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Abstract— Extensive cost, time and effort associated with
setting up a production line often inhibits transitioning novel
best suited for meso scale system integration. The concept,
however, is scalable to higher or lower scales.
The paper has ...
A. Manipulation modules
The prototype system consists of linear manipulation
modules of three different ranges of motion i...
E. Unique features
The following features are unique in the presented modular
and reconfigurable manufacturing cell:
1. Fu...
θ  joint angle in case of revolute joints and displacement
in case of prismatic joints;
ξ  twist vector representi...
vi. After the calibration, the automation program is either
retrieved from an on-board memory card of the master
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Quasi-Static Evaluation of a Modular and Reconfigurable Manufacturing Cell


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Quasi-Static Evaluation of a Modular and Reconfigurable Manufacturing Cell

  1. 1. Abstract— Extensive cost, time and effort associated with setting up a production line often inhibits transitioning novel ideas into commercialized products. Sustainable revenue generation in such enterprises requires production in large quantities in order to lower the unit cost, and thus improving the marketability. Although this model has been successful and is being followed by most of the industries today, it also limits the scope for new, non-conventional products; especially in the early stages of development where manufacturing risks are higher. One way of reducing this risk can be through improving the reusability of the manufacturing hardware, thus allowing quick and inexpensive transition among different iterations and even different products with the same set of hardware. In this paper, we present such a novel and revolutionary solution for flexible manufacturing. Consisting of a unique set of hardware and software innovations, our proposed system offers a viable way to low volume manufacturing and low risk prototyping of novel product ideas. Called as the “Modular and Reconfigurable Manufacturing Cell (MRMC)”, this proposed system enables quick and easy setting up of a fully automated robotic manipulation and assembly platform, optimized for specific products. The system relies on a novel and proprietary multifunctional interconnect design, built-in to various hardware modules of the system, and a distributed intelligence based self-locating software architecture to achieve almost any possible assembler configuration that is suitable for a specific set of tasks. Necessary and sufficient precision level is maintained via a novel precision optimized hybrid controller and path planner throughout the automation. Competitive specifications in terms of travel range, resolution, accuracy, repeatability, force output, size, weight, power ratings etc., as compared to standard commercial manipulators, has been experimentally verified for the proposed Modular and Reconfigurable Manufacturing Cell (MRMC). I. INTRODUCTION Today’s industries heavily depend on automation, mass production and reliable output for the commercial success of their products. These factors, however, do not come cheap as they require highly sophisticated equipment in order to maintain the desired yield, throughput and reliability. Generally, almost all of such equipment are dedicated systems for specific tasks; extremely expensive; and require advanced skills to operate them. Veering from conventional and already well-established technologies with such an This research is supported by US Office of Naval Research (ONR). Aditya N. Das, PhD (corresponding author) is a senior research scientist at the University of Texas at Arlington Research Institute (UTARI), Fort Worth, TX 76118, USA. (Phone: +1-817-272-5970; Fax: +1-817-272- 5946; E-mail: Dr. Das is also a special member to the graduate faculty at the University of Texas at Arlington. Stephen Savoie is with University of Texas at Arlington Research Institute (UTARI), Fort Worth, TX, USA. infrastructure is a highly risky endeavor that few are prepared to take upon. One example can be the almost non-existing assembly and packaging infrastructure for 3D and heterogeneous micro/nano systems. Although 3D heterogeneous micro/nano systems promise huge potential, revolutionizing miniaturization technology and availing many novel classes of products with advanced functionalities, there are no standard off-the-shelf equipment to construct them and, therefore, new micro/nano system developments are prohibitively slow and expensive. Another example is the area of low to medium volume productions. In such cases, where only a few tens to a few thousands of units are needed on-demand as opposed to millions of units, it is not commercially viable to establish dedicated manufacturing facilities. Military products and parts, high tech medical research components, novel technology R&D facilities etc. are some of the examples of areas requiring low to medium volume production capability. In the above described example cases, and many more similar scenarios, flexible manufacturing is the only compelling solution that can offer the pathway to rapid, cost- effective productization. This flexibility is generally considered to fall into two categories: machine flexibility and routing flexibility. The first category, machine flexibility, refers to the system's ability to be changed to produce new product types, and ability to change the order of operations executed on a part. The second category, routing flexibility, offers the ability to use multiple machines to perform the same operation on a part, as well as the system's ability to absorb large-scale changes, such as