Advanced Manufacturing Cloud (AMC)

Advanced Institute of Manufacturing for High-tech Innovations (AIM-HI)

AIM-HI Research Progress Report

(Third Year)

Advanced Manufacturing Cloud (AMC)

PI: Fan-Tien Cheng

Co-PIs: Min-Hsiung Hung

Haw-Ching Yang

Chao-Chun Chen

Rong-Shean Lee

Yung-Chou Kao

Chair Professor, Institute of Manufacturing Information and Systems, NCKU

2

Professor, Department of Computer Science and Information Engineering, PCCU

3

Assistant Professor, Institute of System Information and Control, NKFUST

4

Associate Professor, Institute of Manufacturing Information and Systems, NCKU

5

Distinguished Professor, Department of Mechanical Engineering, NCKU

6

Professor, Department of Mechanical Engineering, KUAS

Table of Contents

1. Research Project’s Background and Goals ... 2

1.1 Research Project’s Background ... 2

1.2 Research Project’s Goals ... 3

2. Research Achievement and Progress ... 4

2.2 MQM and PPP Services of AMC ... 7

2.3 Intelligent Ontology Inference Cloud Service of AMC ... 8

2.4 Virtual Machine Tool (VMT) Cloud Service of AMC ... 11

3. Future Research Scope and Directions ... 13

3.1 Cloud Computing Platform of AMC ... 13

3.2 KDP Service of AMC ... 14

3.3 Intelligent Ontology Inference Cloud Service of AMC ... 14

3.4 Virtual Machine Tool (VMT) Cloud Service of AMC ... 15

4. List of Important Papers or Patents (Selected up to 15)... 16

4.1 Journal Papers ... 16

4.2 Patents ... 17

5. Team members Vita (PI and Co-PIs) ... 18

5.1 PI ... 18

5.2 Co-PIs ... 19

1

Research Project’s Background and Goals

1.1

Research Project’s Background

By leveraging the applications of information, communication, and software, the domestic machine tool manufacturing industry in Taiwan is toward flexibility and intelligence and desires to gain global competitiveness. With the advancement of manufacturing technologies, machine tools are becoming more sophisticated and precise. Therefore, there is a need to perform total inspection on all workpieces. However, total inspection requires many measurement tools and cycle times. To solve the above-mentioned problem economically and efficiently, the technology of virtual metrology should be adopted. Moreover, because machine tools are often used in long-term operations, some of their components may become aged or broken, which will reduce the quality of processed products or workpieces. Therefore, creating diagnostics and prognostics capabilities, such as fault detection, manufacturing precision conjecture, and remaining useful life prediction, for machine tools to ensure their reliability and production quality has become an important topic for the industry. By using these intelligent capabilities, we can know certain failures of machine tools in advance and prevent them from occurring, which can save a significant amount of time and money in maintenance and increase the overall reliability and safety of products and operations. Thus, adding such kinds of intelligence to machine tools fits the trend towards smarter machines and manufacturing systems [1] and is a key factor for the domestic machine tool enterprises to increase their competiveness.

With the rapid development of information and network technology, cloud computing has become a new trend of Internet applications. According to the NIST’s definition [2], cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources, including networks, servers, storage, applications, and services, which can be rapidly provisioned and released with minimal management effort or service provider interaction. Cloud computing possesses the characteristics of rapid elasticity, on-demand self-service, ubiquitous network access, and resource pooling. Because dynamically scalable and virtualized resources are provided as services over the Internet in a pay-as-you-go manner, the manufacturing enterprises adopting cloud-computing technologies and services can not only save the expensive costs of creating and maintaining information infrastructure by themselves, but also are able to create new business models to effectively increase their business benefits.

In recent years, the concept of cloud manufacturing [3] has emerged in academia and industry as a more advanced application of cloud computing. Cloud manufacturing refers to encapsulating distributed manufacturing resources into cloud services that can support manufacturing activities, including product design, simulation, manufacturing, testing, management, and other tasks in a product life cycle, as well as equipment supporting activities. By leveraging the characteristics and benefits of cloud computing, cloud manufacturing could bring a new way for the domestic machine tool manufacturing enterprises to conduct their businesses for increasing competitiveness and gaining profits in the globalized competitive market. However, there has been no report on a developed cloud manufacturing system in literature so far [3].

1.2

Research Project’s Goals

The development trend of machine tools with intelligence is towards on-line statistics, intelligent monitoring and control, easy operation, and easy maintenance. To satisfy such a trend, this project aims at leveraging several technologies, including Automatic Virtual Metrology (AVM) [4][5], Ontology [6][7], Virtual Machine Tool [8], and Cloud Computing [9][10][11][12], to develop a cloud manufacturing platform, called AMC (Advanced Manufacturing Cloud), for providing various intelligent value-added manufacturing services for the domestic machine tool industry. The

architecture of the first-version AMC is shown in Fig. 1. We adopt this AMC architecture as the

blueprint to conduct this project. The AMC is expected to provide the following key services: Machine-Tool Quality Maintenance (MQM) Service, Product-Precision Prediction (PPP) Service, Key-Components Diagnosis and Prognosis (KDP) Service, and Virtual Machine Tool (VMT) Service, as well as the following supporting services: Data Acquisition Service, Model Creation Service, Model Management Service, and Machine-Tool Knowledge Ontology Inference Service.

