Machine Tools in the New Era of Manufacturing

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Chapter 3 Methodology

This chapter introduces the methodology of this research. The origin of the concept of CPMT is explained, followed by a discussion on the definition and features of CPMT. A generic system architecture for CPMT is presented and discussed in detail. Based on the generic system architecture, a systematic development method for CPMT is proposed. The development strategies for the key components and functions of CPMT are studied. The methodology presented in this chapter provides the theoretical basis for conducting this research.

Introduction

Based on the literature review provided in Chapter 2, it is identified that there is an urgent need to advance current machine tools to a higher level of connectivity, intelligence and autonomy, in response to the requirements of CPPS and Smart Factory. Recent advancements in ICT such as CPS, IoT and cloud computing have shown great potential in addressing this issue. However, implementation of individual CPS functions in machine tools cannot provide generic and systematic solutions for Machine Tool 4.0. In this context, a novel concept that represents a new generation of complete CPS-based machine tools, i.e. Cyber-Physical Machine Tool, is proposed. CPMT deeply integrates the machine tool, machining processes, computation and networking. Development of the CPMT requires a systematic methodology to provide guidance on the implementation strategies for various enabling technologies. This chapter introduces the methodology for developing the CPMT and provides the theoretical basis for this research.
The remainder of this chapter is organized as follows: Section 3.2 introduces the definition and features of the proposed CPMT. In Section 3.3, a generic system architecture for CPMT is developed to provide guidelines for advancing current machine tools to CPMT. The key components, functions and characteristics included in a typical CPMT are proposed and explained in detail. A systematic development method for CPMT is proposed in Section 3.4 based on the generic system architecture. The implementation strategies for the key components and functions of CPMT are studied, with a focus on the enabling technologies for modelling the MTDT. Section 3.5 summarises this chapter.

Definition and Features of CPMT

Inspired by recent advancements in ICT such as CPS, IoT and cloud computing, a new generation of machine tools – Cyber-Physical Machine Tools – is proposed as a promising development trend of machine tools in the era of Machine Tool 4.0. The definition of CPMT is proposed as follows:
CPMT is the integration of the machine tool, machining processes, computation and networking, where embedded computations monitor and control the machining processes, with feedback loops in which machining processes can affect computations and vice versa.
In general, a CPMT is a complete CPS-based machine tool that has the characteristics of a typical CPS, such as network connectivity, adaptability, predictability, intelligence, with real-time feedback loops and with humans in the loop [36]. With extensive real-time machining data and computations deeply integrated with machine tool and machining processes, CPMT provides various types of feedback loops such as autonomous feedback control, shop floor decision-making assistance and cloud-based analytics and services; all intend to improve the performance, efficiency and effectiveness of a machine tool.
More specifically, real-time data generated by machine tool and machining processes are collected using various sensors and data acquisition devices. Together with the feedback from the CNC controller, these real-time data from the physical world are transferred into the cyber space through various types of networks to build a Digital Twin of the machine tool. The core of a CPMT, as well as the most significant advancement of CPMT compared to traditional CNC machine tools, lies in its Digital Twin, i.e. MTDT. MTDT represents the characteristics and real-time status of the machine tool, monitors and controls the machine tool with built-in computation and intelligence and sends the field-level manufacturing data to different HMIs and the cloud to provide efficient decision-making assistance for different users.

Generic System Architecture of CPMT

This section introduces the generic system architecture for CPMT (Figure 3.1). A typical CPMT comprises three main components, i.e. Physical devices, Networks and MTDT. The CPMT enables three types of feedback loops indicated by coloured arrows in Figure 3.1, including autonomous feedback control loop, shop floor decision-making assistance loop and cloud-based decision-making assistance loop. The proposed system architecture is designed to be generic and extensible so that different types of machine tools, data acquisition devices, networking technologies, communication standards and Digital Twin modelling technologies can be utilized. Details of the components and functions, the feedback loops and the extensibility of the generic system architecture are discussed as follows.

