the cutting tool. x Pmachining = average electrical machining power x Pmachine = average electrical machine power The cutting depth and the cutting speed should be increased to reduce the manufacturing time. If the cutting depth decreases and the number of passes increases, the machining time increases despite the reduction in the cutting force and electrical cutting power. The cutting speed should be the maximum possible within the working range of the cutting tool to avoid vibration. Vibration does not allow for a higher feed value and cutting depth, making the workpiece worse in terms of surface quality. An increase in cutting speed allows for an increase in feed. The feed must be as high as possible because an increase in feed reduces the manufacturing time for the cutting path strategies used during machining, as shown in equation 2. Of course, there are restrictions, in terms of roughness, form error, maximum electric power of the machine, and tool wear in achieving the maximum feed possible.

§ l · ¸¸ t machining ¨¨ © fz ˜ n ¹

(2)

where: x l = path of the cutting tool x fz = feed per teeth x n = spindle speed According to König et al. [9] and Reinhart et al. [10], the power necessary for the process can be calculated using equation 3.

Pmachining Fc ˜ vc

(3)

where: x Pmachining = power necessary for the process x Fc = cutting force x vc = cutting speed A decrease in the machining time reduces energy consumption more than the resulting increase in electricity consumption for machining power. Increasing the feed

Hugo M.B. de Carvalho et al. / Procedia CIRP 29 (2015) 245 – 250

reduces the machining time and thus the total energy consumption. The total energy consumed is the sum of the cutting power consumed and the power consumed by auxiliary devices during the machining time. Therefore, reducing the machining power reduces only part of the electrical energy consumption. In the machine analysed, the electrical power consumed by auxiliary devices represents more than one third of the total power consumption during the face milling process. The parameters of the face milling process are illustrated in Fig. 1.

Fig. 1: Parameters of cutting forces for face milling [9]

The cutting force of the face milling process is given by equation 4 [9].

Fc

a p ˜ Cs1 ˜ f zr ˜ N r 1 ˜ 1  0,01 ˜ D ˜ senM r

(4)

where: x M = rotation angle of the cutting edge x fz = feed per tooth x hm = undeformed chip thickness x y = distance from the side of the workpiece to the centre of the cutter x ap = depth of cut x α = axial rank angle x N = approach angle of the cutter Substituting equation 4 into equation 3 and then into equation 1, without taking into account P machine, yields the following equation:

E

M ˜ a p ˜ f zr 1 ˜ l

where:

Cs1 ˜ senF ˜ 1  0,01 ˜ D ˜ senM ˜ S ˜ d (6) 1000 ˜ z Vf r 1

M

fz

(5)

r

˜

n˜ z

where: x d = diameter of the cutting tool x z. = number of teeth x Vf = feed speed

(7)

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The value of the parameter r is 0.71 for cast iron. In equation 4, the energy consumed is inversely proportional to the feed rate (Vf-0.29) and directly proportional to the cutting speed (n0.29) [9]. The energy consumed increases linearly with the cutting depth, according to equation (4). However, if the tool path is shortened, the energy consumed during machining will decrease. Another parameter of the equation is the cutting speed of the tool. In face milling of surfaces without continuous cutting, such as in the machining of the cylinder head surface and oil pan surface of an engine block, the thickness of the chip is not uniform, even if the passing frequency of the inserts, determined by the cutting speed, is equal to the vibration frequency of the tool. Therefore, the machining strategy adopted in this study was to machine using a cutting speed as different as possible from the natural vibration frequency, so that the tool oscillations are as small as possible. The proposed methodology described in this paper involves selecting the cutting speed on the basis of an analysis of tool vibration. Oscillation of a cutting tool increases at cutting speeds close to the natural vibration frequency of the tool. The chip thickness varies between a maximum and a minimum value, as shown in Fig. 2 Vibration increases the energy consumption and electrical power consumption. In addition, the energy consumption increases when the cutting depth or feed is decreased to reduce the cutting power.

Fig. 2: Variation in chip thickness

A procedure to minimize the consumption of electrical power was developed for application to a new face milling process. The steps in the procedure are shown in Fig. 3. The first step in the procedure is to identify the parameters associated with the operation of the cutting tool and the machine. The second step is to determine the natural vibration frequency of the cutting tool. The third step is to identify the parameters related to the cutting depth and cutting speed. The feed must then be defined to determine how to reduce the cutting time and the change of the tool path. The last step is to answer the following question: Is it possible to increase the feed? It is possible to increase the feed until one of the following restrictions prevents any additional increase: x Roughness x Form error x Electrical power x Tool wear

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for application of the new methodology. The machined faces of the cylinder block were the oil pan face and the cylinder head face, illustrated in Fig. 5.