Ontology

Advanced Manufacturing Cloud Services Machine Domain Knowledge

GDAD VMK GCI Local DB PAM Client Side Local Client Side Client Side Metrology Equipment X Process Machine A Sensors Variables Precisions Product v-Machine A IBMIBM v-Supplier in Cloud Metrology Equipment X Process Machine A Sensors Variables Precisions Product v-Machine B Service Broker

Local Web Server Cloud Web Server GDAD VMK GCI Local DB

PAM GCI: Generic Communication Interface VMK: Virtual Machine Kernel GDAD: Generic Data Acquisition Driver PAM: Pluggable Algorithm Module Local DB: Local Database

Factory Model Repository Data Acquisition DB Ontology DB Central DB v-Supplier:  Data Acquisition Service  Model Creation Service  Model Management Service  VMT Service Service Broker:  Message Dispatch  Security Management  Privilege Management  v-Machine Management v-Machine:  Premise Machine Tool

monitoring and control  Production Management  MQM Service  KDP Service  PPP Service Data Acquisition Model Creation Model Manag-ement Virtual Machine Service

Fig. 1. Architecture of Advanced Manufacturing Cloud (AMC).

The goals and niches of the AMC are summarized as follows.

 Provide a generic, Internet-accessible cloud platform that can host various intelligent

manufacturing services for machine tools.

 Provide scalable, abundant, on-demand, pay-by-use computing and storage resources for

machine tools’ intelligent services.

 Provide on-line machining-state feedback and prediction capabilities for the purpose of MQM,

PPP, KDP, and VMT.

 Provide knowledge inference functionality on the cloud for machine tools and cutting tools.

 Provide VMT services on the cloud for providing collision and cutting simulations.

2

Research Achievement and Progress

The research achievement and progress of this project so far include the following four parts: (1) the cloud computing service platform of AMC, (2) MQM (Machine-Tool Quality Maintenance) and PPP (Product-Precision Prediction) services of AMC, (3) the intelligent Ontology inference cloud service of AMC, (4) the VMT (Virtual Machine Tool) cloud service of AMC, and (5) live demos in international machine tool exhibitions. They are described sequentially below.

2.1

Cloud Computing Service Platform of AMC

Based the AMC architecture shown in Fig. 1, this project has evolved the AMC to a version that is based on hybrid cloud architecture, as shown in Fig. 2. Specifically, we base on a public cloud, namely Windows Azure, to create a cloud service platform [11] on which we can construct various intelligent cloud computing services. By using the AMC, the users can utilize Web-based GUIs over the Internet to collect factories’ machining-related data which is then stored into the cloud databases, create various prediction models on the cloud, download desired prediction models from the cloud to the target factory for conducting prediction applications for the machine tools, perform knowledge inference for machine tools, and carry out virtual machine tool simulations.

Cloud User Cloud User Model Repository Cloud Web Server

Local User Local User

Private Cloud Knowledge DB Virtual Machines VM Manager Local Web Server Virtual Equipment Knowledge Inference VM Server STDB Historical DB Storage Data Acquisition Model Creation Model Mgt. Ontology Inference Virtual Machine Tool

Advanced Manufacturing Cloud Services Machine Domain Knowledge

Data Collector 1 Data Collector n

Product

Product Metrology Equipment

Process Machine Metrology Equipment Process Machine

...

Fig. 2. Architecture of Advanced Manufacturing Cloud based on hybrid cloud.

In addition, considering that some companies may be concerned for the security issue of the public cloud, we leverage visualization technology to create a small-scale private cloud on the factory side. Then, we move the functional modules of the AMC that involve confidential data, such

as the VM servers and their associated databases, into the private cloud. Such a deployment can facilitate promoting the AMC to the machine tool industry. Current version of AMC can utilize abundant computing resources of the public cloud for carrying out the computation of intelligent services; meanwhile, it can leverage the private cloud to protect the factory’s confidential data, which conforms to the newest trend of cloud computing applications that most enterprises prefer adopting hybrid cloud.

We have developed a generic method for developers to create intelligent cloud services using MATLAB as shown in Fig. 3 [12]. Besides, the intelligent algorithms in Model Creation service are built using MATLAB, and MATLAB does not support multi-core or multi-threading programming in general. Consequently, if multiple users simultaneously access the Model Creation service, then only one user at a time can execute this service, and the rest of users need to wait until the service is available, leading to unsatisfying user experiences. To tackle this problem, we have designed the architecture of the Model Creation service as shown in Fig. 4 [12], which can effectively support multiple users’ simultaneous access. With such innovative architecture and scheme designs, the AMC can allow a great number of engineers who may be located in different machine-tool factories at different places to simultaneously create their own required prediction models on the cloud through the Internet.

2. Use MATLAB Deploytool to build .NET DLLs 1. Implement algorithms using MATLAB Algorithms Module files Algorithm DLLs 3. Develop intelligent functions by adding references of .NET DLLs in the program Local Development Phase Local Intelligent Functions 5. Deploy intelligent functions in the cloud

Cloud Development Phase 4. Upload MATLAB Complier Runtime (MCR) to cloud storage 6. Download and install MCR on virtual machines in the cloud

…

8. Provide cloud services to users through the Internet

Cloud Storage … Cloud Intelligent Functions 7. Communicate

Cloud Web Services

Fig. 3. Generic method for creating intelligent cloud services using MATLAB.

User Admin Fab Mgr

Worker VM Queue Storage MCS Kernel

…

…

Fetch Queue, Upload/Download Models, and Update Statuses

GUI

Table Blob

Also, because the public cloud adopts a pay-by-use charge policy, we have developed a resource auto-scaling mechanism (shown in Fig. 5) to enable the cloud service platform to automatically increase or reduce the leased cloud computing resources according to the number of users being accessing the cloud services. Thus, this mechanism can effectively reduce the operational cost of the cloud service platform.