Components and Functions

As shown in Figure 3.1, a CPMT comprises three main components, i.e. Physical Devices, Networks and MTDT.

Physical Devices

Physical devices refer to all the physical devices involved in the machining processes and data acquisition processes. Hence physical devices can be divided into two categories: machining devices and data acquisition devices. Machining devices include each component of the CNC machine tool, the cutting tools and the workpieces; while data acquisition devices contain various types of sensors and measurement devices such as RFID tags and readers, power meters, dynamometers, accelerometers, Acoustic Emission (AE) sensors, cameras and Coordinate Measuring Machines (CMM).
Physical devices are responsible for three main functions: 1) perform machining tasks, collect various types of data that represents the status of the machine tool and machining processes, and 3) send the data to the cyber space for further analysis. It needs to be noted that field-level machining data acquisition is a critical procedure in the development of a CPMT since it is the prerequisite for all the subsequent functions in a CPMT.

Networks

One of the distinct advantages of CPMT is the advanced connectivity. In a CPMT, various types of networks can be implemented to achieve reliable, efficient and ubiquitous communications among the physical world, the cyber space and humans. Real-time feedback from the CNC controllers can be retrieved through various industrial Ethernet, fieldbus and serial communication protocols such as Profinet, EtherCAT, Powerlink, RS-232 and RS-485. Data collected by various sensors can be transmitted to the cyber space through different communication protocols such as Ethernet, WiFi, ZigBee and Bluetooth, with the implementation of low-cost IoT-based microcomputers such as Arduino and Raspberry Pi. Open communication standards such as MTConnect and OPC UA allow the data to be uniformly transferred across the data acquisition devices, data analysis applications and HMIs through the Internet. Consequently, various types of feedback loops among the CPMT, the cloud and humans can be realized.

Machine Tool Digital Twin

MTDT is the core of a CPMT that distinguishes CPMT from existing machine tools. From the perspective of function requirements, the MTDT is defined as a digital abstraction of the machine tool that is capable of: 1) representing the characteristics and real-time status of the machine tool; 2) monitoring and controlling the machine tool with built-in computation and intelligence; and 3) sending the field-level manufacturing data to different HMIs as well as the cloud to provide efficient decision-making assistance for different users. A typical MTDT comprises four main modules, i.e. data fusion, information model, intelligent algorithms and database.
Data fusion: data fusion is a preliminary step to build the MTDT. Firstly, raw data generated by different sensors are cleansed and preprocessed so that meaningful information can be extracted. Secondly, real-time machining data generated by CNC controller and data acquisition devices are converted into a common format, such that the data can be efficiently manipulated in the following processes. Thirdly, data from different data sources (e.g. CNC controller, RFID tags and various sensors) are grouped to their correlated component, in preparation for the following information modelling. As a result, the data fusion module provides unified, accurate and reliable data as the basis for the modelling of the MTDT.
Information model: the information model provides a comprehensive understanding of the machine tool and the machining processes. On the one hand, it clearly presents the logical structure of the machine tool including the relationships between each sub-system and critical component. On the other hand, it groups all the available data items related to a specific component together such that the real-time manufacturing data can be effectively manipulated and utilized in further analysis. Since the information model is digitally developed, it is easy to be modified or extended when existing physical components and data acquisition devices are replaced, or additional devices are implemented.
Intelligent algorithms: various data visualization, process optimization and PHM algorithms can be embedded in the MTDT. These algorithms retrieve specific real-time machining data directly from the information model, and hence endowing the machine tool with intelligent and autonomous functions and provide efficient decision-making assistance for shop floor technicians. The output of these embedded algorithms can be directly sent to the CNC controller as control commands to realize autonomous in-process optimization or fed back to machine operators to assist them in improving machine utilization and machining performances. In addition, the intelligent algorithms embedded in MTDT can also perform signal processing and data analytics tasks and provide the results to the cloud through the Internet. In this case, the MTDT becomes an Edge device in the Edge Cloud [105] such that the amount of data needed to be transmitted to the public cloud can be significantly reduced.
Database: since the CNC controller and the data acquisition devices generate enormous amounts of real-time manufacturing data during machining processes, it is necessary for the MTDT to have a database that is able to safely, reliably and effectively store and manage all the big industrial data. On one hand, the database categorizes the real-time data into different groups and feeds them into the embedded intelligent algorithms in the MTDT based on specific data requirements. On the other hand, the database stores the timestamped real-time manufacturing data as historical data and provides them to the cloud for further data analytics and value-added services.

