CHINESE JOURNAL OF MECHANICAL ENGINEERING Vol. 28,aNo. 6,a2015

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DOI: 10.3901/CJME.2015.1010.119, available online at www.springerlink.com; www.cjmenet.com; www.cjme.com.cn

Shared and Service-oriented CNC Machining System for Intelligent Manufacturing Process

LI Yao1, 2, LIU Qiang1, 2, *, TONG Ronglei1, 2, and CUI Xiaohong1, 2

1 School of Mechanical Engineering, Beihang University, Beijing 100191, China 2 Beijing Engineering Technological Research Center of High-efficient & Green CNC Machining Process and Equipment,

Beihang University, Beijing 100191, China

Received October 15, 2014; revised June 9, 2015; accepted October 10, 2015

Abstract: To improve efficiency, reduce cost, ensure quality effectively, researchers on CNC machining have focused on virtual

machine tool, cloud manufacturing, wireless manufacturing. However, low level of information shared among different systems is a

common disadvantage. In this paper, a machining database with data evaluation module is set up to ensure integrity and update. An

online monitoring system based on internet of things and multi-sensors “feel” a variety of signal features to “percept” the state in CNC

machining process. A high efficiency and green machining parameters optimization system “execute” service-oriented manufacturing,

intelligent manufacturing and green manufacturing. The intelligent CNC machining system is applied in production. CNC machining

database effectively shares and manages process data among different systems. The prediction accuracy of online monitoring system is

up to 98.8% by acquiring acceleration and noise in real time. High efficiency and green machining parameters optimization system

optimizes the original processing parameters, and the calculation indicates that optimized processing parameters not only improve

production efficiency, but also reduce carbon emissions. The application proves that the shared and service-oriented CNC machining

system is reliable and effective. This research presents a shared and service-oriented CNC machining system for intelligent

manufacturing process.

Keywords: intelligent, service-oriented, shared, database, monitor, optimization, evaluation

1 Introduction

Since the first CNC machine tool born in 1952, CNC machining technology is innovating with modern science and technology. To adapt flexible product market and high-efficient &green machining requirements, intelligent CNC machining technology is an effective way to improve efficiency, reduce cost and ensure quality[1]. Enhancing degree of information for manufacturing process and sharing manufacturing resources in network environment provides a high value-added, low-cost and global service-oriented manufacture module[2].

Given the important role of advanced manufacturing system in manufacture industry, America, Japan, Germany and China put forward to researching advanced manufacturing system successively in the past 30 years, and raise it as a national strategy[3]. As an important component of advanced manufacturing technology, CNC

* Corresponding author. E-mail: qliusmea@buaa.edu.cn Supported by National Defense Basic Scientific Research of China

(Grant No. A2120110002), National Science Foundation of China (Grant No. 11290144), and Major National Science and Technology Special Project of China (Grant Nos. 2010ZX04014-052, 2010ZX0414-017)

© Chinese Mechanical Engineering Society and Springer-Verlag Berlin Heidelberg 2015

machining is a symbol to measure the development of manufacturing industry.

Only relying on low level of information in traditional manufacturing process, it is difficult to deal with challenges of market globalization[4]. In order to realize intelligent manufacturing, many scholars put forward to a variety of manufacturing model, such as agile manufacturing system, fractal manufacturing system, integrated manufacturing system, and so on. These systems make full use of computer promote manufacturing ability. However, how to share data in network environment and transform the concept of “product-oriented manufacture” into “service-oriented manufacture”, the models above have several technical bottlenecks: (1) data in systems above are mostly unshared among different platforms; (2) In optimal allocation of resources and service, systems above reflect the concepts of “focus on the use of distributed resource”, however, they cannot achieve the purpose of “focus on the management of distributed data”.