SQL Azure & Storge Queue In Queue Out … Worker Role 1 with Computing Power User 1 User 2 v-Supplier in Cloud Data Acces s Da_{ta A} cc_es s User n … Worker Role n with Computing Power Worker Role 2 with Computing Power AutoScaler Web Role

AMC Cloud GUI

Web Role

Data Acquisition CreationModel

Model

Management Ontology

Virtual Machine Tool

Advanced Manufacturing Cloud Services

Web Role

Fig. 5. Architecture of the developed cloud-resources auto-scaling mechanism.

In order to verify the effectiveness of the AMC, we have deployed the cloud services and functions, together with the cloud database and storage, in Windows Azure, Microsoft’s public cloud platform, in Hong Kong. On the factory side, we have deployed v-Machines, a Service Broker, and a local Web server in a factory of the FALCON Machine Tools Corp., in Changhua, Taiwan. The client side can be anywhere having Internet access. The user can download the RIA-based Web GUIs from the cloud Web server and begin to operate the AMC. We have conducted thorough integrated tests on the AMC. Fig. 6 shows the performance of the AMC in supporting multiple users’ access to the Model-Creation service. As shown in the figure, without multi-user support in the Model Creation service, the model-creation times of the five testing cases are about proportional to the number of users. By contrast, when the multi-user support is activated, the model-creation times of the five testing cases are almost the same, about 300 seconds. These results validate the efficacy of the designed multi-user support mechanism by paying for more cloud computing resources as the number of users increases, leveraging the advantage of the pay-by-use property [12].

Fig. 6. The performance curves of the Model Creation service with/without activating multi-users support.

2.2

MQM and PPP Services of AMC

Another key feature of this project is that the Automatic Virtual Metrology (AVM) technology developed by our research team is used to create the prediction capabilities of the AMC. In the public cloud service platform, we adopt AVM technology to construct the Model Creation service, which allows the users to create various prediction models. On the other hand, in the private cloud, we utilize the AVM technology to create VM servers. After the user downloads a selected prediction model from the cloud to a target VM server, the VM server can then start to perform the corresponding real-time prediction application on the connected machine tool.

Current AMC has offered two types of prediction applications based on AVM technology to machine tools: machine-tool quality maintenance (MQM) and product precision prediction (PPP). Fig. 7 shows the virtual metrology scheme in the VM server for predicting product precision [14]. The VM scheme contains the following four parts: 1) data preprocess module for reducing noises of sensor data and extracting machining features, 2) data quality evaluation module for automatically evaluating the quality of feature data and measurement data, 3) key feature selection module for automatically selecting key features, and 4) precision prediction module for promptly computing the VM values of machining precision and the associated reliance index (RI) and global similarity index (GSI) [14].

Figure 7. The proposed virtual metrology system for predicting product precision.

Testing results of a 3-axis CNC machine center for machining standard workpieces show that the proposed VMS can conjecture dimensional derivations with MAE (Maximum Average Error) of less than 2 um in all testing scenarios and can complete the prediction of 20 geographic dimensions

within 3.8 sec. For example, the VM, RI, and GSI values of VMSEK are shown on the left hand side

of Fig. 8, where VMSEK refers to the proposed VMS adopting expert knowledge for feature

selection. The maximum error of the back-propagation neural network (BPNN) model is 2.73 um, occurring at the 12th testing sample. The maximum error of the PLS model is 2.54 um. The MAEs of both the BPNN and partial least square (PLS) models are less than 1.2 um. By contrast, the VM,

RI, and GSI values of VMSSS are shown on the right hand side of Fig. 8, where VMSSS stands for

the VMS adopting stepwise feature selection approach. Due to using stepwise feature selection, less 10 features were used for model creation. The testing results shows that the BPNN and PLS models

in VMSSS generate VM values with a MAE of less than 1um. The corresponding RI values were

very close to one, indicating the high reliance level of the VM values.

Conjecture Model Conjecture Model Reliance Evaluation Global Similarity Evaluation Only For

Training & Tuning Dual-Phase VM Algorithm Precision Prediction DQIX Z-Score Standardization DQIy Z-Score Standardization

Sensor & Process Data Evaluation Sensor & Process Data

Evaluation

Metrology Data Evaluation Metrology Data Evaluation

Data Preprocessing

Data Preprocessing Data Quality EvaluationData Quality Evaluation

Cleaning Extraction Association Automatic Feature Selection

Key Feature Selection ( ) ai P Sensor Data Process Data Real Measuring Data Virtual Metrology Data Operation-Precision Features Selection Operation-Precision Features Selection ( ) ai M MZa( )i ( )i Z a F ( )i a F ( )i z a K ( )i r a S ( )i c a S ( )i T a S ( )i I VM ( )i II VM ( )i RI ( )i GSI ( ) p m i ( ) Z ai M

Fig. 8. Comparison of prediction accuracy of a dimensional derivation using VMSEK and VMSSS.

Notably, the proposed VMS provides a Wavelet-based method for denoising signals to improve the S/N ratio of sensor data. Moreover, the VMS also provides an automatic feature selection method for extracting key features related to machining operations and reducing the number of essential features. By using the proposed VMS, the AMC can provide MQM and PPP services to machine tools, which could achieve the following merits: (1) providing machine tools with the capability of online workpiece-by-workpiece virtual metrology so that the time and cost of product inspection/measurement can be significantly reduced, in turn increasing the production yield [13][14]; and (2) providing the capability of automatic model tuning and retraining so that the model-creation time can be significantly reduced while maintaining the desired accuracy levels of the VM models [14].

