Internet-Based Manufacturing Process Optimization and Monitoring ...

Internet-Based Manufacturing Process Optimization and Monitoring System P. Renton, P. Bender, S. Veldhuis, D. Renton, and M. A. Elbestawi McMaster Manufacturing Research Institute, Hamilton, ON., Canada R. Teltz1 and T. Bailey2 2 1 Iomega, Burlington, ON, Canada, United Technologies Research Center, East Hartford, CT, USA. Abstract E-intelligence for integrated product design, manufacturing and service is becoming the core of 21st century advanced manufacturing technology. This paper presents the current efforts at the McMaster Manufacturing Research Institute (MMRI) to implement an Internet-based Computer Aided Engineering (CAE) facility for the development of new manufacturing processes. The facility, termed The Centre for Online Manufacturing Optimization (COMO), integrates advanced process simulation software, a remote machine monitoring system, and state of the art multimedia and Internet technologies, to realize a virtual manufacturing process optimization environment. This environment leads the manufacturing engineer through a set of procedures in which physics based process simulations allow process parameters and operational sequences to be evaluated and optimized with respect to desired product features. Once the process parameters are established, the part can be prototyped at the MMRI laboratory or the industrial facility. The Internet-based computer engineering facility will provide companies with predictive intelligence tools for process development, and production monitoring. A Case Study is presented, in which a 3-axis milling operation is defined, optimized and executed using the COMO facility. 1. Introduction The business world is entering a new era of eintelligence, e-factory, e-automation, e-maintenance and e-service [1]. The globalization of industry and the worldwide competitive economy are forcing leading manufacturing organizations to possess the ability to develop and produce virtually defect-free products quickly in response to opportunities and needs of the changing world market. Companies with e-intelligence know-how will have a unique position in bringing about innovative products, manufacturing and service systems to support their customers and sustain leadership in future competitive business. The initial generation of smart manufacturing were developed at the Machine Tool Agile Manufacturing Research Institute (MTAMRI) [2-4] and the University of California-Berkeley [5,6]. The efforts were concentrated on developing a series of well-established prediction and optimization methods for different machining processes

including milling, drilling, tapping, fixture design and cutting fluid evaluation. In general, the required inputs include cutting geometry, tool and workpiece materials, machining conditions and types of cutting fluids, and the outputs are the cutting forces, torques, power, tool deflections, process dynamics and surface finish. The University of California-Berkeley, has developed an Internet-accessible, CAD/CAPP/CAM system called CyberCut, which creates a part from prismatic stock by removing feature primitives defined by conventional machining operations. Generally, the part design is deterministic with respect to manufacturability. Both applications are accessed remotely and do not reside on the client’s computer. The University of Wisconsin developed an Emaintenance concept, providing predictive intelligence tools to prevent unexpected breakdown, by monitoring products over wireless internet communication systems. The systems can also compare a product’s performance through globally networked monitoring systems, allowing companies to focus on degradation monitoring and prognostics rather than fault detection and diagnostics [1]. The concept used by the MMRI to implement an Internet-based CAE facility for the development of new manufacturing processes has been discussed in [7]. This paper discusses the technologies used to implement a user-friendly, internet-based facility for optimizing and monitoring manufacturing processes. Utilizing advanced process simulation software and monitoring technologies, the facility provides the manufacturing engineer with a scientific understanding of the manufacturing process. By analyzing, comparing and reconfiguring the process, the engineer is able to establish the capability of a proposed processes with significantly lowered risk, and to focus on the manufacture of their product designs, delivering optimized solutions to their customer. The alternative is to have costly capital equipment available for trial and error testing, while enduring unpredictable costs as new processes are “tuned” on production equipment. This paper expands on previous research efforts at the MMRI, focussing on the following: • Development of a generalized framework for optimizing manufacturing processes, accessed via the Internet. • Generic Remote Machine Monitoring System • Case Study of a 3-axis Milling operation

2. System Overview The COMO system has a generalized, modular framework, composed of a set of modules, illustrated in Figure 1, that interact using a defined set of communication protocols. The communication is performed over two different networks: the local area network (LAN) and the internet. The four modules performs distinct tasks within the system and are divided into two groups – the Client Module and the server-side network. The Client Module is local to the system user and is platform-independent. Its main function is to provide the mechanism for interfacing with COMO via the internet. The remaining Modules make up the essence of COMO – the simulation, optimization and testing are all performed on the Server-Side Network. A key challenge in developing an internet-based system like COMO is making it function as if it is installed locally on the client’s PC. The system requires a bandwidth of approximately 2-Mbps, to allow for the responsiveness necessary to mimic local installation. With increased bandwidth, better results are possible. The generic implementation of the Modules, and their integration into the system presents one of the greatest difficulties in developing COMO. The following sections describe the system Modules. 2.1 Client Module The client module provides a platform-independent interface (written in Java) through which the user can gain access to COMO. The Client provides the engineer with a method for setting up and defining the manufacturing process to be optimized and also allows the user to view process simulation and monitoring results in graphical or tabular format. A 3D graphical display and animation of the process allows for visual confirmation of the overall operation.

