# A Reliable Turning Process by the Early Use of a Deep Simulation Model at Several Manufacturing Stages

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## Abstract

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## 1. Introduction

^{®}offers a virtual assistant for setup and maintenance support in-office or off-site aimed at management functions, work scheduling, mobile alerts, etc. The Web Monitor of DMG-Mori Seiki

^{®}connects any machine tool to a web-based platform. In both cases users can log in to the control to access this information remotely from any internet-connected device and see an up-to-date status of their machine operation.

## 2. Models in Machining Processes

^{®}developed by Prof. Y. Altintas is a well-known simulation software for chip removal processes that solves the process stability equation in the frequency domain [18]. Recently, this laboratory released new more complex software, MACHpro

^{®}, to integrate the compliance of a given system into a CAM module aimed at tool path optimization (both trajectory and cutting parameters) from a dynamic point of view.

^{®}records the sound signal during cutting, which is then noise filtered and post-processed before being transformed into the frequency domain. The optimum spindle speed is calculated from the measured vibration frequency, the natural frequency of the system, the initial spindle speed, and the end-mill number of teeth. Similarly, Harmonizer

^{®}is able to determinate the chatter frequency just by measuring the Acoustic Emissions (AE); in two or three iterations the program is able to find a spindle speed where the cut would be stable [19]. However when the machining depth of the cut is too high or in finishing operations where the vibration modes notably vary (in magnitude and direction along time), this solution has a difficult convergence. So, the system is useful for 1 degree of freedom (DOF) systems and for rough to medium machining operations. Normally, such types of software allow for introducing information from real cutting tests for feedback, and to tune the predictive analysis. For instance, the MetalMAX

^{®}package offers a complete modal characterization module (TXF), prediction software (MilSim), and the Harmonizer module to obtain the recommended new spindle speed.

## 3. Software Used for the Integrated Approach

^{®}, which integrates the models developed during the last few years concerning static and dynamic behaviour (Section 4) of the turning, boring, and milling processes. The development tool Visual Studio was selected for the program code, due to its multi-step nature. Additionally, MATLAB was used to create other different modules in which numerical calculations are required.

^{®}only uses 50 MB of memory. The program may run on the Windows Vista, Windows 7, 8, and 10 platforms. Users should install the Matlab Compiler Runtime program to get access to the Matlab functions as well.

#### 3.1. User Interfaces

#### 3.2. Simulation under Static Mechanical Conditions

#### 3.3. Simulation of the System with Dynamical Behavior

^{®}, the user would be able to predict those dynamic problems by obtaining the so-called “stability lobes”. These graphs define the boundary between the stable and unstable cutting conditions (plotting Spindle speed S vs. Depth of cut a

_{p}) in the working range once the dynamic features, tool geometry, and material properties are fed into the model. In Section 4.2, the basics from the numerical method implemented in Dynpro

^{®}will be presented. Both the turning/boring [20] and milling models [21] have a similar code structure.

^{®}includes an innovative toolbox to filter and fit the frequency response function (FRF) archives to the modal parameters, which are easily recognized and used by the program. These are then directly set by the keyboard in the Modal parameters tab. Otherwise, a *.txt archive with equivalent information can be loaded. Once the modal parameters are loaded, a final tab with the simulation parameters must be filled.

#### 3.4. Signal Measurement and Analysis

^{®}dynamometer, Artis

^{®}, vibration analyzers, data acquisition cards DAQs, etc.), all of them common in a usual workshop. The Dynpro

^{®}software has a power toolbox for in-process power measurements that makes this task easier. Additionally, a low-cost device is implemented for measuring the power directly from the machine servodrives, using an Ethernet connection. After that, the user can post-process these time signals, for example, to obtain estimations of the specific cutting energy. Once the threshold value for the power consumption in stable cutting is defined, this feature can be used either to predict tool breakages or to keep wear under control [22].

#### 3.5. Resources

#### 3.6. Functions and Modules

^{®}offers an integrated development environment with a custom programming language, which is the most appropriate option for developing these libraries. Using this programming design, a black box utility isolates the analytical expressions of the model from the Dynpro

^{®}interface.