2.3

Intelligent Ontology Inference Cloud Service of AMC

Ontology is an important technology that has been widely used to create and infer domain knowledge. We have created an Ontology Inference cloud service (OICS) in the public cloud platform, as shown in Fig. 9. The Ontology Inference service can allow the user to create domain knowledge for machine tools. It is also capable of recommending suitable machine tools according to the machining criterions set by the user. The research achievement of the OICS part is described in the following:

Fig. 9. Architecture of the Ontology Inference cloud service. (1) Auto-scaling Capability:

Fig. 10 illustrates the framework of the proposed Ontology Inference Cloud Service with Auto-scaling capability (OICS/AS). The CNC Application GUI is the interface for users to log in the OICS. Each user will be served by one worker which contains core functional modules of the OICS, including request filtering module, Ontology inference module, and VMT cloud service. The machine tool knowledge base includes the machine tool Ontology data and inference rule databases. In order to provide auto-scaling capability, the worker controller (WCR) is designed to periodically increase or decrease the number of workers based on the customized scaling algorithm. The details of OICS/AS can be referred to [15][16].

Worker 1 of

Ontology-Inference Cloud Service

Request Filtering Module VMT Cloud Service Ontology-Inference Module Machine Tool Knowledge Base CNC Application GUI Worker 2

...

Fig. 10. Framework of the Ontology Inference cloud service with auto-scaling capability (OICS/AS).

(2) Machine-tool knowledge base for recommending machine tools:

The development of the machine-tool knowledge base includes two stages. The first stage is to create the ontology data together with associated instances. Fig. 11 shows the class hierarchy of our machine-tool ontology knowledge which is implemented by using Protégé tool whose underlying data format obeys Web Ontology Language (OWL) [17]. The second stage is to develop inference rules, and the flowchart of the Ontology inference module is shown in Fig. 12.

Fig. 11. Visualizing the CNC knowledge data through the OWL-Viz plug-in in Protégé (only classes and instances are displayed).

Inference Rules Machine Tool Knowledge Base Specify Ontology Reasoning Engine Ontology Data Preprocessor

Specify Rules Inferred Machine Tools QP-2040 QP-2440 QP-2033 VMC-2443 Stage 2. Format Transformation Stage 3. Reasoning (SPARQL and SWRL Rules) Stage 1. Inputs

(Ontology and Rules)

Outputs (Inference Results)

Fig. 12. Processing flow of inference rules.

(3) Ontology Inference Module Supporting multiple users:

OICS/AS allows multiple users to use the Ontology inference service simultaneously, and the flowchart is shown in Fig. 13. Note that multiple users would incur wrong inference results if inferences or updates from different users are performed on the same Ontology data in the interleaving manner. Hence, the requirement of designing request process module is to force a worker to serve the same user until this user finishes his/her service request. The request processing module uses the locking mechanism with a variable, called lock, to exclude requests of other users during serving a user.

Retrieve request whose Progress step = Step 1 from TABLE-IN

Set progress step and lock: Step =1 AND Lock = UserID Retrieve request whose UserID = lock from TABLE-IN

Send the request to Ontology-Inference Module Wait and receive results from Ontology-Inference Module Is current step=LastStep?Is current step=LastStep? Reset lock: Lock = null Is Lock = null? Is Lock = null?

Lock≠ null Lock= null

Serve a new user Serve current user whose UserID=lock Yes No Execute Machine Tool Inference Unlock for serving next user Set progress step: Step = Step+1

(4) Building the worker controller and the scaling rule for supporting auto-scaling capability:

The worker controller (WCR) is designed to automatically scaling up and down the number of workers. Fig. 14 shows an example to design the WKR scaling rule where the auto-scaling algorithm mainly includes three parts: (1) scaling algorithm name and location of the external functions, (2) input parameters, and (3) output parameter. Note that the values of input parameters can be assigned by external functions maintained in the WKR scaling rule. In the OICS/AS, these external functions are developed in Java Web Service (JWS) so that the WCR can easily access them through HTTP/SOAP requests.

(1) Scaling algorithm name and location of statistic/monitoring component

(1) Scaling algorithm name and location of statistic/monitoring component

(2) Input parameters (2) Input parameters

(3) Output parameter (3) Output parameter

Fig. 14. Example of WKR scaling rule.

2.4

Virtual Machine Tool (VMT) Cloud Service of AMC

The research achievement for the VMT cloud service of AMC is described as follows:

(1) Demo of VMT Cloud Service in Machine Tool Exhibition:

In this year, we have successfully made two live demos of the VMT cloud service in international machine tool exhibitions held in Taiwan (TMTS 2012 and TIMTOS 2013), which includes the following three demo scenarios:

 Scenario 1: material removal, as shown in Fig. 15.

 Scenario 2: overcut, as shown in Fig. 16.

 Scenario 3: collision detection, as shown in Fig 17.

Fig. 17. Collision Detection. (2) Add remote control capabilities to the virtual machine tool:

 Create a server-client mechanism for remotely controlling virtual machine tools.

 Construct the functions of transferring native data stream to Java data stream for the collision

detection module and geometry removal module.

 Construct a real-time 3D remote display mechanism for virtual machine tools.

Because Java provides some APIs that are easy to be used for networking communication, the communication program of the remote control function in the VMT cloud service is implemented in Java. By contrast, for the sake of efficiency, the geometry removal module and collision detection module are implemented in C/C++. The native (C/C++) data stream cannot be transferred through Web by Java remote functions. Furthermore, the geometry data are too massive to be transferred through Web without any management. Thus, we write functions to transfer native data stream to Java data stream and create a mechanism to lower the transferred geometry data.

(3) Expand the functions of the virtual controller:

 Link the virtual controller with collision detection. When detecting a collision taking place in

the virtual machine tool, the virtual controller will stop the motion of the virtual machine tool. Then, the components in collision will change their color in red as shown in Fig. 18(a).