Feedback Loops

Feedback loops are an indispensable part of any CPS since they complete the integration of the physical world, the cyber space and humans. Another significant advancement of CPMT compared to existing machine tools is the diverse types of feedback loops from the cyber space to the physical world. Traditional CNC machine tools only provide very limited machining data such as axis positions, spindle speed and operating mode to the operators through the control panels fixed on the machine tools. Visualization of machining processes can only be realized by looking through the expensive viewing windows. Remote machine tool monitoring and distributed field-level data access are difficult due to the limited connectivity of the machine tools. These issues result in poor decision-making assistance for humans such as machine operators, maintenance technicians and product designers and production managers.
With extensive field-level data deeply integrated in the MTDT, CPMT addresses these issues by enabling three types of feedback loops as indicated by the coloured arrows in Figure 3.1, i.e. autonomous feedback control loop (blue arrows), shop floor decision-making assistance loop (purple arrows) and cloud-based decision-making assistance loop (green arrows).

Autonomous Feedback Control Loop

In the autonomous feedback control loop, the MTDT analyses the real-time manufacturing data with embedded intelligent algorithms and sends control commands directly to the CNC controller, such that the machine tool can autonomously adjust machining parameters during machining processes to adapt to the dynamically changing machining conditions, while meeting specific requirements such as surface quality, cutting tool wear and processing time. It is worth mentioning that open CNC controllers such as STEP-NC based controllers [106], [107] play an important role in this context. With the implementation of STEP-NC technology, data related to machine capability, process planning, and various machining parameters can be optimized in STEP-NC files and directly feedback to the STEP-NC controllers as control commands [108], [109]. Consequently, autonomous feedback control loop can be efficiently realized without developing additional interfaces between MTDT and CNC controllers.

Shop floor Decision-making Assistance Loop

The integration of the real-time analytical information generated by the embedded intelligent algorithms and the advancements of data visualization technologies such as AR give rise to various types of smart HMIs (e.g. smart phones, tablets and wearable devices) that allow the shop floor technicians (e.g. machine operators, maintenance technicians and quality control technicians) to have an instant, comprehensive and intuitive perception of the machining processes, thus enabling them to make efficient decisions to improve the machining performance as well as the equipment effectiveness. For instance, AR applications can be developed on smart phones or AR headsets to retrieve the real-time status data of the machine tool and machining processes from the MTDT; these data can be overlaid on the real machining environment as virtual objects, diagrams and information to provide advanced AR-assisted process monitoring, high-fidelity machining simulation and maintenance guidance for the shop floor technicians.
Cloud-based Decision-making Assistance Loop
During machining processes, enormous amounts of field-level historical manufacturing data generated from each component and machining process are transmitted to the MTDT. These big data will be further transferred to the cloud database for long-term archiving. Various big data analytics algorithms and computational resources can be utilized to provide cloud-based decision-making assistance for the designers and planners. For example, product designers and process planners can optimize the product designs and the process plans based on the data related to the machining performance; production planners can improve the OEE based on the statistical data related to the machine utilization rate; device providers can also take advantage of the data provided by their customers to improve the design of their devices and provide value-added services. From another point of view, the MTDT can also be encapsulated as a composite manufacturing service [110]–[112] that can be provided, managed and consumed in the cloud manufacturing platform.

