Sensor Data and Information Fusion to Construct Digital-twins Virtual Machine Tools for Cyber-physical Manufacturing

Available online at www.sciencedirect.com

ScienceDirect Procedia Manufacturing 10 (2017) 1031 – 1042

45th SME North American Manufacturing Research Conference, NAMRC 45, LA, USA

Sensor data and information fusion to construct digital-twins virtual machine tools for cyber-physical manufacturing Yi Caia, Binil Starlya, Paul Cohena and Yuan-Shin Leea * a

Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, North Carolina, NC 27695, United States

Abstract This paper presents sensor data integration and information fusion to build “digital-twins” virtual machine tools for cyberphysical manufacturing. Virtual machine tools are useful for simulating machine tools’ capabilities in a safe and cost-effective way, but it is challenging to accurately emulate the behavior of the physical tools. When a physical machine tool breaks down or malfunctions, engineers can always go back to check the digital traces of the “digital-twins” virtual machine for diagnosis and prognosis. This paper presents an integration of manufacturing data and sensory data into developing “digital-twins” virtual machine tools to improve their accountability and capabilities for cyber-physical manufacturing. The sensory data are used to extract the machining characteristics profiles of a digital-twins machine tool, with which the tool can better reflect the actual status of its physical counterpart in its various applications. In this paper, techniques are discussed for deploying sensors to capture machine-specific features, and analytical techniques of data and information fusion are presented for modeling and developing “digital-twins” virtual machine tools. Example of developing the digital-twins of a 3-axis vertical milling machine is presented to demonstrate the concept of modeling and building a digital-twins virtual machine tool for cyber-physical manufacturing. The presented technique can be used as a building block for cyber-physic manufacturing development. © 2017 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license © 2017 The Authors. Published by Elsevier B.V. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-reviewunder underresponsibility responsibility Scientific Committee of 45th NAMRI/SME. Peer-review of of thethe organizing committee of the SME North American Manufacturing Research Conference Keywords: virtual machine tool; sensory data; data acquisition; information fusion; cyber-physical manufacturing; digital manufacturing.

* Corresponding author. Tel.: +1-919-515-7195; fax: +1-919-515-5281. E-mail address: [email protected]

2351-9789 © 2017 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 organizing committee of the 45th SME North American Manufacturing Research Conference doi:10.1016/j.promfg.2017.07.094