 Link the virtual controller with overcut comparison. When the workpiece model being cut is

not correct. Part of the model will change its color in red as shown inFig. 18(b).

(a) Snapshot of Collision Simulation (b) Snapshot of Cutting Simulation

Fig. 18. Snapshots of collision and cutting simulations based on the Virtual Machine Tool cloud service.

2.5

Live Demos in International Machine Tool Exhibitions

We have demonstrated the results of this project in two international machine tool exhibitions held in Taichung (TMTS 2012) and Taipei (TIMTOS 2013), respectively. The results of this project gained very good appraisals in the exhibitions, resulting in an academy-industry cooperation project being conducted by our team and a domestic machine tool company.

3

Future Research Scope and Directions

Based on current achievements, this project will continue to refine the functions and features of the AMC to provide advanced intelligent value-added services for domestic machine tool industry. The research scope and directions for the next year are planned as follows.

3.1

Cloud Computing Platform of AMC

Considering that enterprises may be concerned for the security issue of the public cloud, we will develop a new-generation AMC fully based on private cloud for promoting the AMC to the industry. Fig. 19 is the illustration of the future private cloud-based AMC, which only shows AVM-related components.

VM Client

Data Collector 1 Data Collector n

Product Product Metrology Equipment 1 Sensors _Precisions Process Machine 1 Metrology Equipment n Process Machine n Sensors Precisions

Storage _{Private Cloud}

VM Manager VM Server 1 ... VM Server n MC Server Virutal Machine Administrator Standard DB Central DB A.2

Fig. 19. Illustration of the future private cloud-based AMC, which only shows AVM-related components.

The followings are the expected tasks involved in developing the cloud computing platform for the private cloud-based AMC:

 Scale up the private cloud environment: We will scale up the current private cloud environment by adding in more high-end physical servers, more physical RAMs and storages, and required physical networking devices.

 Enhance the virtualization environment: We will adopt the newest version of virtualization software VMware vSphere and vCenter to create virtual machines and storages on top of hardware. Then, we will exploit vCloud to build more advanced cloud functions.

 Deploy servers and databases of AMC: We will deploy the essential servers and services (including VM servers, VM manager, model creation server, VMT service, and OI service), as well as the required databases and storages of AMC to the virtual machines and virtual storages, respectively.

 Establish advanced capabilities for the private cloud:We will develop advanced capabilities for the private cloud, such as load balance scheme, failover mechanism, resource auto-scaling mechanism, and mechanism for supporting multi-users to access cloud services simultaneously.

 Conduct thorough integrated tests on the private cloud-based AMC: We will apply the private cloud-based AMC to machine tools and conduct thorough integrated tests to evaluate and validate the performance and robustness of the new-version AMC.

3.2

KDP Service of AMC

We will adopt the VM-based Baseline Predictive Maintenance (BPM) technology to develop the capabilities of fault diagnosis and predictive maintenance of the machine tool’s key components, i.e. the KDP service. The required functions and tasks of the AVM-based KDP service are highlighted as follows:

 Provide models for key-component prognosis, under the goal of using a minimum of operating

features and diagnosis models to serve different types of machine tools.

 Provide various diagnosis models with reliance indices according to key components of machine

construction and operating histories, under the goal of using a minimum of machine models to serve a maximum of customers.

 Update running diagnosis models based on machine’s states, while maintaining machining

accuracy of machine tools.

3.3

Intelligent Ontology Inference Cloud Service of AMC

We will create cutting tool evaluation functions, as well as develop the machine tool recommendation service and cutting tool evaluation service in the private cloud. Also, we will develop the automation tool for the integration between the Ontology inference service and the VMT service. The future research directions of this part are summarized as follows.

 Design of cutting tool knowledge base for machine tool with twin cutting tools: We will construct cutting-tool ontology for complex-structure cutting tools. We will interview the experts in the machine-tool industry and collect the related domain knowledge and then extract ontology elements (classes, properties, instances) as well as organize the class hierarchy from the collected knowledge. We will generate the corresponding Ontology knowledge in the standard RDFS format and such knowledge base can be manipulated by popular knowledge base packages, e.g., Protégé. The generated Ontology knowledge can be used in related cloud inference systems.

 Design of Ontology inference system for recommending cutting tools: We will develop the Ontology inference system for recommending complex-structure cutting tools. Traditional simulation packages could incur collision during manufacturing because they do not consider complex-structure cutting tool scenarios. Hence, the mission of our developed Ontology inference system is to assist engineers of product design department and manufacturing department to find out suitable cutting tools for a given workpiece file, so that the maintenance cost can be saved.

 Design of automatic module deployment (AMD) for VMT cloud service: Continuing our achievement of VMT cloud service, we will design the automatic module deployment (AMD) service for VMT cloud service so that the collaborative design between our team and VMT team can be proceeded in an automation manner. The AMD service will generate necessary package-encapsulating function (PEF) by parsing the sources of VMT packages, and then automatically deploy the generated web service to the cloud environment together with the standalone VMT package.

3.4

Virtual Machine Tool (VMT) Cloud Service of AMC

The research scope and directions for the VMT cloud service of AMC in the fourth year are planned as follows:

 Adjust geometry removal module and collision detection module:

 Increase precision of geometry removal module.

 Improve efficiency of collision detection module.

The original geometry removal module used octrees algorithm. Voxel will become a surface that can achieve higher accuracy and presents a more complete image. In the collision detection module, it also implements the algorithm for the octrees composite B-rep. According to the different distance, changing their way of using judgment can effectively enhance the efficiency of operations.

 Expand the functions of the virtual controller:

 Link the virtual controller with cutter location display.