Extensibility

Machine tools are usually not reconfigurable or extensible once they have been assembled in the shop floor. However, the proposed CPMT architecture allows the machine tool to be extensible in respect of the available real-time data, the information model, the autonomous functions and the decision-making assistance.
Firstly, different types of data acquisition devices can be deployed on the machine tool to extend the available real-time data based on specific needs of the users. Other than the feedback from the CNC controller, additional RFID tags and readers, cameras, accelerometers, dynamometers, AE sensors and so forth can be implemented to collect real-time machining data related to specific components and machining processes. Based on the unified communication standards and different types of networks, these extended data can be transferred to the cyber space and appended to their associated components as additional data items in the information model. Secondly, the autonomous functions can be extended by embedding customized intelligent algorithms in the MTDT. Various intelligent algorithms such as Artificial Neural Network (ANN) [113], Fuzzy Logic [114], [115], Random Forests [116] and Support Vector Machine (SVM) [117] can be embedded in the MTDT to retrieve multiple real-time data items directly from the information model and endows the machine tool with autonomous in-process optimization, tool wear prediction, surface roughness prediction, and so forth. Furthermore, the advanced connectivity of CPMT allows diverse forms of decision-making assistance and value-added services to be further developed. For example, cutting tool providers can access the machining data to help the users optimize the tool path and cutting parameters based on their knowledge. Maintenance service providers can access the prognosis data and provide remote maintenance guidance using AR technology.
Due to the generic and extensible characteristics, the proposed CPMT system architecture provides strategic guidelines for advancing traditional CNC machine tools to CPMT. From the perspective of machine tool design, the proposed architecture also provides guidelines for the design of the next generation of machine tools that thoroughly integrate machine tool, data acquisition devices, MTDT and networks from the initial design stage right down to the shop floor deployment.

Table of Contents
Abstract 
Acknowledgements 
Nomenclature 
Table of Contents 
List of Figures 
List of Tables 
Research Outputs 
Chapter 1 – Introduction 
1.1 Research Background
1.2 Identifying the Challenges
1.3 Objectives and Scope
1.4 Thesis Synopsis
Chapter 2 – Literature Review 
2.1 Current Trends in Manufacturing Systems
2.2 Machine Tools in the New Era of Manufacturing
2.3 Digital Twin Technology
2.4 Communication Standards for Machine Tools
2. 5 Augmented Reality in Manufacturing
2. 6 Research Gaps and Motivations
Chapter 3 – Methodology 
3.1 Introduction
3.2 Definition and Features of CPMT
3.3 Generic System Architecture of CPMT
3.4 A Systematic Development Method for CPMT
3.5 Methodology Summary
Chapter 4 – MTConnect and OPC UA enabled CPMT 
4.1 Introduction
4.2 MTConnect-enabled CPMT
4.3 Case Study: An MTConnect-enabled CPMT Prototype
4.4 OPC UA-enabled CPMT
4.5 Case Study: An OPC UA-enabled CPMT Prototype
4.6 Summary and Discussion
Chapter 5 – Development of a Consolidated CPMT Platform 
5.1 Introduction
5.2 Conceptual Framework for the Consolidated CPMT Platform
5.3 MTConnect to OPC UA Interface
5.4 A Prototype of the CPMT Platform
5.5 Summary and Discussion
Chapter 6 – Augmented Reality-assisted Human-Machine Interfaces for CPMT 
6.1 Introduction
6.2 Conceptual Framework for the iWindow Application
6.3 Development of the AR-assisted Intelligent Window
6.4 AR-assisted Process Visualization on a Wearable Headset
6.5 Summary and Discussion
Chapter 7 – Conclusions and Future Work
7.1 Recap of the Research
7.2 Scientific Contributions
7.3 Improvement Opportunities of Current Work
7.4 Future Research Directions
References
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