Fig. 5: Faces of the block cylinder: a) cylinder head face b) oil pan face

Fig. 3: Methodology for reducing energy consumption in the machining process

The previous process used a milling cutter with eight inserts. Other details of the cutting process are shown in Fig. 6. When this process was used, damage to the inserts occurred frequently, which made it impossible to increase the rate of chip removal and the energy efficiency.

2.1 Equipment The equipment used to determine the natural frequency of the cutting tool consists of the following: x x x x x x x x x

NI Compact DAQ hardware chassis Model AEL15US12 voltage source Type AB USB cable NI 9234 (VGA) analog-input amplifier module Coaxial communication cable PCB accelerometer Hammer Desktop or laptop personal computer (PC) Data acquisition software

Fig. 6: Parameters of the milling process for the oil pan face and cylinder head before the method was applied

The machining centre used in this study was a BZ600 horizontal four-axis produced by GROB. Fig. 7 shows the data for this numerical control (NC) machine.

The acquisition system shown in Fig. 4 is used to determine the natural frequency of the cutting tool. The cutting tool must be in the spindle. The acquired data are analysed, and the frequencies with the highest oscillations are identified. Fig. 7: Machine data

3.1 Definition of the range of cutting conditions of the new cutting tool

Fig. 4: Acquisition system; 1) laptop personal computer, 2) Data acquisition software, 3) Hammer, 4) PCB accelerometer, 5) Model AEL15US12 voltage source

3. Case Study The methodology was applied to a face milling process used on an automotive manufacturing line. A rough face milling process for the faces of a block cylinder was chosen

The first step in the methodology is to define the range of parameters for the cutting tool. The tool chosen for the test was a mill with 12 octagonal inserts. The tool chosen has more inserts than the tool currently to allow it to work at higher speeds and greater cutting depths. A ceramic insert was chosen to achieve higher cutting speeds and higher feeds. The mill code is F45NM D125-12-40-R08A, and the code of the insert is ONHQ 0806-TN IS8. The ranges of cutting conditions for use of the tool and the insert are shown in Table 1. This information was provided by the supplier.

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This spindle speed is close to the maximum speed of the tool. A high spindle speed makes it possible to increase the feed, which reduces the machining time and the energy consumed for machine a given volume of material. Initially, the milling process was performed in two passes. To reduce the machining time, the machining was set to be performed in a single pass, which was possible using the new cutting tool.

Table 1: Details of the insert and the cutting tool Parameters

Minimum

Maximum

n (rpm)

300

3500

fz (mm/z)

0.025

z

0.4 12 (constant)

d (mm)

125 (constant)

ap (mm)

1

249

5.5

3.3 Determination of the feed for a given machining time 3.2 Determination of the spindle speed and cutting depth parameter values The second step of the methodology is to determine the natural frequencies of the new tool. A hammer and an accelerometer were used to do this. The results are shown in Fig. 8.

According to the methodology, the feed needs to be as high as possible to maximize productivity and energy efficiency. Furthermore, the feed has little influence on the tool life in comparison to the cutting speed. However, there is a restriction: increasing the feed can produce roughness and deformation of the part, depending on the tolerance of the geometry of the part and its fixation. The feed was set to 4200 mm/min, which corresponds to a value of 0.125 mm/insert for the feed per tooth. 3.4 Modification of the tool path

Fig. 8: Frequency response function (FRF) of the new mill

As Fig. 8 shows, there are two natural frequencies in the tool with high amplitudes of oscillation. The natural frequency with the highest amplitude is 679.94 Hz, and the other is 854.01 Hz. Equation 6 is used to determine the spindle speeds at which the passages of the inserts coincide with the natural vibration frequency of the cutter.

N

60 ˜ f n j˜z

Once the methodology for the feed is defined, the path of the cutting tool must be analysed. The old path included a change in the feed direction. This change of direction during the machining was eliminated in the new path. The differences in the paths for the old and new processes are illustrated in Fig. 9. The machining centre table does not permit the cutter to go beyond the lower part of the piece to be machined.

(6)

where: x N = spindle speeds at which the tooth passing frequency is equal to an integer fraction of the natural frequency x J = an integer x z = number of teeth The spindle speed is obtained using data from the tool. Its natural frequencies of vibration are shown in Table 2. Table 2: Spindle speeds that coincide with the natural frequency of the new cutting tool Natural N N N N N N N Frequency (j=1) (j=2) (j=3) (J=4) (j=5) (j=6) (j=7) (Hz) 679.94

3400

1700

1133

850

680

567

486

854.01

4270

2135

1423

1068

854

712

610

The spindle speed chosen was the one that was the most different from the natural frequency of the passage of the inserts. For this case study, the spindle speed was 2800 rpm.

