A Smart Machine Supervisory System Framework

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A Smart Machine Supervisory System Framework

Sri Atluru1, Samuel H. Huang1, and John P. Snyder2

1School of Dynamic Systems University of Cincinnati Cincinnati, OH 45221

2Techsolve Inc.

6705 Steger Drive Cincinnati, OH 45237

Abstract

Machine tools and machining systems have gone through significant improvements in the past several decades. Recent advance in information technology made it possible to collect and analyze a large amount of data in real-time. This brings about the concept of a smart machine tool, enabled by process monitoring and control technologies, to produce the first and all subsequent parts correctly. This paper presents a system framework for a smart machine supervisory system. The supervisory system integrates individual technologies and makes overall intelligent decisions to improve machining performance. The communication mechanism of the supervisory system is discussed in detail. Its decision-making mechanism is illustrated through an example that integrates process planning, health maintenance, and tool condition monitoring. Keywords: Smart Machine, Supervisory System, Communication, Decision Making 1. Introduction

With growing technological advancements in the manufacturing world, there has been an emergence of various control systems and technologies that would help increase the efficiency of the machine tool. However, most of these technologies are disparate in the sense that their specialization was confined to the optimization of only one component of the machining process. A general consensus has recently emerged that the effectiveness of automation lies not only in the technical capabilities of individual process monitoring and control systems, but also in the ability of a machine tool to coordinate among all the individual technologies and control systems to deliver an overall optimal performance. The ability to monitor and control multiple process modules forms the basis of the next-generation machine tool, the Smart Machine, which will result in higher productivity, better quality, and prognostic capability for near-zero breakdown performance in the machining process.

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However, it is important to understand that coordination between individual control systems and technologies can potentially lead to a number of problems including conflicting outputs from different systems and the ordering of priority for individual process adjustments. Hence, in order to realize the idea of the smart machine, there is a need for a closed-loop supervisory system to coordinate individual process modules for real-time adjustment, conflict resolution, and priority assignment. The motivation for the development of a supervisory system was identified in the Smart Machine Platform Initiative [3]. The smart machine supervisory system is the manufacturing expert system which works like the brain and nervous system of a smart machine. It collects information from individual components of the smart machine and makes hierarchical decisions based on a set of predefined manufacturing rules and logics. Hence, it addresses the need for an all-encompassing system to enable the First Part Correct philosophy [1].

This paper describes the design and implementation of a smart machine supervisory system. It focuses on the technical definition and architecture of the supervisory system as an over-arching functional area over other components of a smart machine. It then proceeds to illustrate how to implement a supervisory system with an emphasis on integrating tool condition monitoring, pre-process planning, and machine health and maintenance.

2. Literature Review

It is well established that multiple-process monitoring and control improves productivity and reduces machining time [16]. However, existing technologies related to supervisory control have been limited to regulating a single process using a single process variable [7, 14]. Additionally, it was observed that within the existing machining applications, there are no established procedures or standards to implement effective process control [20]. Most of these applications use propriety software and hardware that are bundled together, making them incompatible with other applications.

The technologies related to process monitoring and control in a smart machine can be classified into specialized thrust areas based on their functionalities, briefly described as follows:

Tool condition monitoring: It allows detection of cutting tool conditions, including wear, breakage, missing tools, and collision, in the machining process. It can also function as an excellent process monitoring mechanism with additional capabilities such as metalworking fluid flow monitoring, spindle health and maintenance monitoring, and adaptive control. Technologies that come under this thrust area include popular systems used to monitor cutting tool conditions during the cutting process, such as Caron Engineering TMAC, Artis, and Techna-Tool.

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On machine probing: It concerns with technologies that allow the inspection of a work piece on a machine tool with minimal peripheral equipment or personnel. It also facilitates the verification of machined parts on the machine tool mitigating the use of a Coordinate Measuring Machine (CMM) to determine geometrical tolerance accuracy. On machine probing is usually accompanied by the use of an on-machine probe similar to the probes used on a CMM. The on-machine probe is used to accurately determine the coordinates at pre-determined locations to support the verification process.