Meanwhile, information technology with computer and internet brings revolutionary influence on manufacturing. Manufacture based on knowledge and information is an important development direction. Ref. [5] discussed manufacturing mode based on internet of things. Its structure and key technology was researched. Ref. [6] put

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forward to a concept of intelligent factory. By internet of things and online monitoring, it improved information management and service for manufacturing system. Ref. [7] presented a wireless manufacturing system based on wireless sensors and internet of things. Ref. [8] mainly presented that Germany government implemented “industry 4.0 project” to maintain its international competitiveness. The project combined traditional manufacturing technology with ubiquitous internet technology. Finally, an intelligent manufacturing system can be achieved. Ref. [9] overviewed current virtual machine tool technology and its new development. Ref. [10] researched the interaction between different parts of machining system. It pointed out that analysis based on dynamic characteristic promoted machining precision and efficiency. Ref. [11] discussed distributed intelligent process planning system based on online monitoring technology for cloud manufacturing. Ref. [12] putted forward to cloud manufacturing technology to transform “product-oriented” manufacturing into “service-oriented” manufacturing. Ref. [13] researched the application of internet of things in cloud manufacturing in which physical resources are divided into four levers.

Subject to resource and environment, green and intelligent manufacturing is the only way to improve manufacture[14]. Service-oriented CNC machining system is a new mode for CNC machining. The system has ability to realize value-added in value chain of machining process, which solved contradiction between environment and efficiency[15]. Intelligent manufacture is an effective way to transform machining system from energy driven into information driven. Intelligent manufacturing improved the ability of production operation and quality control[16].

In the paper, an intelligent CNC machining system has been established. Dynamic information perception and information optimization has been achieved with internet of thins as support and database as shared platform. The first section presents the composition of service-oriented and intelligent CNC machining system. The second section presents a shared CNC machining database. The third section presents realization of online monitoring for machining process. The fourth section presents high efficiency and green parameters optimization technology. The fifth section presents an application of intelligent CNC machining system. The last section is conclusions.

2 Service-oriented Intelligent CNC

Machining System CNC machining is a complex process coupling with

force and motion. Hence, there are several factors influencing machined surface quality, such as state of machine tool, process dynamic, cutting parameters. Because a shared data platform is lack, machining data chain is separated in traditional process, and most product design information has been lost in product processing.

Traditional CNC machining process is shown in Fig. 1. In order to schedule CNC machining process tasks more reasonably, internet of things has been introduced to compose a modern manufacturing system by network devices. The key technologies referred include shared CNC machining database, online monitoring, machining parameters optimization, and so on.

Fig. 1. Traditional CNC machining process

At present, due to lack of comprehensive and practical CNC database, machining factories often select parameters based on experience. Then, these parameters are adjusted by trial cutting. Hence, it is not only time-consuming but also resources-wasting. When new machines or tools are adopted, empirical parameters are seriously inclined to influence machining precision and efficiency. A service-oriented CNC machining system based on intelligence and network technologies can solve some problems, such as long processing cycle, unreasonable parameters selection, inconvenient storage, query of machining parameters, and so on. Meanwhile, the service-oriented machining system can percept and evaluate processing characteristic information. A shared CNC machining database ensures that manufacturing information is not lost from design to processing. Application systems, such as simulation system and multi-sensors on-line monitoring system can transform machining physical model into digital information. Finally, Cyber physical system can be achieved by network and computer. Fig. 2 shows contraction between traditional and service-oriented CNC machining.

2.1 Service-oriented and shared CNC machining

database 2.1.1 Design for CNC machining database

CNC machining database is a basic data platform for implementing intelligent CNC machining. The system can ensure convenient collection and safe storage for CNC machining parameters. Machining parameters in the service-oriented CNC machining database comprehensively coverage all aspects of CNC machining process. Its structure is shown in Fig. 3.

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Fig. 2. Contraction between traditional and service-oriented CNC machining

Fig. 3. Structure of function and interface of the database

2.1.2 Realization for CNC machining database CNC machining database system consists of database,

application program, database management system, administrator system and computer system[17]. Database is the storage for cutting parameters by computer. In order to coverage all aspects of CNC machining process, the system is divided into machine tool sub-database, cutter tool sub-database, cutting fluid sub-database, process parameters sub-database and typical part process sub-database. By means of relationship database based on conceptual model, each data item has been constrained to form physical data model. The interface of CNC machining parameters database based on relational data model is shown in Fig. 4.

2.1.3 Evaluation model for CNC cutting parameters

system Because resources of cutting data are diverse, it is

necessary for machining parameters to test the authenticity, suitability, reliability. Then, the tested parameters are stored hierarchically into database. According to source of data, membership degree of data, satisfaction of user, there are 5

levers to evaluate cutting data from subjective and objective aspects. Membership degree is determined by five sub-division evaluation indexes, which are machining stability, surface roughness, tool life, material removal rate and energy consumption. The best cutting parameters are the 5th lever. The worst ones are the 1st lever. Subjective aspect is user evaluation after parameters used, and objective aspect uses fuzzy comprehensive method to intelligently score machining parameters. The structure of three-lever evaluation system is shown in Fig. 5. Evaluated machining parameters can increase machining stability and reduce damage for machine tools and cutter tools.