 Link the virtual controller with processing time estimation.

Tool path display and processing time estimation of the controller functions will be added. In the simulation of the machining process, tool path display can verify the correctness of NC files. Processing time estimation will be displayed for the user. The developed modules will allow the user to decide whether to modify or adjust the process sequences to shorten the processing time.

4

List of Important Papers or Patents (Selected up to 15)

4.1

Papers

1. F.-T. Cheng, H.-C. Huang, and C.-A. Kao, "Developing an Automatic Virtual Metrology

System," IEEE Transactions on Automation Science and Engineering, vol. 9, no. 1, pp.181-188,

January 2012.

2. W.-M. Wu, F.-T. Cheng, and F.-W. Kong, “Dynamic-Moving-Window Scheme for

Virtual-Metrology Model Refreshing,” IEEE Transactions on Semiconductor Manufacturing,

vol. 25, no. 2, pp. 238-246, May 2012.

3. M.-H. Hung, W.-H. Tsai, H.-C. Yang, Y.-J. Kao, and F.-T. Cheng, “A Novel Automatic Virtual

Metrology System Architecture for TFT-LCD Industry based on Main Memory Database,”

Robotics and Computer-Integrated Manufacturing, vol. 28, no. 4, pp. 559-568, August 2012.

4. M.-H. Hung, C.-F. Chen, H.-C. Huang, H.-C. Yang, and F.-T. Cheng, “Development of an

AVM System Implementation Framework,” IEEE Transactions on Semiconductor

Manufacturing, vol. 25, no. 4, pp. 598-613, November 2012.

5. C.-A. Kao, F.-T. Cheng, W.-M. Wu, F.-W. Kong, and H.-H. Huang, "Run-to-Run Control

Utilizing Virtual Metrology with Reliance Index," IEEE Transactions on Semiconductor

Manufacturing, vol. 26, no. 1, pp. 69-81, February 2013.

6. Y.-S. Hsieh, F.-T. Cheng, H.-C. Huang, C.-R. Wang, S.-C. Wang, andH.-C. Yang, “VM-based

Baseline Predictive Maintenance Scheme,” IEEE Transactions on Semiconductor

Manufacturing, vol. 26, no. 1, pp. 132-144, February 2013.

7. F.-T. Cheng and Y.-C. Chiu, "Applying the Automatic Virtual Metrology System to Obtain

Tube-to-Tube Control in a PECVD Tool," IIE Transactions, vol. 45, no. 6, pp. 670-681, June

2013.

8. C.-C. Chen and D.-C. Mao, “PPZTEM: An Efficient Approximate Trajectory Extraction

Method with Error Bound Constraint for Wireless Sensor Networks,” Computer

Communications (COMCOM), vol. 35, Issue 8, pp. 952–969, May 2012.

9. R.-S. Lee, K.-J. Mei, C.-M. Wu, “Feed Rate Optimization Based on Milling Force Evaluation as

a CAPP Supporting System in Five-Axis Virtual Machine Tool,” in proc. of International

Academy for Production Engineering 63rd General Assembly-Copenhagen -DK, August 18- 24,

2013.

10.Y.-C. Kao, “A Reconfigurable Interactive 3D Five-axis Machine Tool Virtual Machining

Process Operation Training and Learning System,” in proc. of International Academy for

Production Engineering 63rd General Assembly-Copenhagen -DK, August 18- 24, 2013.

11.H. Tieng, H.-C. Yang, M.-H. Hung, and F.-T. Cheng, “A Novel Virtual Metrology Scheme for

Predicting Machining Precision of Machine Tools,” in Proc. of the IEEE International

Conference on Robotics and Automation, Karlsruhe, Germany, May 7-10, 2013. [Best

4.2

Patents

1. “Dual-Phase Virtual Metrology Method;” Fan-Tien Cheng, Hsien-Cheng Huang, Chi-An Kao;

R.O.C. Patent No.: I338916, Patent Period: 2011.03.11~2027.06.07; U.S.A. Patent No.: 7,603,328 B2, Patent Period: 2009.10.13~2027.07.18; Japan Patent No.: 4584295, Patent

Period: 2007.10.15~2027.10.15; China Patent No.: 823284, Patent Period:

2007.06.08~2027.06.08; and Korea Patent No.: 10-0915339, Patent Period:

2009.08.27~2027.09.12.

2. “System and Method for Automatic Virtual Metrology;” Fan-Tien Cheng, Hsien-Cheng Huang,

Yi-Ting Huang, Jia-Mau Jian; R.O.C. Patent No.: I349867, Patent Period:

2011.10.01~2028.05.19; U.S.A. Patent No.: 8,095,484 B2, Patent Period:

2012.01.10~2032.01.10; Japan Patent No.: 4914457, Patent Period: 2009.01.27~2029.01.27

[Certificated on 2012.01.17]; China Patent No.: 843932, Patent Period:

2008.06.05~2028.06.05 [Certificated on 2011.07.02]; Korea Patent No.: 10-1098037, Patent Period: 2008.12.30~2028.12.30 [Certificated on 2011.11.30].

3. “Product Quality Fault Detection Method and Real Metrology Data Evaluation Method;”

Fan-Tien Cheng, Yi-Ting Huang, Fu-Jian Chang; R.O.C. Patent No.: I400619; Patent Period: 2013.07.01~2028.11.25.