Intelligent process planning: It generates, verifies, and optimizes tool paths, automatically selects the most-suitable cutting tools, and optimizes cutting conditions. In addition, optimum cutting parameters (speeds and feeds, etc.) are generated based on overall machining performance requirements, including surface roughness, cutting forces, material removal rate, and tool life. Most Computer-Aided Design/ Manufacturing technologies (CAD/CAM) are drawn under the purview of this thrust area. Additionally, various Computer-Aided Engineering (CAE) software solutions that enable users to analyze machining in 2D and 3D environments by predicting performance indicators are also classified under this thrust area.

Machine tool metrology: It identifies the differences in the reported and actual position of a cutting tool. Sources for these differences can be errors built into the machine such as straightness, linearity, square-ness, pitch, roll, yaw, or dynamic error sources such as thermal growth and cutting force tool deflection.

Machine health and maintenance: It assesses the health condition of the machine tool (in the areas of availability and utilization). Valuable data, such as controller signals and sensor measurements, are analyzed using appropriate prognostics algorithms that allow for machine condition assessment, as well as prediction of performance degradation, so that equipment can be repaired before component failures actually occur.

Supervisory system: it is in charge of coordinating technologies resulted from all the other thrust areas to provide an overall solution to improve machine tool performance.

Research in these thrust areas over the past decades has resulted in a number of commercial products and promising new technologies. These products and technologies are summarized in Table 1.

Table 1: Products and technologies related to smart machine thrust areas

SMPI Thrust Area Products/Technologies Reference

Tool Condition Monitoring

Caron Engineering TMAC http://www.caron-eng.com/ Blum Laser Measurement Tooling http://www.blum-novotest.com/

Artis http://www.artis.de/en/competences/monitoring-solutions/tool-monitoring/

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Nordmann http://www.nordmann.eu/usa/aeltere_neuigkeiten.html Techna-Tool http://www.techna-tool.com/

On-machine Probing PC-DMIS NC http://pcdmis.com/pc-dmis-nc

Intelligent Process Planning

ThirdWave AdvantEdge Production Module

http://www.thirdwavesys.com/products/advantedge_production_module.htm

Esprit CAM http://www.dptechnology.com/ CimSkil http://www.cimskil.com/ Master CAM http://www.mastercam.com/ Vericut http://www.cgtech.com

Machine Tool Metrology

INORA http://www.inora.com/ Remmele http://remmele.com/

Health and Maintenance

WatchDog Agent http://www.imscenter.net/

Freedom E-log http://www.infimatic.com/products/freedom-elog-products.html

Supervisory System

GE Fanuc Oi http://ge-fanuc.com/ NI LabVIEW DSC http://www.ni.com/labview/labviewdsc/ I/Gear DTU http://www.igearonline.com/Products/DTU/

Siemens SINUMERIK 840Di

http://www.sea.siemens.com/us/Industry_Solutions/Machine-tools/Products/CNC/Pages/SINUMERIK_840Di.aspx

KEPServerEX OPC Server http://www.kepware.com/Products/kepserverex_features.asp

B2D Solution Manufacturing http://www.b2dsolutions.com/Solutions_HTML/industry.html MTConnect http://www.mtconnect.org

With regards to the incorporation of a supervisory control mechanism for individual

technologies, it is notable that current R&D efforts in academia and industry is related and directed towards the development of Open Architecture Systems viz. Open Modular Architecture Control (OMAC) technologies group, Open System Architecture for Controls within Automation (OSACA) systems, Japan FA Open systems Promotion (JOP) group and STEP-NC [10-13]. These open architecture systems are expected to address the limitations posed due to the lack of a standard communication protocols among individual technologies.