Internet Local Area Network

Process Simulation • Executes manufacturing process simulations • Returns results to server Server

Client

• Handles client requests

• User input

• Database containing user project/process information

• Data viewing

• Simulation Results Monitoring • Captures data from process sensors • Data transmission to Client

Figure 1: System Schematic

2.2 Server-Side Network The server-side network for COMO implements the Server, Monitoring and Simulation Modules. Updates to the code which add features or fix bugs are therefore only done in one place, and all the clients gain access to them immediately, without having to apply patches or re-install the software on their side. This reduces the maintenance for the client and puts less load on their computers. Having the client’s data accessible over the Internet allows them to access it from different locations and to collaborate with others. Server Module The Server Module handles requests made to COMO by the Client. The Server also contains a database that is used to store all the data dealing with a COMO project. Some of the files generated by the Simulation and Monitoring System are large and are stored outside of the database. These files are placed in a standard project directory structure. Monitoring Module If the client chooses to create a prototype, the process is monitored using the Monitoring Module. The monitoring system software and server are generic and process independent. The monitoring system gets its setup information from the servlets. This module of COMO requires human interaction to physically set up the manufacturing process, verify it and execute it. The Monitoring module acquires real data from sensors that are measuring process parameters and streams the data directly to the Client, synchronized and in real-time, while the process is running. This information (data) is also stored as a file for access at a later date. Simulation Module The Simulation module contains the set of all process simulations available to the user. The Server selects the appropriate process simulation based on the type of manufacturing process being optimized, as specified by the user. For example, for a machining operation, these processes include milling, turning, drilling, tapping etc. 2.3 Project Structure and Data The user interface is based on the concept of user “projects”, as illustrated in Figure 2. A project represents a series of N “Operations” that are to be performed on a workpiece to produce a final part. For example, a metal cutting application may consist of operations that include processes such as milling, turning, drilling and tapping. Each operation is optimized by setting up and simulating a number of (M) Test Cases, which are scenarios for performing the operation. For instance, each test case can change any of the operation parameters including the tool, path, machine, or feeds and speeds. Post-processing

Main Server

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Operation 1

Test Case 1

Operation 2

Test Case 2

…

LAN

Operation N

… Test Case M

Internet

Data PC

Data PC

Data PC

CNC 1

CNC 2

CNC n

Figure 3: Schematic of the Monitoring Facility Figure 2: Project Structure capabilities aid the user in the evaluation of results and in guiding further simulation studies. Once acceptable conditions are determined (the process is optimized), the user may choose to monitor the execution of the process on an instrumented machine. Process data is sent back to the user to allow the confirmation of the physical setup, troubleshooting of the operation, and evaluation of process results. This data includes a video of the process execution, data measured from sensors monitoring process parameters, and part geometry information. 3. Remote Machine Monitoring System (RMMS) A generic data-acquisition system has been created for remotely monitoring manufacturing processes as they are executed. A Client has the ability to monitor a process as it is executed from a remote location. This is a valuable tool for new product design, development and testing. After iterating through the manufacturing process and selecting the most productive and economical production setup from all Test Cases investigated, the user has the ability to produce a prototype part using the existing facilities. The creation of a prototype completes the design cycle, taking the process from concept to final product. During the production of the prototype, the Client is able to monitor process parameters from any electronic sensor connected to the RMMS. The following sections provide details of the monitoring facilities. 3.1 Monitoring Facilities The MMRI has a wide variety of Computer Numerical Control (CNC) machines. These machines range from conventional to state-of-the-art high-speed machines. The MMRI also has several Polymer Processing Machines. All of the machines within the Institute are available to clients using COMO, providing a means to generate a prototype. The facility also has two Coordinate Measuring Machines (CMM). These CMMs can be used to verify certain features of the product, such as dimension, surface quality, etc.. This information is then relayed back to the