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1. Introduction Here introduces the paper, and put a nomenclature if necessary, in a box with the same font size as the rest of the paper. The paragraphs continue from here and are only separated by headings, subheadings, images and formulae. The section headings are arranged by numbers, bold and 10 pt. Here follows further instructions for authors. Since the early 1990s, there has been a trend in manufacturing from ‘real’ to ‘virtual’ production. It becomes possible that some of the activities of a physical manufacturing system can be simulated with the aid of computers. By understanding and emulating the behavior of a particular manufacturing system on a computer prior to physical SURGXFWLRQ WKH DPRXQW RI WHVWLQJ DQG H[SHULPHQWV RQ WKH VKRS ÀRRU FDQ EH VLJQLILFDQWO\ UHGXFHG [1]. Virtual PDQXIDFWXULQJV\VWHPVSURYLGHDXVHIXOPHDQVIRUSURGXFWVWREHPDQXIDFWXUHGµULJKWWKH¿UVWWLPH¶ZKLOHUHGXFLQJ the need of numeURXVSK\VLFDOWHVWLQJRQWKH VKRS ÀRRU:LWKD YLUWXDOV\VWHPOHVV PDWHULDOLV ZDVWHGDQGDFWXDO PDFKLQHLQWHUUXSWLRQVRQWKHVKRSÀRRUFDQEHDYRLGHG7KHVDIHW\OHYHORIWKHZRUNLQJHQYLURQPHQWLVLPSURYHG[2]. Manufacturing data management and product life-cycle management can be easily documented systematically [3]. When such a system is made Internet-enabled, collaborations between different factories or sections can be supported [4]. ,Q UHFRJQLWLRQ RI WKHVH EHQH¿WV D QXPEHU RI YLUWXDO V\VWHPV KDYH EHHQ GHYHORSHG 6RPH UHVHDUFK IRFXVHG RQ Virtual Manufacturing, which was in tandem with the development of a Digital Factory or e-manufacturing. Within VXFK DQ HQYLURQPHQW SURGXFWV GHYLFHV VKRS ÀRRU FDSDELOLWLHV DQG VRPHWLPHV WKH HQWLUH HQWHUSULVH DUH PRGHOOHG thus enabling decision-making for manufacturing processes [4-6]. Development of Virtual Machine Tool systems has also gained interest in the research community. A large amount of work has been conducted either for the purpose of designing machine tools [7] or simulating machine tool’s capabilities [8,9]. Some was done either in a wider simulation mode such as construction of a complete machine tool model [10], while others were in a more VSHFL¿FPRGHE\VLPXODWLQJDVSHFLILFW\SHRIPDFKLQLQJDSSOLFDWLRQ[11]. The capabilities of virtual systems have expanded rapidly over the years, from simple simulation (e.g. straightforward tool-path generation, NC-code YHUL¿FDWLRQ DQG PDWHULDO UHPRYDO VLPXODWLRQ  WR SHUVRQQHO WUDLQLQJ [12,13] and complex predictions (e.g. tool life analysis, surface topography analysis, chip simulation, machining error predictions, vibrations and temperature) [1]. Other virtual systems such as Virtual Assembly [14,15], Virtual Tooling [16,17] and Virtual Prototyping [18,19] have also been studied over the years. The reliability of the functionalities provided by the virtual machine tools is highly dependent on the level of emulation to the physical tools. However, due to the system complexity and process uncertainty, it is often challenging to build a virtual machine tool realistic to its specific physical counterpart. Just like two mobile cars of the same model from the same factory. Even though they are supposed to be highly identical in terms of performance, they may still have differences in driving noise, horse power, acceleration speed and gas consumption rate. They can be considered to have their own characteristics like human beings. The same also applies to machine tools. Performance differences exist between machines of the same model and brand, not to mention those of different models and brands. As a result, applying the same virtual machine tool to two physical machines of the same model and brand is not always accurate. This paper presents methods to build the “digital-twins” virtual machine tools of physical machines for cyberphysical manufacturing by using sensory data and information fusion integration techniques. This paper proposes the integration of manufacturing information and sensory data into virtual machine tools to improve their accuracy and capabilities. Techniques are presented to convert the sensory data into the machining characteristics profiles of a specific virtual machine tool, with which the tool can better reflect the actual status of its physical counterpart in its various applications. It is our intention of this paper to present the technical method as one of the major building blocks for cyber-physic manufacturing development. An example of developing a “digital-twins” virtual 3-axis vertical milling machine with power and vibration sensors is presented for demonstration with preliminary results. Details of the proposed techniques are presented in the following sections.

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2. Developing digital-twins virtual machine tools using sensory data and information integration Fig. 1 shows the schematic of the proposed development of “digital-twin” virtual machine tool integrated with sensory data and machining information. The manufacturing data include the key machining parameters describing the manufacturing process, for example, work piece material and size, spindle speed, feed rate, depth of cut, G-code, cutter location, etc. The sensor data are collected from a variety of sensors used in machining monitoring and analysis, such as electrical current sensor for power measurement, accelerometer sensor for vibration measurement, dynamometer for force measurement and acoustic emission sensor, etc. The overall architecture for data acquisition and management is illustrated in Fig. 2. The manufacturing data are obtained from the machine controller, while the sensor data from sensory data acquisition device. They are organized at a gateway and uploaded to data base through internet (see Fig. 2). Users can get access to the data through internet using devices like computer and tablet. The immediate goal is to combine these two types of sources into a virtual machine tool to construct its characteristics profiles. For example, a machine may undergo more significant vibration in a specific cutting direction under the same cutting parameters, or it consumes more power than another machine of the same type when cutting the same part with the same tools and G-code. These characteristics make the virtual machine tool a more realistic avatar of its physical counterpart. The characteristics profiles of the machine are updated after each machining task. This helps to keep track of and predict the performance of the machine. As a result, when the virtual machine tool is used to simulate the fabrication of new parts, the simulation results can reflect the up-to-date status of the machine and are thus likely to be more accurate. The ultimate goal is to more accurate provide diagnosis, prognosis and optimization to improve the performance and utilization of the physical CNC machines. Digital-twins g virtual machine tools Monitoring

Analysis

Simulation

Management

Characteristics profiles

Manufacturing data Material, dimension, cutter location, cutting parameters (spindle speed, feed rate, chip load), Gcode, etc.