^{®}Windows functions were used to generate the report of the simulations. These functions, which belong to the Visual Studio development kit, allow the creation of a Word document from the simulation data obtained by Dynpro

^{®}.

#### 3.7. Tests

^{®}offers a toolbox for obtaining and fitting the modal parameters (peak-picking) once the frequency response function (FRF) has been obtained (by impact hammers or shakers), shown in Figure 5.

^{®}or, if the user has undertaken experimental tests for material characterization, they may also be edited (and loaded) by the keyboard.

#### 3.8. Process Data

^{®}to reduce waiting times and unnecessary data loads, by implementing a database to save all the simulations. The features of a commercial well-known database fit in well with the required amount of data to be saved.

^{®}allows user to create an easy-to-use parameter file by just introducing the machining system dynamic behavior specifying the frequency, stiffness, and damping of each mode.

## 4. Inside the Software: The Deep-Model

- Analytical models, out of use currently.
- Mechanistic models based on the concept of specific force. They are precise enough and executed in milliseconds, and are suitable for being used in real production.
- Finite Element Models to represent chip deformation, but are too slow to be use in daily production. FEM is a tool for other types of analysis, for example fixture design.
- AI-based models based on neural/Bayesian networks or on fuzzy logic. Without a model based on the chip removal mechanism, these are useless.

#### 4.1. Cutting Force Prediction

_{MT}, Y

_{MT}, and Z

_{MT}or in the local tool axis (using in this case the indices: c, cutting force; t, tangential force; and r, radial force). The mechanistic approach assumes that force components are composed of two summation terms, one responsible for the shearing mechanism related to the chip formation mechanism and depending on the chip section (a

_{p}∙f or b∙h) and the other related to the friction of chip sliding onto the tool rake face that is proportional to the engaged edge length (or chip width b). Generally, the cutting forces can be expressed as:

_{rct,c}and K

_{rct,e}are the cutting and friction specific coefficients respectively, which depend on the chip section (b*h) and chip width (b). With slight differences, similar relationships are found for the other processes such as boring or milling. Once these coefficients are defined, the mechanistic model predicts the static cutting forces based on Equation (1). In other words, Dynpro

^{®}makes use of the cutting coefficients (obtained either from a material database or from user-defined ‘previous experiments’) to calculate the cutting forces. It also predicts the cutting forces in finishing operations including the effects of any particular tool geometry (with nose radius r

_{ε}and cutting edge angle κ

_{r}).

#### 4.2. Chatter Avoidance

^{®}to study stability combines the collocation method [27] which is a numerical method with the dynamics of the machine tool in modal coordinates. The multi-mode approach takes into account the orientation of the machine tool modes. This modal vector is referenced to the machine-tool axes, denoted X

_{MT}− Y

_{MT}− Z

_{MT}. Thus in turning, X

_{MT}defines the radial direction, Y

_{MT}the cutting speed direction, and Z

_{MT}the feed (or axial) direction, in a common straight turning operation (Figure 6a). One frequency mode is defined by two angles: β

_{y}, the angle between the modal vector i

_{m}and the X

_{MT}− Z

_{MT}plane and β

_{xz}, the angle between its projections over the X

_{MT}− Z

_{MT}plane with respect to X

_{MT}. In milling (Figure 6b), the machine tool axes are different from the tool local axis. Here, Z

_{MT}is defined by the rotational motion of the tool (or axial) and α

_{f}is the angle between the feed direction (X

_{T}) and X

_{MT}. The modal orientation is defined through the angles β

_{z}, between i

_{m}and its projection in X

_{MT}− Y

_{MT}, and β

_{xz}, between the projection of i

_{m}and X

_{T}. α

_{x}is the sum of the angles α

_{f}and β

_{xy}.

## 5. Application to Real Cases

#### 5.1. Case Study 1: Design of Clamping Systems

^{®}was used to predict the cutting forces for the different tools, workpiece materials, and cutting conditions. Figure 8 shows the three Cartesian cutting forces for two typical turning tools, a rhombic 35° insert (standard ISO VBMT) and a square-45° insert (SNMG).