A recent effort to develop the communication standard between multiple process controls is the MTConnect initiative, which was intended to help realize the "seamless manufacturing pipeline" from design to production [17]. The goal of this pipeline approach is to allow for universal capture of data from the machine tool and then transfer this captured data to other control systems; thereby facilitating a seamless method for managing and analyzing data for process and product optimization. MTConnect is based on the eXtensible Markup Language (XML), which provides for exchange of semi-structured machine-readable

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data. It is also expected to account for the seamless connectivity between various components and systems of the machine tool, right from the lowest end of the process chain to the highest level. Additionally, MTConnect is also expected to deliver on its goal of interoperability, which will enable third party solution providers to embrace the standard in their products [2]. MTConnect specifications were formulated after extensive review and analysis of various other standards including OMAC, Cam-X, and OPC. The XML based approach accounts for hierarchical levels inside the data transferred. It is widely supported by various software and hardware systems, which implies that it can be adopted relatively easily by the manufacturing industry.

In addition to communication, there is a need to develop sensor fusion technologies, as well as systematic design approaches to intelligently construct and implement multi-process control modules in the manufacturing industry [19]. The research is this area had largely been based on ad-hoc construction of various process controllers for specific manufacturing systems [7, 9]. The effort for developing an integrated multiple process control technology is limited. This paper presents a systematic framework to develop an integrated smart machine supervisory system to bridge this technology gap.

3. System Framework

The smart machine supervisory system is defined as a system that integrates and coordinates individual process monitoring and control modules such that a globally optimal machining solution could be delivered real-time to achieve desired quality and maximum productivity. A schematic framework of the supervisory system is shown in Figure 1. The major functions of the supervisory system are communication and decision making. The following subsections describe the communication and decision functions, along with an illustration of the relationship between the supervisory system and individual control modules.

(Insert Figure 1 here)

3.1 Communication Function

Communication, including sending control signals from the smart machine supervisory system to the machine and reading the machine information into the supervisory system, is a key function of the supervisor system. The communication function is intended to be implemented complying with the MTConnect protocol. Implementation of MTConnect on any non-compliant system or legacy machine requires the deployment of an adapter and an agent system, technically referred to as agent core. The MTConnect compliant data is then output by the agent, which can be utilized by external applications for further processing and analysis.

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An MTConnect adapter was built for the Fanuc oi-mc controller. It was developed in C++ programming language. It is a simple adapter built to accept one connection on its socket server. The Fanuc adapter is in turn, programmed to use the FOCAS (Fanuc Open CNC API Specifications) libraries which enable the reading of CNC and PMC data from the machine controller via the Ethernet. The FOCAS libraries (Fwlib32.dll and Fwlibe1.dll) contain numerous functions that reference the data window functions on the Ethernet board of the CNC to access the data available about the machine. The adapter essentially serves as a routing channel between the controller and the MTConnect agent, while also assuming the responsibility for translating raw data from the individual FOCAS functions into structured data that can be easily comprehended by the MTConnect agent. The agent was developed on the Ruby on Rails platform and uses a SQLite3 database.

3.2 Decision Function

One of the features of the supervisory system is to have the ability to make decisions based on the data supplied by the thrust area technologies. The supervisory system needs to monitor, in real time, the inputs from various technologies and must be able to effectively process multiple process signals simultaneously to make the necessary decisions. However, to initiate any adaptive action, there is a need for the supervisory system to communicate back to the CNC. Additionally, in case of an emergency, there is a need for the supervisory system to ensure that the machine responds to the supervisory system with a higher priority than the current NC code being processed.

A solution to address the aforementioned issues is to communicate efficiently with the CNC in real-time, using methodologies such as Interruption Type Custom Macros, which implement the ability to read inbuilt data window functions within the CNC machine controller. This is explained in detail below and can be controlled through USB control switches on the PC.


In Fanuc controllers, when a program is being executed, it is possible that another program can be called by inputting an interrupt signal (UINT) from the machine. This function is referred to as an interruption type custom macro function (Figure 2). The format is as follows:

M96 Pxxxx; enables the macro interrupt

M97; disables the macro interrupt.

When M96 Pxxxx is specified in a program, subsequent program operation can be interrupted by an interrupt signal (UINT) input to execute the program specified by Pxxxx.

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When the interrupt signal (UINT, marked by * in Fig. 2) is input after M97 is specified, it is ignored.