Client for review. The client can then modify the process and re-simulate, or re-prototype. A schematic of the monitoring facilities is shown in Figure 3. A series of CNC machines are connected to the Main Server on a Local Area Network (LAN). Each of the CNC machines has a monitoring system connected to it – labeled “Data PC” – which acquires data during the prototyping operations. When a Client logs into the Main Server, they obtain indirect access to each of the machines within the facility, via the Server. Through this access, the user is able to send information (programs) to the machines, and to view data acquired by the monitoring PC. The details of the monitoring system are described later in this section. The potential of the RMMS is illustrated in Figure 4. A set of four machines, instruments and sensors are connected to the RMMS – shown are a CNC Milling Machine, a CNC Lathe, a Coordinate Measuring Machine (CMM) and a force dynamometer. Each of these components can be monitored by individuals located around the World, via the internet. This provides a valuable tool for collaborative manufacturing research and development. 3.2 RMMS Hardware Referring back to Figure 3, each of the CNC machines within the Prototyping Facility is equipped with a “Data PC”. This represents the Remote Machine Monitoring System (RMMS) developed, which is capable of monitoring the processes being performed on the machines and transmitting data acquired from the process sensors attached to the machine to a remote client. The purpose for developing the RMMS is to provide the client with the maximum amount of information about the prototyping operation for concurrent or post-operation analysis. A schematic of the RMMS is shown in Figure 5. The PC itself is a Real-Time Linux computer, containing task specific hardware. This hardware includes a frame grabber, sound card and a Data Acquisition and Control (DA/C) card. Each of these peripherals are used to acquire data from the sensors, located on the machine.

CNC Milling Machine

User 1 (Hamilton, ON, Canada)

CNC Lathe User 2 (Calgary, AB, Canada) User 3 (Hartford CT, USA)

RMMS

Coordinate Measuring Machine

User 4 St. John’s, NF, Canada)

Process Sensors (Dynamometer)

Figure 4: Multi-User Connectivity to the Remote Machine Monitoring System (RMMS) The frame grabber captures video from a CCD camera, and the sound card captures audio from a microphone, both of which are mounted within the workspace of the machine. The DA/C card is used to obtain information from sensors mounted on the machine. The number of signals that can be sent (control) and received (acquired) simultaneously can be modified by selecting a different DA/C card. An advantage of this system is that it is designed to be completely open, allowing for hardware changes, without sacrificing performance and capability. This provides a great deal of flexibility in further developing the RMMS. The system has a total of three data sinks. During the prototyping operation, the data from the sensors is temporarily stored locally. The data can also be

Sensors

transmitted via the internet to the client’s PC, for immediate viewing of the results, if requested. This allows the user to view the machining operation as it is happening (live). Upon completion of the operation, the data is transferred from the monitoring PC to the Main Server for permanent storage. 3.3 RMMS Software The sensor data acquired by the RMMS is recorded with a common clock, or time reference. Each segment of sensor data and video acquired is time-stamped, and packaged into three separate files. Upon display to the user, the time reference associated with the sensor data allows it to be displayed with the corresponding video in real time. This allows the sensor data to remain

Real-Time Linux PC

CCD Camera

Frame Grabber

Microphone

Sound Card

Client PC (Hard Disk) Machine Server

Analog Signals Digital Signals Encoder Signals

Data Acquisition and Control Card

Data Sink

Main Server (database)

Data Storage (Hard Disk)

Figure 5: Schematic of the Remote Machine Monitoring System (RMMS) PC

synchronized with the video data in the case where a frame is dropped due to a lack of bandwidth during transmission and/or playback. The transmission and playback rates and resolution are hardware dependent. 4. Results: 3-Axis Milling Optimization using the COMO Facility The RMMS is currently installed on one of the milling machines in the Monitoring Facility. It has been used to acquire both video and sensor data from a three-axis force dynamometer and this data has been successfully transmitted across the local Intranet to a terminal outside of the Monitoring Facility. The COMO facility was tested from setup, through simulation and prototype monitoring for a 3-axis milling operation. The 3-axis milling process is simulated using the model developed by Bailey in [7]. To set up the process, the user selects a 3D CAD model for the initial part. A milling Operation is then defined and parameters are entered for the first Test Case (see Figure 6). The setup information is rendered in the 3D window. With the process parameters set, the user may verify the process by animating it. Assured that the simulation parameters are correct, the user launches the simulation on the server. Next, the user evaluates the simulation results. The simulation output can be displayed to the user in graph or tabular form. Figure 7 shows the output for the initial Test Case in graphical form, where the top graph is the force trace for the overall process, and the bottom graph shows the instantaneous conditions for a selected point in the process. The force trace for the overall process is repeated in Figure 8, indicated as the Nominal Force. The 3D view shows the state of the operation corresponding to the simulation data being displayed. If desired, the user can request that the part geometry be updated to a specified step in the simulation so the process conditions at a particular location can be seen. This assists the user in determining the cause of unexpected results and in determining a course of action for the next iteration of the optimization process. At this point, the user can iterate through as many simulations as required to find the process parameters that produce the desired results.