4. “Advanced Process Control System and Method Utilizing Virtual Metrology with Reliance

Index;” Fan-Tien Cheng, Chi-An Kao, Wei-Min Wu; Japan Patent No.: 5292602, Patent

Period: 2011.08.01~2031.08.01; China Patent No.: 1205265, Patent Period:

2011.08.01~2031.08.01; with R.O.C. Patent Application No.: 100126401, Application Date: 2011.07.26; U.S.A. Patent Application No.: 13/193,607, Application Date: 2011.07.29; Korea Patent Application No.: 10-2011-0076666, Application Date: 2011.08.01; under pending.

5. “Method for Screening Samples for Building Prediction Model and Computer Program

Product Thereof;” Fan-Tien Cheng, Wei-Min Wu; R.O.C. Patent Application No.: 100147447, Application Date: 2011.12.20; U.S.A. Patent Application No.: 13/667,039, Application Date: 2012.11.02; Japan Patent Application No.: 2012-265665, Application Date: 2012.12.04; China Patent Application No.: 201210453644.7, Application Date: 2012.11.13; and Korea Patent Application No.: 10-2012-0143563, Application Date: 2012.12.11; all under pending.

6. “Baseline Predictive Maintenance Method for Target Device and Computer Program Product

Thereof;” Fan-Tien Cheng, Yao-Sheng Hsieh, Chung-Ren Wang, Saint-Chi Wang; R.O.C. Patent Application No.: 101126242, Application Date: 2012.07.20; U.S.A. Patent Application No.: 13/845,144, Application Date: 2013.03.18; European Union Patent Application No.: EP13168089.4, Application Date: 2013.05.16; Japan Patent Application No.: 2013-125444, Application Date: 2013.06.14; China Patent Application No.: 201310181746.2, Application Date: 2013.05.16; and Korea Patent Application No.: 10-2013-0061112, Application Date: 2013.05.29; all under pending.

7. “Method for Predicting Machine Quality of Machine Tool;” Haw-Ching Yang, Hao Tieng,

Min-Hsiung Hung, Fan-Tien Cheng; R.O.C. Patent Application No.: 102139801, Application Date: 2013.11.01; U.S.A. Patent Application No. 14/069,382, Application Date: 2013.11.01; both under pending; with China Patent Application about to be issued.

5

Team members Vita (PI and Co-PIs)

5.1

PI



Fan-Tien Cheng

Fan-Tien Cheng received his B.S. degree from National Cheng Kung University (NCKU), Taiwan, ROC (1976), his Master's (1982) and Ph.D. (1989) degrees from The Ohio State University, USA, all in Electrical Engineering.

Dr. Cheng is, currently, Chair Professor of NCKU. He served as the Director of the Institute of Manufacturing Engineering (IME), NCKU (Aug. 1998 -- July 2001.) He built a web-enabled experimental Manufacturing Execution System and a Supply Chain Information System for IC Packaging. He also established test beds for e-Diagnostics, Equipment-Engineering-System, Engineering-Chain-Management-System, and Automatic-Virtual-Metrology frameworks for semiconductor manufacturing at IME Automation Laboratory for educational and research purposes. His research interests include Semiconductor Manufacturing Automation, e-Manufacturing, Virtual Metrology, and Intelligent Predictive Maintenance.

Professor Cheng received the Senior Scientist Award from the DoD, ROC (1994). He won the Kayamori Best Automation Paper Award at IEEE ICRA 1999. He also received Outstanding Industry-University-Cooperation (IUC) Award from the MoE, ROC (2003); NCKU Distinguished IUC-Professor Awards (2004 & 2008); ROC National Science Council (NSC) Outstanding IUC Award (as the only awardee in 2006); ROC NSC Outstanding Research Award (2006 & 2009); University Industry Economy Contribution Award - Individual Award from the Ministry of Economic Affairs (MOEA), ROC (2008); the TECO Award from the TECO Technology Foundation, ROC (2010); the 2011 National Invention and Creation Award (Silver Medal) from MOEA, ROC; the 2011 Award for Outstanding Contributions in Science and Technology from the Executive Yuan, ROC; the 2012 National Invention and Creation Award (Gold Medal) from MOEA, ROC; and 2013 IEEE Inaba Technical Award for Innovation Leading to Production.

Professor Cheng served as Associate Editor of IEEE Transactions on Robotics and Automation (2000-2004) and IEEE ICRA 2006 Kayamori Best Automation Paper Award Committee Chair. He also served as the Senior Program Committee member of ICRA 2011 and the Program Chair of IEEE WCICA 2011. He was the Convener and Program Director of the NSC Automation Engineering Program, Taiwan, ROC (2007-2009). Prof. Cheng has founded the e-Manufacturing Research Center (eMRC) at NCKU since Jan. 2008 and, currently, serves as the Director of eMRC. Prof. Cheng is now also the Program Chair of IEEE CASE 2014, which will be held in August 2014.

5.2

Co-PIs



Min-Hsiung Hung

Min-Hsiung Hung was born in Changhua County, Taiwan, Republic of China (R.O.C.), on September 9, 1966. He received the B.S. degree from Chung Cheng Institute of Technology (CCIT), Taoyuan, Taiwan, R.O.C., the M.S. degree from National Cheng Kung University, Tainan, Taiwan, R.O.C., and the Ph.D. degree from The Ohio State University, Columbus, Ohio, U.S.A., in 1988, 1992, and 1999, respectively, all in electrical engineering. He was with the Department of Electrical Engineering, CCIT, as a Lecturer in 1992, and was promoted to Associate Professor and Professor in 2000 and 2006, respectively. He also served as the Chairman of the Department of Electrical and Electronic Engineering, CCIT, National Defense University (NDU), Taiwan, from September 2007 to August 2009, and as the Dean of School of Defense Science, CCIT, NDU, between September 2009 and July 2010. Since August 2010, he has been a Professor of the Department of Computer Science and Information Engineering, Chinese Culture University, Taipei, Taiwan, R.O.C. Currently, he is the chairman of his department. His expertise is in the fields of e-Manufacturing, e-Diagnostics, Virtual Metrology, RFID and Wireless Sensor Networks, Dynamic Simulation and Control of Mobile Robots, and Microprocessor and Embedded Systems.