Diagnosis Prognosis Optimization

Sensor data Current, vibration, dynamometer, acoustic emission sensor, etc.

Physical CNC NC machines Fig. 1. Schematic of constructing the digital-twins virtual machine tool integrated with sensory data and manufacturing information fusion.

With the two types of data collected and fused, real-time monitoring of the machining process can be conducted in a more comprehensive way. The data also provide rich sources for off-line review and analysis after the machining process. For example, tool wear prediction has been accomplished by utilizing third degree regression models and pattern recognition system based on the data from simultaneous detection of acceleration and spindle drive current [20], and dynamic data driven approaches have been developed based on data from dynamometer, accelerometers and acoustic emission sensors for monitoring and prediction of cutting tool wear condition [21]. From the shop floor management level, virtual machine tools with characteristics profiles provide a panorama of the strengths and limitations of each machines and help management personnel better allocate the tasks and among

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multiple machine resources. The overall objective is to provide diagnosis, prognosis and optimization via the cyberphysic manufacturing systems to improve the performance of the physical CNC machines, as shown in Fig. 1.

Users

CNC machine

Controller Internet Gateway

Sensors

Data acquisition

Data base

Fig. 2. System architecture for data acquisition and management.

To facilitate the autonomous decision inference, machining data are recorded and processed to extract machining feature information, as shown in Fig. 3. In Fig. 3, three types of machining data and information are recorded and passed on for information extraction: machine data stream, the sensor data stream, and machine health status. The machine status table stores the system status and information about the part and program. The machine_data_stream table contains other machine data like coolant level, tool number, cutter location and spindle speed. The sensor_data_stream table stores the text files containing the sensor data. Two important keys, the machine_name and timestamp, are placed in each of the tables and used to associate the data in the three tables. As a result, it is easy to retrieve the data of the machine of interest within a time window using standard database inquiry functions. Detailed technique of streaming data for machining analysis and information extraction has been developed in our earlier work as presented in [27].

Fig. 3. Recording machining data and extracting machining feature information for inferencing decisions.

3. Development of sensory data and information fusion for cyber-physical manufacturing 3.1. Machine tool sensory data and information integration

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Currently a virtual machine tool test platform integrated with manufacturing and embedded sensor data is being developed in our lab in North Carolina State University. A Haas VF-2 3-axis CNC vertical milling machine was chosen as the test bed, as shown in Fig. 4(a). A corresponding CAD model with proper simplification has already been built with SolidWorks software, as shown in Fig. 4(b). The movable parts such as the working table, the spindle case and the doors were built individually. By transferring the different parts of the model into STL format separately and assembling them with the open-source OpenGL library for display, a graphically realistic virtual milling machine can be built and work like the real one. Given much work has been done on graphic rendering of virtual machine tool [10,22,23], this paper will focus on the data acquisition and analysis. Two different sensors are installed on the machine at the current stage. A Hall effect sensor (CR4111S – 100, CR Magnetics Inc.) was mounted around one of the output cables of the vector drive the CNC machine to capture the change in current consumed by the spindle, as shown in Fig. 5(a). A ground-isolated 3-axis accelerometer (Type 8762A10, Kistler Instrument Corp.) is used to capture the induced vibration between tool and workpiece during the milling operation. It is mounted magnetically on the spindle case as shown in Fig. 5(b) to avoid the splashing of coolant which will hamper the measurement accuracy and shorten sensor working lifetime. A data acquisition device is programmed to capture the spindle power consumption and vibration data as shown in Fig. 1. The outputs of the Hall effect current sensor and the accelerometer are fed into an NI 9234 4-channel analogue input module mounted on an NI cDAQ-9184 Ethernet chassis. The maximum sample rate is 51.2 kHz per channel. The data are collected on a desktop computer installed with Window 7 system using a program compiled with C#, and uploaded to an open-source PostgreSQL database on a local PC through the internet using a script in Python. Since it is of extremely low efficiency to upload the large amount of data collected in kHz one by one through the internet to the local data base, the data collected in a predefined interval (i.e. 1 second) are compiled into a text file, and the file is uploaded to the database instead. The text file contains 4 columns which represent the current data and the 3-axis vibration data. This file is updated after each predefined interval of data collection to store the data of next interval. In other words, there is a maximum data delay by the predefined interval when compared to real-time. In terms of manufacturing data, an RS232 serial adaptor cable is connected between the DB-25 gateway of the controller of the CNC machine and the USB port of the desktop computer to collect manufacturing data. For the Haas machine, the current collectable data include coolant level, tool number, cutter location, spindle speed and machine status (on/off and busy, idle or emergency stop). Due to the limitation of the communication speed between the PC and the controller using Python, such data can only be updated every 1-2 seconds. While this should be sufficient for slow-changing data like coolant level and machine status, it is for fast-changing data like cutter location and spindle speed. Nevertheless, the cutter location and spindle speed collected in low frequency still help to identify what operation the machine is conducting when associated with the G-code. Both the manufacturing data and sensor data are stored at a PostgreSQL database on a local PC connected to the internet. Users can get access to the data in the database through the internet with valid username and password.