Fig. 9: Milling process before and after changing the tool path

3.5 Test with the new tool The next step in the methodology is to test the new cutting parameters. The new cutting parameters are shown in Fig. 10.

Fig. 10: Parameters of the milling process for the oil pan face and cylinder head after the method was applied

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The electricity consumption of the spindle was 194.48 Wh/piece for the old milling process. The new consumption was 151.3 Wh/piece after the application of the methodology to the new tool, as shown in Fig. 11.

routes and shallower maximum depths of cut, a vibration analysis is required.

Fig. 12. Tool life of the old and the new cutting tools (before and after application of the new methodology)

Acknowledgement Fig. 11: Electricity consumption during the old and new face milling processes

The machine has 5000 W of power on standby mode. The application of the proposed methodology reduced the machining time from 50 seconds to 41 seconds. The electricity consumption of auxiliary devices associated with the machine tools decreased from 69.4 Wh to 56.9 Wh because of the reduction in machining time. 4. Conclusions A methodology for inclusion of vibration analysis and consideration of energy efficiency in the design of interrupted face milling processes was presented in this paper. In the case study considered, application of this methodology reduced the machining time of the faces of a cylinder block by 9 s, from 50 s to 41 s. The procedure for determining the correct parameter values is based on minimising consumption of electrical energy and vibration of the cutting tool and takes into account the path and the depth of cut of the cutting tool. Application of the procedure to the case study made it possible to increase the life of the cutter from 52 parts to 102 parts, as shown in Fig. 12. Before the new methodology was applied, the wear (VB) of the insert ranged from 1.3 mm to 0.4 mm, with an average of 0.74 mm. After the new methodology was applied, the tool wear range decreased to 1 mm to 0.4 mm, with an average of 0.67 mm. Thus, the wear of the insert decreased even as the life of the new tool increased. The new process can also eliminate quality problems due to insert breaks. The new methodology and the new tool reduced electricity consumption by 23%. A test was carried out with the same parameter values for the new face mill, but the machine could not machine the face because the electrical capacity of the machine was exceeded. The tool path was found to have a large impact on reducing power consumption, but for shorter

This project was part of a Ph.D. thesis completed at the Technological Institute of Aeronautics (ITA). The research was supported by and carried out on the manufacturing line of Renault of Brazil. References [1] Renaldi R, Kellens K, Dewulf W, Duflou J, Exergy efficiency definitions for manufacturing processes, 18th CIRP LCE Conference, 2011, Braunschweig, pp. 329–334. [2] Behrendt T, Zein A, Sangkee, M, Development of an energy consumption monitoring procedure for machine tools, CIRP Annals, 2012— Manufacturing Technology, 61, pp. 43–46 [3] Gonzalez, A, Machine tool utilisation phase: costs and environmental impacts with a life cycle view. 2007. 82f. Thesis (Master of Science) Royal Institute of Tecnology, Stockholm. Available at . Accessed on 25September 2014. [4] Chen SJ, Hinduja S, Barrow G, Automatic tool selection for rough turning operations, International Journal of Machine Tools and Manufacture, 1989, 29(4):535–553. [5] Hinduja S, Sandiford D, An optimum two-tool solution for milling 21/D features from technological and geometric viewpoint, Annals of the CIRP, 2004, 53(1):77–80. [6] Narita H, Desmira N, Fujimoto H, Environmental burden analysis for machining operation using LCA method manufacturing systems and technologies for the new frontier, 2008, pp. 65–68. [7] Chapman PF, Energy costs: A review of methods energy policy, 1974, 2(2):91–103. [8] Mativenga PT, Rajemi MF, Calculation of optimum cutting parameters based on minimum energy footprint, CIRP Annals - Manufacturing Technology, 2011, 60, Elsevier, pp. 149–152. [9] König W, Essel K, Witte L, Specific cutting force data for metal-cutting, 1982, Verlag Stahleisen, Düsseldorf, Germany. [10] Reinhart G, Reinhardt S, Föckerer T, Zäh MF, Comparison of the resource efficiency of alternative process chains for surface hardening, 2011, Braunschweig, pp. 311–316. [11] Heikkala J, “Determining of cutting-force components in face milling, Journal of Materials Processing Technology, 1995, 52, pp. 1 8.