Haw-Ching Yang

Haw-Ching Yang was born in Tainan, Taiwan, R.O.C., in 1966. He received the B.S. degree in engineering science from National Cheng Kung University, Tainan, in 1989, the M.S. degree in control engineering from National Chiao Tung University, Hsinchu, in 1991, and the Ph.D. degree in the Institute of Manufacturing Engineering from the National Cheng Kung University, in 2003. He was with Automation Engineering at Dayeh University, as a lecturer from 1997 to 2000. From September 2004 to July 2013, he was with an Assistant Professor of Institute of System and Control at the National Kaohsiung First University Science and Technology. He also served as the Director of the Reseach Center of Opto-mechatronic of NKFUST, Taiwan, from September 2011 to July 2013. Since August 2013, he is an Assistant Professor of Institute of Electrical Engineering at the National Kaohsiung First University Science and Technology, Kaohsiung, Taiwan, R.O.C. He is with experiences in the analysis, modeling, design, and implementation of manufacturing automation. His research interests include factory automation, system simulation, virtual production, and cloud application.



Chao-Chun Chen

Chao-Chun Chen is an associate professor in the Institute of Manufacturing Information and Systems (IMIS), National Cheng Kung University, Taiwan. He received the PhD degree in the Department of Computer Science and Information Engineering at National Cheng-Kung University, Taiwan, 2004.

Author's recent photo

Before joining NCKU, he served in Southern Taiwan University and Shih-Chien University Kaohsung Campus. His research interests include mobile/wireless data management, sensor technologies and applications, artificial intelligence, system integration, spatio-temporal databases, and computational geometry. He has published dozens of papers in prestigious journals and conferences, including IEEE Transactions on Knowledge and Data Engineering (TKDE), Wireless Networks (WINET), GeoInformatica Journal, Computers and Mathematics with Applications, Distributed and Parallel Databases, Performance Evaluation, International Symposium on Advances in Geographic Information Systems (ACM GIS), International Conference on Distributed Computing Systems (ICDCS), International Conference on Mobile Data Management (MDM), and International Conference on Database Systems for Advanced Applications (DASFAA).

Dr. Chen served as Editorial Board of Journal of Information and Management Science (IMS), and Program Committee for several international conferences on the topics of intelligent systems and system optimization, such as International Conference on Genetic and Evolutionary Computing (2010-2011), International Conference on Business Intelligence and Financial Engineering (2009-2012), International Conference on Hybrid Intelligent Systems (2009-2012), International Conference on Intelligent System Design and Applications (2012). He also served as a session chair for the 2nd International Conference on Advanced Communication and Networking and the 2nd International Conference on Advanced Science and Technology. Currently, he is the Director of Manufacturing and Mobile Database Lab, IMIS, NCKU.



Rong-Shean Lee

Rong-Shean Lee is a Distinguished Professor at National Cheng Kung University, Taiwan. He received his Ph.D. degree in 1982 in mechanical engineering from University of Leeds, U.K..He taught manufacturing engineering at NCKU since 1982 and established the Institute of Manufacturing Engineering in 1994. He served as the Director of the Institute from 1994 to 1998. Since 2002, he was honoured as Distinguished Professor of NCKU.

He has been invited to be the member of technical advisory committee of materials and processes for Ministry of Economic Affairs. He also served as the adviser to research organizations in Taiwan , including Metals Industry Research and Development Centre, and Mechanical Industry Laboratory, ITRI.

He has published over one hundred international journal and conference papers and held two patents. He has received awards for his outstanding research and technical achievements. His teaching and research interests include applied plasticity, computer integrated manufacturing, process simulation and die design optimization for metal forming, formability and manufacturability evaluation, multi-axes machining and virtual machine tools.

He has actively participated in several professional societies. He is a member of International Cold Forging Group(ICFG). He served as the editor of Journal of Chinese Society of Mechanical Engineering. From 1998-2007, he served as the Member of the Editorial Board for the Journal of Engineering Manufacture, Institution of Mechanical Engineers (IMechE), U.K. He is well known for his dedication and contribution to the development and promotion of scientific and systematic methods to solve metal forming problems and computer applications in manufacturing processes and systems.



Yung-Chou Kao

Yung-Chou Kao is Professor at the National Kaohsiung University of Applied Sciences (KUAS), Taiwan. He received his Ph.D. degree in 1998 in mechanical engineering from University of South Australia, Australia. He lectured manufacturing engineering related subjects at KUAS since 2000 and established the Remote Virtual Rapid Manufacturing (RVRM) Laboratory in 2002.

He has been invited to be the adviser of research organizations in Taiwan, including Metals Industry Research and Development Centre (MIRDC), and Precision Machinery Research and Development Center (PMC).

He has published over one hundred and seventy international journal and conference papers, held three patents with two patents under application, and published three books. His teaching and research interests include virtual reality machine tool simulator, remote manufacturing system, computer aided manufacturing, metal forming process simulation and die design optimization, multi-axes machining and emulations.

He has been actively participated in several professional societies such as Taiwan Forging Association and Taiwan Light Metal Association. He has been reviewers of International Journal of Advanced Manufacturing Technology and Computers in Industry. Professor Kao has successfully developed a software system on three-axis virtual milling machine center simulation and this system has been adopted by several major Senior Vocational High Schools and Universities in Taiwan and in Hong Kong.

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