(a)

(b)

Fig. 4. Test bed: (a) Haas VF-2 3-axis vertical milling machine and (b) its CAD model.

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(a)

(b)

Fig. 5. Embedded sensors: (a) Hall effect current sensor and (b) 3-axis accelerometer.

3.2. Machining data acquisition and machining features extraction Block size: 4 inch × 4 inch × 0.75 inch Material: 6060 aluminum alloy

ż4

Drilling (1/4 inch spot drill and 3/16, 1/4, 3/8 drill)

1 Face milling (2 1/4 inch face mill) 䕿

ż2

Shoulder milling (1/2 inch end mill)

ż3

Pocket milling (1/2 inch end mill)

ż5

ż6

Chamfering (1/4 inch drill mill)

Contour milling (1/4 inch drill mill)

Fig. 6. Test part with different operations on milling machine.

Fig. 7. A fabricated test part on the milling machine.

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To provide rich data to the virtual milling machine, a test part was designed which required a variety of machining operations, including face, shoulder and pocket milling, as well as chamfering and drilling. The details of the test part design and machining operations are shown in Fig. 6. Feasible G-code was generated and simulated with the SurfCAM software. Fig. 7 shows a fabricated test part with the Haas VF-2 milling machine. Fig. 8 shows the spindle current data measured by the Hall effect sensor for the test part, where the six operations in Fig. 6 are labeled, and Fig. 9 shows the acceleration data in the 3 axis during the fabrication process. The whole fabrication process took around 900 seconds. Even without advanced analysis, these four channels of data provided information of machine operation status and revealed some characteristics of the machine. Face milling

Shoulder milling

Drilling Contour Chamfering milling

Pocket milling

25 Enlargement

Spindle current (A)

20 15

Roughing Finishing Drill in

10 5

Conners

Tool path Tool path

0 27 54 81 108 135 162 189 216 243 270 296 323 350 377 404 431 458 485 512 539 566 593 620 647 674 701 728 755 782 809 836 863

0

Time (s) Fig. 8. Spindle current data of the test part.

1. There was a short pause at the early stage of shoulder milling due to coolant adjustment. This was also recorded in the manufacturing data from the machine controller as “Idle” status. 2. At the beginning and at the end of the usage of a specific cutting tool, the current could go to up to 100 Amp due to motor turning on and off. Based on such status of motor on/off, the different cutting operations described in Fig. 6 can be identified correspondingly as labeled from 1 to 6. 3. Among all the operations, face milling consumed the least power, while the middle stage of pocket milling and the drilling of the 3/8 inch whole consumed the most. 4. It is interesting to notice that for almost all the operations, the vibration amplitude in the X axis was greater than that in the Y axis, especially for shoulder milling and pocket milling. Although this may be related to the position where the accelerometer was mounted or the toolpath of the G-code, it was also possible that it was the characteristic of the VF-2 machine under test to have a relatively active X-axis. To discover other hidden characteristics of this machine, more analysis tools may be applied, such as frequency domain or frequency/time domain analysis. This will be a topic as more data are collected. 5. In the shoulder milling operation, roughing consumed more current than finishing, but the current increased at the four corners during finishing due to increased cutting load as shown in the tool path in Fig. 8.