Fig. 4. Interface of CNC machining parameters database

Fig. 5. Structure of three-lever evaluation system

Based on reasonable physical structure model, a CNC machining parameters database system with friendly interface has been developed. To provide more intuitive cutting parameters information for users, the system integrates three-lever evaluation system.

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Database technology is developed from the first generation of hierarchical database model to the second generation of relationship database model, then to the object-oriented database model. Modern database technology is combined with intelligence and fuzzy control. On the other hand, data are transformed among different platforms in network environment. In order to server CNC machining well, cutting data can be shared by Active MQ bus technology. Based on the above work, service-oriented CNC machining database system has been established including three data processing systems. They are online monitoring system, machining dynamic identification and simulation system, machining parameters evaluation and optimization system. Supported by internet of things and computer, a shared and service-oriented CNC machining system for intelligent manufacturing is formed shown in Fig. 6.

Fig. 6. A shared and service-oriented CNC machining system

for intelligent manufacturing process

2.2 Online monitoring system for CNC machining

2.2.1 Online monitoring technology and configuration structure

CNC machining stability is an effective way to guarantee cutting quality, improve processing efficiency and prolong life for machine tool. Chatter is an important factor to influence high speed machining stability. Chatter is produced from self-excited vibration mechanism on chip formation. Initially a mode of machining system is inspired by cutting force. Then, chatter happens close but not equal to natural frequency of machining system[18]. Although chip formation in machining process is complex, chatter can be monitored effectively based on computer and sensor technology[19]. Vibration information in machining process is the feedback to machining parameters optimization with network assistant.

CNC machining monitoring system uses multi-sensors to collect noise, acceleration, current signals. The system overcomes some disadvantages that single type signal cannot effectively reflect characteristics for CNC

machining [20]. Firstly, noise and acceleration signals are de-noised as preprocessor. Then, characteristic value is extracted by wavelet packet transform (WPT) and HHT from two aspects, which are energy frequency distribution and probability distribution. Finally, vibration diagnosis model has been established by fuzzy support vector machine (FSVM), as shown in Fig. 7. On the other hand, current of spindle and feed are monitored to forecast overload fault for CNC machining.

Fig. 7. Online monitoring system for CNC machining

Hardware composition of online monitoring system for CNC machining and its structure are shown in Fig. 8. To reduce influence on data acquisition instrument and ensure the safety of CNC machining equipment, communication between signal and computer adopts wireless network technology.

Fig. 8. Online monitoring system for CNC machining

2.2.2 Realization of online monitoring system Noise and acceleration signals acquisition and analysis

system for CNC machining is shown in Fig. 9. Noise is one channel signal, and acceleration is three channels signal including X/Y/Z directions. The signals are collected and analyzed to monitor vibration for CNC machining process.

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To avoid influence on machining accuracy and damage to machine tools and cutter tools, machining parameters can be modified timely and automatically. Characteristic value is extracted by WPD and HHT. Finally, online monitoring system for CNC machining vibration has been established by FSVM. Wavelet packet decomposition is 3, then 8 entropies can be obtained in one segment signal. Thus, different vibration modes can be identified effectively (stable cutting, weak chatter, chatter, tool-worn cutting). HHT is divided into two steps including empirical mode decomposition (EMD) and Hilbert transform. Instantaneous frequency and amplitude by HHT reflect time-frequency characteristic of non-stable signals[22–23]. The mean μ and variance of time-frequency characteristic curve are extracted as characteristic value[24]. Based on statistical theory, FSVM is a learning machine method overcoming the shortcomings of neural network learning, which needs a large number of samples. Hence, the model based on FSVM can diagnose vibration effectively based on small samples[25].

Fig. 9. Noise and acceleration signal acquisition and analysis

system for CNC machining

2.3 CNC machining parameters optimization system High-efficiency and green CNC machining parameters

optimization technology for service-oriented manufacture is an effective way to solve the contradiction between friendly environment and high quality[26]. Under the background above, machining parameters optimization system has been developed, which integrates cutting force simulation, chatter stability prediction and cutting parameters optimization. By CNC machining database, machining data can be shared among CNC machining online monitoring system by network technology. Finally, the optimization system can improve machining efficiency and reduce carbon emission.