The supervisory system stores relevant decisions, such as stop machining when a broken tool is detected, in programs specified by Pxxxx. During machining, it evaluates signals provided by individual control modules in real-time, and triggers the interrupt signal when necessary. A detailed example of the decision making process is provided in Section 4.

3.3 Relationship between the Supervisory System and Individual Control Modules

The supervisory system is responsible for communicating with individual control modules, evaluating the information it obtained, making appropriate decisions, and controlling the machining process. Here we use machine health and maintenance to illustrate the relationship between the supervisory system and thrust area technologies. The health and maintenance thrust area is intended to monitor the CNC machine, perform machine prognosis, and determine the OEE (Overall Equipment Effectiveness) of the CNC machine on the shop floor [5].

It is the responsibility of the supervisory system to provide the over-arching functionality for health and maintenance technologies to seamlessly communicate with the CNC machine to monitor the required data and subsequently, use the prognosis forecasted by these technologies to determine the suitability of the CNC machine to do a certain job. Health and maintenance technologies require the following data parameters from the machine controller [15]:

- Actual spindle speed - Spindle status - Trigger to start monitoring the tool assembly - Two macro-variables, which provide information about the system parameters of the

controller - Actual feed rate - Spindle load (in % from the load meter on the controller)

The MTConnect implementation within the supervisory system is designed to provide

for seamless data transfer between the machine tool controller and health and maintenance applications. The MTConnect adapter built for Fanuc captured the machine PMC data and transferred it to the MTConnect agent. This agent transformed the machine data into MTConnect standards and relayed it onto a HTTP port, which was accessed readily by the health and maintenance technologies. The health and maintenance technologies had access to peripheral sensors mounted on the CNC machine, thus enabling the accurate prognosis of CNC machine tool health using sensory data as well as the PMC data parameters supplied through the MTConnect Agent Core implemented.

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Figure 3 depicts the architecture of the MTConnect system installed enabling communication between the supervisory system and the health and maintenance technologies. This implementation also addressed the issue of plug and play functionality across a variety of machine tool controllers from different manufacturers. It illustrated the concept of having multiple MTConnect agent cores for various CNC machine tools that would communicate across the shop floor network to various applications and technologies.


4. System Integration: Process Planning, Health Maintenance, and Tool Condition Monitoring

Now we focus on the decision-making capability of the supervisory system when coordinating various thrust area technologies. An integration interface was developed among three of the thrust areas -- Intelligent Process Planning (IPP), Tool Condition Monitoring (TCM), and Health and Maintenance (HAM) -- as well as with the CNC Machine Tool. As there are a multitude of technologies pertaining to each thrust area, one technology was chosen to represent each of these thrust areas. The choice of these technologies was based on detailed review of the technologies and expert feedback. The thrust area technologies, thus selected, are briefly described as follows:

- IPP deals with virtual simulation and the subsequent generation of optimized tooling and tool paths necessary for machining operations. In addition, cutting parameters (speeds, feeds, etc.) are also optimized based on overall machining performance requirements, including surface roughness, cutting forces, material removal rate, and tool life. ThirdWave AdvantEdge Production Module [18] was used by IPP in its efforts to simulate machining process in order to generate NC programs based on user-defined tool profiles. It then verifies the generated tool path in the NC program based on its own machining performance database related to force calculations, physics based material models, and optimization speedups. Finally, it draws up an optimized tool path and a new NC program in order to achieve reduced cycle time, maximum machine utilization, and optimum machining performance.

- TCM monitors the in-process condition of the cutting tools, including wear, breakage, presence of tool, and unforeseen collisions. Most of TCM technologies are power sensor based applications that monitor the spindle power to determine and predict the occurrences of tool wear and other tool defects. Tool Monitoring Adaptive Control (TMAC) by Caron Engineering [6] is selected for TCM. TMAC supports all central monitoring tasks expected of TCM technologies based on fluctuations in spindle power recorded. The recorded spindle power is weighed against a power representation that

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forms the basis for determining the permissible limits of wear for specific cutting processes. These limits that determine the permissible wear on the cutting tool are based on the quality requirements of the manufactured part.