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6. In the pocket milling operation, the end mill needed to drill in to reach the depth of the pocket first, and then enlarged the hole until eventually reaching the corners. These steps were reflected in the current data shown in Fig. 8. The drill in motion consumes least current, while the enlarge motion most. The vibration data followed a similar relation. Significant fluctuation was observed at the corners due to uneven cutting as in shoulder milling. 7. In the drilling operations, the increase in drill bit diameter resulted in increase in spindle current as well as its fluctuation, but the difference in vibration was not as significant as in current. The vibration in the Z direction was more significant than in X and Y direction due to the nature of drilling process. 2

Shoulder milling

Pocket milling

Chamfering Contour Drilling milling

1 0.5 0

-0.5

0 27 54 81 108 135 162 189 216 243 270 296 323 350 377 404 431 458 485 512 539 566 593 620 647 674 701 728 755 782 809 836 863

X-axis acceleration (g)

1.5

Face milling

-1

-1.5 -2

1.5

Face milling

Shoulder milling

Drilling

Chamfering Contour milling

1 0.5 0 -0.5 -1 -1.5 -2 2 1.5

Time (s) Face milling

Shoulder milling

Pocket milling

Drilling

Chamfering Contour milling

1 0.5 0

-0.5

0 27 54 81 108 135 162 189 216 243 270 296 323 350 377 404 431 458 485 512 539 566 593 620 647 674 701 728 755 782 809 836 863

Z-axis acceleration (g)

Pocket milling

0 27 54 81 108 135 162 189 216 243 270 296 323 350 377 404 431 458 485 512 539 566 593 620 647 674 701 728 755 782 809 836 863

Y -axis acceleration (g)

2

Time (s)

-1

-1.5 -2

Time (s) Fig. 9. 3-axis acceleration data of the test part.

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For the four channels of data above, if they can be related with manufacturing data such as spindle speed, feed rate and chip load, a series of characterization profiles of the VF-2 machine can be constructed. For example, given a spindle speed of 3000 rpm, a feed rate of 30 inch/min and a chip load of 0.01 inch, the average current of cutting 1060 aluminum alloy using a 1/4 inch end mill is 12.5 Amp with a fluctuation level of ± 0.3 Amp. In the future, when a new part is to be machined, the current consumption and 3-axis vibration features in these profiles can be extracted for the virtual machine to simulate the fabrication process on the virtual machine tool more accurately. 3.3. Machining feature extraction and inferencing In our earlier work presented in [27], a Particle Learning method has been developed for online tool wear diagnosis and prognosis. In this paper, we look into the analytical modeling of sensory data of cutting and modeling for the prediction of machined surface roughness. The example below demonstrates how manufacturing and sensor data can be used together to predict surface roughness in face-milling. Surface roughness is a widely used index of product quality and in most cases a technical requirement for mechanical products. Achieving the desired surface quality is of great importance for the functional behavior of a part. It is therefore desirable that a virtual machine can predict the surface roughness of a part before it is machined with the specific tool and parameters. The theoretical surface finish produced in the face-milling operating by an end-mill insert can be expressed as [24] ܴ௔_்௛௘௢௥௬ =

௙೟మ ଷଶ௥

(1)

where ݂௧ is the feed per tooth (mm/tooth) and ‫ ݎ‬is the nose radius of the end-mill insert (mm). The two parameters can be considered to be manufacturing data. The actual surface roughness is usually greater than the theoretical roughness value given by Eq.(1) due to factors including cutting parameters, tool/workpiece properties, machine conditions and machining environment [25]. A two-flute indexable milling tool with diameter 12.7 mm, Sandvik Coromill 390 (RA390-013O13-07L) installed with one uncoated insert, Sandvik Coromill 390 Insert (390R-070204E-NL) was used in the cutting process. The nose radius r of the insert is 0.4mm. The workpiece material utilized is Steel 4142 (cold rolled, 4045HRC). A total of 10 passes was conducted. Each single cutting pass was 37.0mm long, with the spindle speed at 500 rpm, leading to a surface speed of 19.81m/min and chip load at cutting ݂௧ to be 0.05mm. The radial depth of cut is 12.7 mm and axial depth at 0.5mm. Sensor data were sampled at 1652 kHz. After the 10 passes of cut along the length of the workpiece, offline direct measurements were taken to measure the surface roughness along the center of each pass using a Mitutoyo SJ-210 surface roughness measuring tester. The cut-off length was 0.8mm, and the measurement length was 5 times of the cut-off length. A linear model was used for the roughness prediction as ܴ௔_௉௥௘ௗ௜௖௧ = ܴ௔_்௛௘௢௥௬ + ‫ ݅ܣ‬+ ‫ܽܤ‬