2.3.1 Optimization objective function and constraint

Multi-pass machining parameters are optimized from two aspects of green and high-efficient directions. Optimization goal is profile and carbon emission in machining process. The profile takes account of efficiency, time, cost and quality. Thus, complex optimization problems can be uncoupled. Based on the highest profile to satisfy the demand of green and high-efficient cutting,

carbon emission is optimized as follows. (1) Profile objective function for machining process. Single-pass means machining one feature by the same

machine tool, cut tool and machining parameters continuously. No matter how complex work-pieces are, machining processes can be divided into several single-pass processes in real machining. Hence, profile objective function of multi-pass machining is as bellow (k is the numbers of feed):

( )1 1

1,

k k

j v j m h p T mrj j

je e e c tT

q e v= =

é ùê ú= ê úë

æ ö÷ç´ - + + +çè øû÷ç ÷å å (1)

where ev is volume value coefficient, v(j)r is volume remove rate, eh is labor cost rate, ep is power cost rate, cT is cut tool cost rate for each flutter, tmj is cost time for one feed, T is tool lift.

(2) Carbon emission objective function for machining process.

Carbon emission in machining process is combined with material consumption carbon emission Cem, electrical energy consumption carbon emission Cee, machining carbon emission Cep (including tool carbon emission Cet, cutting fluid carbon emission Cec, scraps carbon emission Ces)

[27]. Material consumption carbon emission Cem is determined by process design. Scraps are handled together finally. On the other hand, method to optimize cutting fluid is dry cutting or minimum quantity lubrication. Hence, carbon emission optimization is concentrated on electrical energy consumption carbon emission Cee and tool carbon emission Cet. Carbon emission objective function based on instantaneous rigid mechanical model is shown as bellow:

20 1 2

1

{0.545 [( ) 1.2 ]

},

k

p u mj mj mjj

mjt t

C P A n A n t P t

tFW

T

=

= ´ + + + +å

(2)

where Pu0 is on-load power, Pmj is cutting power, Ftl is cut tool carbon emission coefficient, Wt is cutter tool weight.

According to optimization target and constraint conditions including machine tool performance, work-piece, cutter tool, process stability, machining parameters optimization is implemented. The process is shown in Fig. 10.

2.3.2 Machining process parameters simulation

and optimization system Machining process parameters simulation and

optimization system combines three modular, including cutting force simulation, chatter stability domain simulation and machining parameters optimization. Cutting force simulation and chatter stability domain simulation cannot only be used independently, but also provide constraints for machining parameters optimization. The process for machining parameters optimization is shown in Fig. 11.

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Fig. 10. Technology structure of high efficient and green optimization system for service-oriented manufacturing

Fig. 11. Process and example for machining parameters

optimization

3 Application

Based on shared and intelligent CNC machining system, CNC machining service platform has been developed. The system contains CNC machining database, dynamic test sub-system, CNC machining parameters optimization sub-system, chatter stability simulation sub-system and online monitoring sub-system, shown in Fig. 12.

Fig. 12. Application examples of CNC machining platform

3.1 Online monitoring system for machining test Vibration noise and acceleration signals collected are

decomposed by WPD and HHT to get energy spectrum entropy and time-frequency characteristic curve. The paper introduces noise decomposition as example (Fig. 13). Three-lever wavelet packet energy spectrum entropies reflect the features of energy distribution in frequency. The mean value μ and variance σ of time-frequency curve by HHT can be extracted as characteristics. These values constitute characteristic vectors for vibration signals.

Fig. 14 shows prediction accuracy by FSVM, according to real machining signals. The evaluation parameters are extracted from energy distribution in frequency domain and amplitude statistical distribution in time-frequency domain. Thus, it can overcome the subjectivity and arbitrariness for pattern identification. Finally, the prediction accuracy based on FSVM is up to 98.8%. The prediction result is totally correct at the state of normal milling and chatter milling. Prediction error occurs mainly on weak chatter milling and tool-worn milling. Mainly weak chatter is classified as stable milling, and tool-worn milling is classified as chatter milling.