- HAM aims at providing an accurate description of the quality of the machine tool based on machine prognostics and experimental data. The WatchDog Agent [8] was developed by the Intelligent Maintenance Systems Center at the University of Cincinnati to support the HAM module, with a goal to achieve a health monitoring system capable of accurately monitoring and predicting the machine health for near-zero downtime.

The integration of these thrust area technologies via the supervisory system aims to overcome drawbacks of individual technologies to achieve optimal machining performance. We first discuss the drawbacks of the TCM and IPP and how these drawbacks can be overcame through supervisory system integration. We then discuss further supervisory system integration with HAM to provide required data in real time.

The current principle of almost all TCM technologies, including TMAC, is based on learning a good cut to set the tool limits to diagnose defects in the tooling assembly. The essentiality of a learning cut, which acts as a basis for future monitoring, has the following drawbacks:

- The machine needs at least one stock part and one new tool for the learning cycle. - The tool and machine are not protected against errors or collisions during the learning

cycle. - Given the lack of computerized monitoring during the learning cut, it is very possible

that the part produced through a learning cut will need to be scrapped or re-machined. - The subsequent limits to monitor tool wear, tool breakage and tool presence were

usually set based on historical data rather than a scientific approach.

On the other hand, IPP technologies utilized physics based material models to draw up force calculations in their efforts to optimize NC tool paths to increase machine utilization. However, the outputs from the ThirdWave production module were only the NC program with an optimized tool path. The internal force calculations and physics based material models served as volatile internal data that was simply put away on subsequent optimizations; and thus, could not be further utilized in streamlining the monitoring process of other technologies.

By tapping into the force calculations and the physics based material models used by ThirdWave AdvantEdge Production Module, it is possible to actually postulate a fundamental power representation of the cutting process. This power representation can be used in place of the power representations picked up by Caron TMAC technology during its learning cut. Several mappings were created to within the supervisory system to enable the integration

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between IPP and TCM. These mapping included ways to tap into the internal force calculations of the ThirdWave Production Module, and also methods to customize the scheduled tool condition monitoring job to look at the power representations built based on those force calculations instead of the usual representations picked up during the learning Cut.

As shown in Figure 4, the idea is to use a combination of these mappings to generate an accurate power representation (peak power) and force prediction based on material physics. This way, the job file can be generated automatically and the process of learning a good cut can be eliminated. However, it must be noted that the power representation picked up by Caron Engineering during its learning cut is subjected to processing under various internal filters during actual monitoring process. To facilitate this internal filtering by the Caron TMAC technology, the first cut is instead used as a calibration cut.


Similarly, while doing away with the manual inputs required by HAM technology represented by the WatchDog Agent (as described in Section 3.3), additional functionality was incorporated by using a set of advanced decision rules based on expert feedback and historical data, which was utilized to check for tool validity and determine if the tooling selected by the NC program meets the conditions (user defined tool profile and machine definitions) assumed by IPP technologies during their optimization and NC code generation. The tooling and machine setup used in ThirdWave Production Module (as well as other IPP technologies) needs to match the tooling setup that exists on the ATC (Automatic Tool Changer) of the CNC machine, to avoid potential conflicts during actual machining. The process diagram illustrating this integration is shown in Figure 5.


Figure 6 shows the complete integration approach adopted by the supervisory system. The interruption type custom macro is used to handle any cutting tool abnormality (wear beyond permissible limits, missing tool, and broken tool) detected by Caron TMAC. A retract program is written in such a way that the cutting tool retracts back whenever the supervisory system detects an abnormality through Caron TMAC technology. In this process, the supervisory system takes the alarm signal from the log file of the Caron TMAC system, instead of waiting for the alarm to be generated on the controller screen. This is to facilitate real-time response to the situation.