#### 5.2. Case Study 2: Chatter Free Turning of Large Crankshafts for the Naval Industry

^{®}was used to predict chatter-free zones in the turning of large crankshafts, calculating the stability lobes that can be used for evaluating the best dynamic behavior among different virtual architectures. The selected machine for the model validation was a horizontal lathe. As seen in Figure 10, the dynamic study of the process was performed following different levels. Firstly, the modal data from the machine tool cutting system was obtained by FEM and hammer impact tests. Secondly, the simulation of the dynamic behavior module simulated the stability limits for an operation typically performed in this machine.

^{®}. With the power values, the specific cutting coefficients in the Cartesian and tool coordinates were obtained for a rhombic carbide insert (CNMG19) and AISI 1045 steel, resulting in Kt = 2079 MPa, Kr = 775 MPa, and Ka = 533 MPa. It must be noted that this tool is prone to vibration in the axial direction (Z) when performing straight turning operations due to its lead angle (positioned at κ

_{r}= 95°).

^{®}allows for converting the signals to the FFT spectrum (magnitude vs. frequency) or to the spectrogram (frequency vs. time). It was observed that the machine exhibited (see Figure 13) high frequency vibration (near 3026 Hz). This was confirmed also by the wavelength of the surface marks. Figure 14a,b show the frequency signals with respect to time, the frequency spectra (FFT), and the final surface finish for the two cases, stable and chatter. The first one leads to a distributed FFT signal in the frequency range with low amplitude peaks while the second one presents a dominant peak near the natural frequency and a phase lag periodic peak pattern with respect to the cutting frequency of the cutting and its multiples (circles in red of Figure 14b).

## 6. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 7.**Stiffness measurement between the tool tip and a planet carrier. (1) Strain gauge; (2) Adapted design tool; (3) Part; (4) Strain indicator device.

**Figure 8.**Cutting forces from Dynpro

^{®}for two turning inserts: (

**a**) Rhombic 35° (VBMT); (

**b**) Square 45° (SNMG).

**Figure 14.**Frequency spectrum of the signal and surface finish: (

**a**) Stable case a

_{p}= 5 mm/n = 80 rpm; (

**b**) Unstable case: a

_{p}= 10 mm/n = 100 rpm.

Element | Mode # | Modal Parameters | ||||
---|---|---|---|---|---|---|

f_{n} [Hz] | u_{x} [1/√kg] | u_{y} [1/√kg] | u_{z} [1/√kg] | ξ | ||

Workpiece | 1 | 30 | 0.0067 | −0.1508 | 0.2770 | 0.020 |

Tool/Machine | 1 | 178 | 0.55 | 1.078 | 0.814 | 0.020 |

2 | 1338 | 2.40 | 0 | 4.88 | 0.030 | |

3 | 1736.3 | 4.45 | 3.71 | 9.75 | 0.020 | |

4 | 2220.1 | 1.15 | 3.18 | 6.27 | 0.023 | |

5 | 2509.6 | 6.20 | 13.85 | 7.87 | 0.0175 | |

6 | 2600 | 4.15 | 24.22 | 3.9 | 0.015 | |

7 | 3026.5 | 8.23 | 31.06 | 23.97 | 0.0155 |

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## Share and Cite

**MDPI and ACS Style**

Urbikain, G.; Alvarez, A.; López de Lacalle, L.N.; Arsuaga, M.; Alonso, M.A.; Veiga, F.
A Reliable Turning Process by the Early Use of a Deep Simulation Model at Several Manufacturing Stages. *Machines* **2017**, *5*, 15.
https://doi.org/10.3390/machines5020015

**AMA Style**

Urbikain G, Alvarez A, López de Lacalle LN, Arsuaga M, Alonso MA, Veiga F.
A Reliable Turning Process by the Early Use of a Deep Simulation Model at Several Manufacturing Stages. *Machines*. 2017; 5(2):15.
https://doi.org/10.3390/machines5020015

**Chicago/Turabian Style**

Urbikain, Gorka, Alvaro Alvarez, Luis Norberto López de Lacalle, Mikel Arsuaga, Miguel A. Alonso, and Fernando Veiga.
2017. "A Reliable Turning Process by the Early Use of a Deep Simulation Model at Several Manufacturing Stages" *Machines* 5, no. 2: 15.
https://doi.org/10.3390/machines5020015