3.2 Test for high-efficient and green milling

parameters optimization Machining parameters are optimized by high-efficient

target, green target, green and efficient multiple-target. In real machining, process is divided into rough machining, semi-finished machining, finished machining. The number of feed in test is k, (1) according to real situation, the number of feed is 1 for finished machining. Thickness is 1 mm to ensure the final accuracy of parts; (2) the number of feed is (k–2) for rough machining; (3) the number of feed is 1 for semi-finished machining. Its thickness is that total machining thickness minus the sum of rough thickness and finished thickness. Finally, results of parameters optimization are shown in Table 1. The total allowance of aluminum alloy parts is 50 mm, and its length is 1000 mm.

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Fig. 13. Result of machining vibration noise by WPD and HHT

Fig. 14. Result and accuracy of prediction

Table 1. Multi-pass machining parameters optimization

Optimization target

Machining thickness ap/mm

Feed number

fn

Spindle speed

n/(m • min–1)

Feed per tooth

fz/(mm • z–1)

Profile

P/yuan

Carbon emission

Ce/(gCO2)

Energy efficiency ratio

Er/(gCO2 • yuan–1)

Empirical machining

apr=5 8 3000 0.08

224.2 924.3 4.12 aps=4 1 3000 0.08 apf=1 1 4000 0.08

High efficient and green machining

apr=14.5 3 7045 0.09

282.9 305.5 1.08 aps=5.5 1 7045 0.09 apf=1 1 7150 0.09

High efficient machining

apr=16 3 7150 0.1

286.3 320.75 1.21 aps=1 1 7150 0.1 apf=1 1 7150 0.1

Green machining

apr=7.3 6 6950 0.08

247.4 292.5 1.18 aps=5.2 1 6950 0.08 apf=1 1 7150 0.08

Note: apr is the thickness for rough machining, aps is the thickness of semi-finished machining, aps is the thickness of finished machining.

Compared to empirical machining, energy efficiency ratio has been reduced significantly by optimized parameters. When high-efficient and green machining is the

multiple-target, energy efficiency ratio is the lowest as 1.08. So parameters optimization system meets manufacturing demands.

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4 Conclusions

A shared and service-oriented CNC machining database for intelligent manufacturing can improve machining efficiency, guarantee processing quality and reduce energy consumption. Supported by network technology, the machining parameters can be shared among different systems. The system is also open for further simulation and optimization modules. It is a demonstration and direction for intelligent manufacturing as further development. Hence, some conclusions can be achieved.

(1) Combining internet of things and multi-sensors, characteristic signals can be collected continuously to ensure the well state of CNC machine tool.

(2) A machining parameters optimization system for high efficient and green manufacture has been established. It provides value-added services for machining process. Finally, reasonable parameters optimization results can be achieved.

(3) A shared CNC machining database ensures the completeness and safety of parameters data. Hence, the concept of “decentralized data used intensively and shared data used dispersedly” has been achieved.

Some work needs to improve and research as bellow. (1) Enrich some more service functions. Methods to

improve the accuracy of online monitoring should be researched further.

(2) Improve standards for parameters evaluation. These standards provide basis to realize open CNC machining.

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Biographical notes LI Yao, born in 1987, is currently a PhD candidate at Beijing Engineering Technological Research Center of High-efficient & Green CNC Machining Process and Equipment, Beihang University, China. His research interests include virtual manufacturing technology, service-oriented and high-efficient and green manufacturing Tel: +86-10-82316298; E-mail: liyao123_1987@163.com

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LIU Qiang, born in 1963, is currently a professor at Beihang University, China. His research interests include simulation and optimization of CNC milling process, high performance servo motor digital control technology, network manufacturing. Tel: +86-10-82316699; E-mail: qliusmea@buaa.edu.cn

TONG Ronglei, born in 1989, is currently a master candidate at Beijing Engineering Technological Research Center of High-efficient & Green CNC Machining Process and Equipment, Beihang University, China. His research interests include

vibration signals collection and analysis. Tel: +86-10-82316298; E-mail: 314439243@qq.com CUI Xiaohong, born in 1987, is currently a master candidate at Beijing Engineering Technological Research Center of High-efficient & Green CNC Machining Process and Equipment, Beihang University, China. His research interests include cutting parameters evaluation. Tel: +86-10-82316298; E-mail: kybmcxh@163.com