(2)

where i is the average current (A) and ܽ is the average peak acceleration (g, gravitational acceleration) calculated by averaging the peak values of the acceleration in Z axis. The Z axis acceleration was used because it was found that the effects of the vertical direction vibrations on the surface roughness were much more significant than the other two [26]. Coefficients A and B are to be determined. It should be pointed out that the values of coefficient A and B are specific for the VF-2 machine being tested, which reflect the characteristics of this machine.

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ࡾࢇ (ࣆ࢓)

i(A)

a(g)

Passes

ࡾࢇ (ࣆ࢓)

i(A)

a(g)

1

0.201

13.933

0.1334

6

0.270

14.088

0.1920

2

0.249

13.949

0.1496

7

0.264

14.093

0.1958

3

0.241

13.958

0.1588

8

0.259

14.099

0.1930

4

0.227

13.983

0.1675

9

0.276

14.100

0.1959

5

0.260

13.985

0.1823

10

0.271

14.122

0.1975

Table 1 shows the measured surface roughness ܴ௔ and the collected data of i and a for the 10 passes. The progressive increases in ܴ௔ , i and a are mainly due wear of the cutting tool. By calculating ܴ௔_்௛௘௢௥௬ with ݂௧ and ‫ݎ‬ and using least-squares regression to determine A and B with the data in Table 1, the resultant equation is ܴ௔_௉௥௘ௗ௜௖௧ = 0.195 െ 0.00758݅ + 0.92241ܽ

(3)

To validate this model, an on-line prediction was conducted by machining the workpiece for 11th and 12th pass. The tool insert and cutting parameters remained the same as before. The measured values of i for these two passes were 14.150 A and 14.154 A respectively, and the values of a were 0.2055g and 0.2123g respectively. The predicted values of ܴ௔_௉௥௘ௗ௜௖௧ were 0.278 ߤ݉ for the 11th pass and 0.284 ߤ݉, while the measured values of ܴ௔ were 0.283 ߤ݉ and 0.288 ߤ݉ respectively. In addition to on-line prediction, off-line prediction before the actual cutting is also desirable in practice to make sure “right at the first time”. An off-line prediction was therefore conducted before the 11th and 12th pass were machined without actual current and vibration data. In order to obtain predicted values of i and a for Eq. (3), leastsquares regression was applied to the data in Table 1, and the resultant equations are ݅௉௥௘ௗ௜௖௧ = 0.0236ܰ + 13.901

(4)

and ܽ௉௥௘ௗ௜௖௧ = 0.0071ܰ + 0.1377

(5)

where N is the number of passes. By putting N =11 and 12 into Eq.(4) and (5), predicted values of i and a were obtained for these two passes, which were further put into Eq.(3) to get the predicted values of ܴ௔ . Table 2 summaries the results of on-line and off-line prediction. It is interesting to observe that the predicted values of i and a obtained from Eq.(4) and (5) tended to over-predict due to the linear fitting, resulting in predicted ܴ௔ values greater than measured ones. It should be pointed out that if further prediction is required for more passes, the data for the 11th and 12th pass should be used to determine new values for the coefficients in Eq.(3) to keep it up-to-date. Table 2. Summary of on-line and off-line prediction On-line prediction

Off-line prediction Passes

Predicted

Predicted

ܴ௔ (ߤ݉)

Measured ܴ௔ (ߤ݉)

i(A) and a(g)

ܴ௔ (ߤ݉)

0.278

0.283

11

14.161, 0.2158

0.287

0.283

0.284

0.288

12

14.184, 0.2229

0.293

0.288

Passes

Measured

Predicted

i(A) and a(g) 11

14.150, 0.2055

12

14.154, 0.2123

Measured ܴ௔ (ߤ݉)