Figure 6: Test Case Setup

Figure 7: Initial Simulation Results Referring back to Figure 8, it is seen that the forces increase substantially at the end of each tool pass, up to a factor of four at the end of the seventh pass. This is caused by an increase in the amount of material being removed, due to the cusp left behind from the previous pass. The process is optimized by performing a feed scheduling operation, where the feed is reduced at the end of the pass to keep the magnitude of the force at or below a specified maximum. In this case, the maximum force was limited to 300 lbf. The new optimized, Scheduled Force trace is shown in Figure 8, along with the Nominal.

Figure 8: Process Optimization – Feed Scheduled Force

With the process optimized, it is then monitored during prototyping. The monitoring hardware includes a video camera and a three-axis force dynamometer and is connected directly to the RMMS. Figure 9 shows the RMMS display during prototyping. The top section is the view from the monitoring video camera, focussed on the area where the tool is in contact with the workpiece during execution. The bottom section shows the three sets of force data acquired by the dynamometer – in the x-, yand z-directions. Once the process is ready to run, the client will be contacted and informed that the prototype is starting. The user then has the option to tune and watch the prototype live or review it after the process execution is complete. Once production of the prototype is complete, the user has several options available. Upon analyzing the data acquired by the monitoring system – video and force traces – they can return to the simulation environment, modify the process and re-simulate. They may also choose to have the prototype further examined using a Coordinate Measuring Machine, or other metrology equipment before modifying the process. In this case, the user is required to specify the measurements required. The final option is to have the part shipped directly to them for on-site analysis.

The milling machine used for this procedure has a CMM working in tandem with it. The CMM is positioned so that the probe head is able to enter the workspace of the milling machine, once the operation is complete and the chips and cutting fluid have been cleared from the part. One clear advantage to this setup is the ability to verify geometric features on the part produced without having to disassemble the setup. Any modifications that are required can be done in a more timely fashion, as the part does not need to be remounted on the machine. Methods for incorporating the CMM into the RMMS are currently under development. 5. Conclusion This paper presented the implementation of a generalized internet-based manufacturing process optimization and monitoring facility. This facility provides a client with a virtual manufacturing process optimization facility which combines advanced process simulation software with a generic remote machine monitoring system (RMMS). A case study is presented in which a 3-axis milling operation is optimized using COMO. The greatest advantage of systems like COMO is that a wider variety of users have access to the system on an “on demand” basis, using only the required functionality. To make COMO commercially viable, more calibration, optimization and testing of the force model is required. Acknowledgements The authors would like to thank Materials and Manufacturing Ontario (MMO), Canarie Inc. and the Ontario Aerospace Council (OAC) for their support of this project.

Figure 9: Sample RMMS display during a Prototyping Operation

References [1] Lee, J. (2001) E-Intelligence Heads Quality Transformation, Quality in Manuf., Mar/Apr 2001. [2] DeVor, R.E., et al. (1995). An Internet-Based Model for Technology Integration and Access, Parts 1 & 2, ASME J. of Manuf. Sci. & Eng’g, V. 2-2, 991-1022. [3] MTAMRI. (2000). SoftwareTestbeds, http://mtamri.me.uiuc.edu/testbeds. [4] Sutherland, J.W., et al.(1997). CFEST: An InternetBased Cutting Fluid Evaluation Software Testbed, Trans. of NAMRI/SME, Vol. 15, pp. 243-248. [5] Smith, C.S., Wright, P.K. (1996). CyberCut: A World Wide Web Based Design-to-Fabrication Tool, J. of Manuf. Systems, Vol. 15, No. 6, pp. 432-442. [6] Wright, P., Dornfeld, D. (1998). CyberCut: A Networked Machining Service, Transactions of NAMRI/SME, Vol. 16, pp. 281-286. [7] Bailey, T., et.al. (2000). Internet Based Implemetation of Multi-Axis Milling Process Simulation and Optimization, CIRP Int’l Seminar on Intelligent Computation in Manufacturing Engineering.