In addition to the integrations, various correlations pertaining to process uncertainty and tool tolerances, such as limits for wear of the tool, were analyzed so that intelligent

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knowledge-based decisions can be made by the supervisory system. A feed forward process design about the correlation between these technologies was then developed to simulate the learning curve in consequent process plans. A number of tests were conducted to determine a correlation between predicted power (using power representations from ThirdWave) and measured power (during TMAC learning cut). This correlation was then used to determine the predicted power representation for the monitoring process. The fitting relation for TCM power representation (peak power prediction) obtained was

Pp = 1.396PTW + 0.574 (R = 0.988)

where,

PTW is the peak power representation predicted from Third Wave

Pp is the predicted peak power to be used as the power representation in Caron TMAC

This correlation was generated from tests on 5 different cutting regimes (each regime being defined as a distinct combination of speed and feed) and 10 data points on each of the regimes. Subsequently, a number of tests were conducted to investigate the uncertainty of prediction based on the ThirdWave power representations associated with the above equation. An unbiased experimental design block was generated for two different cutting regimes.

As per the guidelines for evaluating and expressing the uncertainty of measurement results provided by the National Institute of Standards and Technology, a combined standard uncertainty of 3 ( being the standard deviation in the observations) would encompass over 99% of the total normal distribution. Hence, these uncertainties associated with 3 were used as a starting point limits to determine permissible wear on the cutting tool during the calibration run.

As has been mentioned before, subsequent to the calibration cut, the supervisory system continues to monitor the log file of the TCM technology during all ensuing tool condition monitoring tasks, to initiate any adaptive action that might be necessary in the case of any alarm. The communication module of the prototype supervisory system continues to communicate with one of the subsystem components, specifically Caron TMAC System, to generate an alarm if required. The alarm generation would then trigger an interruption type custom macro in the Fanuc controller which is preprogrammed to execute a retract program to prevent any further damage to the tool and the work piece. An USB controlled digital output generator was also used to aid in the implementation of the interruption type custom macro. LabVIEW (National Instruments) was used to trigger the interruption type custom macro.

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The GUI (Graphical User Interface) of the main LabVIEW program (Figure 7) was developed to create a flexible and operator friendly application for use on the shop floor. It automated the whole process with the goal of the supervisory system and First Part Correct in view. This prototype supervisory system was successfully demonstrated to the industry a number of times during the years 2008 to 2010. The supervisory system was successful in demonstrating the generation of job files for TCM technology based on the inputs (power representations) from the optimized cutter path generated by IPP technology. It also successfully verified whether the tooling available on the ATC of the CNC machine was in line with the requirements of the tooling definitions and machine profiles assumed during the pre-process by IPP technologies. It also successfully verified the suitability of the tool holder assembly to do the prescribed cutting and made changes to the NC program when appropriate.


5. Conclusion

The Smart Machine program was developed as a reinvention of the basic manufacturing process. It aimed at providing an optimal manufacturing process through the coordination of various disparate manufacturing control systems. The supervisory system is in charge of coordinating individual technology areas to deliver an optimal manufacturing solution in real-time. A prototype of the supervisory system was developed to demonstrate this functionality. It made use of available data in technologies employed by intelligent process planning, tool condition monitoring, and health and maintenance, to provide an optimized solution by cutting down on time required for tool verification, metal cutting for learning processes, and calibration. In the future, the prototype also needs to incorporate other thrust areas, viz. on-machine probing, and machine tool metrology, to develop a more robust supervisory system.

Acknowledgement This research was sponsored by the U.S. Army Benet Laboratories and was accomplished under Cooperative Agreement Number W15QKN-06-2-0100. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of U.S. Army Benet Laboratories or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation heron.

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Figure 1 : Schematic of supervisory system architecture.

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Figure 2 : The interruption type custom macro (from GE Fanuc documentation)

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Figure 3: Architecture of MTConnect interface for health and maintenance technology

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Figure 4: Process diagram for TCM-IPP integration

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Figure 5: Process flow of integration with health and maintenance technology

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Figure 6: Flowchart showing the overall integration approach

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Figure 7: GUI of the supervisory system

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A Smart Machine Supervisory System Framework