A limitation in the example presented above is that the cutting parameters, i.e. depth of cut, feed rate and cutting speed, remained the same for all the passes. In order to make more accurate prediction, cutting parameters, tool/workpiece properties, machine conditions and machining environment should be considered together with the

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data collected from sensors. In this case, artificial intelligence (AI), which includes but is not limited to artificial neural network (ANN), genetic algorithms (GAs), fuzzy logic and expert systems, will be a powerful tool to handle these large amount of and somehow interdependent parameters. 4. Discussions and future works At the current stage of the work, only one milling machine tool and limited number of machining sensory data sets were implemented for the “digital-twins” virtual machine tool development. To fully explore the potentials of “digital-twins” virtual machine tools, we intend to further the study to include the following immediate tasks: 1. Currently, a Haas VF-2 vertical milling machine is used as the archetype of the virtual machine tool. It will be interesting to implement the data acquisition setup at multiple machine tools carrying a similar machining process for comparison. The same test part will be machined on multiple different machine tools with the same cutting tools (e.g. end mill and drill bits) using the same G-code for comparison. It is expected that the collected data will show different patterns, and we intend to identify the machine characteristics and differential one machine from another one. For example, the newly added milling may undergo less current fluctuation during the pocketing process, or its X-axis vibration is not as significant as the current Haas machine. These patterns will be used to characterize each machine to build its “digital-twins” and provide helpful information on classification of machine patterns. 2. Currently only two types of sensors, namely the Hall Effect current sensor and vibration sensor (accelerometer) are installed on the milling machine for testing. We intend to extend multiple sensors in experiments to identify key types of sensory data for building the “digital-twins” virtual machine tool. For example, a dynamometer can be applied to measure the cutting force, and an acoustic emission sensor can capture the radiation of acoustic waves in solids that occurs when a material undergoes irreversible changes in its internal structure resulting from crack formation or plastic deformation. 3. Currently, the collected manufacturing and sensor data are stored at a database on a local PC. However, using local PC has limited computation capability, and its storage space tends to be filled up with the large amount of data collected in kHz. High speed data acquisition will be developed for on-line data recording and uploading. To enhance the capability of the system, the data will be uploaded to cloud storage through internet for various purposes. This will allow accessibility to multiple remote users and to provide in-time monitoring and off-line review for related applications. 5. Conclusions This paper presents techniques of building “digital-twins” virtual machine tools by using sensory data integration and machining information fusion for cyber-physical manufacturing. The presented technique can be used to integrate manufacturing and sensor data into virtual machine tools to improve their accuracy and capabilities. Sensors are used to record key machining data and the analytical tools are used to extract the characteristics profiles of a specific virtual machine tool. Examples of a virtual 3-axis vertical milling machine with actual machining experimental data were presented. In the experiment, illustrative examples of data acquisition and machining feature extraction were demonstrated. Preliminarily experimental results showed that the collected data were helpful to monitor the operation condition and demonstrated useful manufacturing features and characteristics of the machine under test. The results also demonstrated the prediction capability of a surface roughness model built based on manufacturing and sensor data. Future research efforts will be focused on formatting machine characteristics profiles through machining sensory data fusion and enhancing data accessibility with cloud. The presented technique of using sensory data and information can be used as one of the key building blocks for the development of cyber-physic manufacturing.

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Acknowledgements This work was partially supported by the National Science Foundation (NSF) Grant (CMMI-1547105) and the DMDII Grant (#15-16-08) to North Carolina State University. Their support is greatly appreciated. We are grateful to the Digital Manufacturing Commons (DMC) team for the support and technical assistance in getting App deployed and running. Technical assistance from NCSU manufacturing staff (Dan Leonard) is also gratefully acknowledged. References [1] A. A. Kadir, X. Xu and E. Hämmerle, Virtual machine tools and virtual machining—a technological review. Robotics and ComputerIntegrated Manufacturing, 27(3), (2011) 494-508. [2] J. Tichon and R. Burgess-Limerick, A review of virtual reality as a medium for safety related training in mining. Journal of Health & Safety Research & Practice, 3(1), (2011) 33-40. [3] B. Huang, C. Li, C. Yin and X. Zhao, Cloud manufacturing service platform for small- and medium-sized enterprises. 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