in volume, capacity, or capability. Among the major advantages of flexible manufacturing system the following are noteworthy: reduced manufacturing times; lower cost per unit; greater labor productivity; greater machine efficiency; reduced parts inventories; adaptability to operations; and shorter lead times [1, 2]. Flexible manufacturing systems are, however, significantly difficult to achieve due to the cost to implement and also the requirement of substantial pre-planning. These are also the reasons for their slow acceptance by industry. In this paper, we present a flexible manufacturing system that is low cost and implements an automated pre-planning approach. The proposed modular and reconfigurable manufacturing cell (MRMC) primarily offers machine flexibility, as described above. The modules of the proposed MRMC can be configured and reconfigured for various pick and place, bonding and probing applications, as needed during typical assembly and packaging operations. In this paper we will be discussing a prototype of the MRMC that is Quasi-static Evaluation of a Modular and Reconfigurable Manufacturing Cell Aditya N. Das, Member, IEEE, Stephen Savoie 2013 IEEE International Conference on Robotics and Automation (ICRA) Karlsruhe, Germany, May 6-10, 2013 978-1-4673-5643-5/13/$31.00 ©2013 IEEE 258
  2. 2. best suited for meso scale system integration. The concept, however, is scalable to higher or lower scales. The paper has been organized as follows: in section II we present the background work leading to the development of MRMC. Section III presents the technical details for the MRMC prototype. In section IV, the analytical evaluation of the MRMC prototype has been presented. Section V presents the experimental results. Finally, section VI concludes the paper with notes on future direction. II. BACKGROUND The fundamental principles of the proposed modular and reconfigurable manufacturing cell (MRMC) have been built upon the findings and requirements from our previous research in heterogeneous microsystems development via automated 3D microassembly. In past, we have demonstrated that complex microsystems [3, 4, 5], consisting of heterogeneous 3D parts, can be built reliably with high yield and throughput via a combination of custom manipulation hardware [6] and custom automation software that implements special motion planning [7] and control [8] algorithms. We also demonstrated that necessary and sufficient precision in custom configured manipulation hardware can not only be controlled in real-time via the high speed hybrid controller but also can be predicted, and thereby compensated for beforehand during the automation execution, through a unique kinematic analysis [9] that quantitatively predicts the uncertainty in end-effector position due to individual motion errors in the manipulation hardware and their location in the overall kinematic chain. Built upon these know-hows, we have also presented one-of-its-kind pre-planning software [10] and assembly- process simulation software [11], which aid optimization of manufacturing processes that use custom equipment for system integration. These software help optimizing three general manufacturing metrics, i.e. production yield, overall cost, cycle time, and one product specific metric i.e. device performance. Conceptually, manufacturability ‘M’, involving assembly and packaging via custom hardware, is a function of product complexity ‘Ω’, assembler reconfigurability ‘Λ’ and the production volume ‘v’. Mathematically: M = f (Ω, Λ, v) (1) The expression in equation (1) represents the governing dynamics of a flexible manufacturing system. The complexity index ‘Ω’ is a binary value that primarily suggests if a specific task can be automated using a simple open loop control (Ω = 0) or it requires complex closed loop control with active feedback based manipulation (Ω = 1). The derivation is based on a statistical model which suggests that if the combined uncertainty of locating a part, grasping it with an end-effector, manipulating it to the destination is lower than the designed tolerance in the mating mechanism at the destination then there is a high probability (> 99%) of successful assembly and thus the operation can be executed via open loop control. On the other hand, the reconfigurability index ‘Λ’ is a value in between 0 and 10, with 10 representing the highest reconfigurability, which suggests if a robotic manipulation system is easy to reconfigure or not, as a function of the percentages of cost and time associated with the reconfiguration. For absolutely fixed equipment, such as single function tools and certain off-the-shelf hardware, the associate cost and time for reconfiguration is assumed to be infinity and thus the reconfigurability index for such a system is taken as zero. The production volume ‘v’ is incorporated in the form of a histogram data where the number of bins and bin size are determined based on the product type. Finally the manufacturability index ‘M’ is estimated as a value in between 0 and 10, with 10 representing the highest manufacturability. Development of the proposed modular and reconfigurable manufacturing cell (MRMC) is motivated by the need for a truly flexible robotic hardware system that can serve as an application platform and experimental test-bed for the above mentioned innovations. III. SYSTEM DESCRIPTION The modular and reconfigurable manufacturing cell (MRMC) prototype consists of several linear and rotational manipulation modules, different connecting fixtures, and multiple end-effectors. Figure 1 shows the 3D renderings of two of the many possible configurations of the MRMC prototypes. Figure 1. 3D renderings of different configuration for the manipulation modules of the modular and reconfigurable manufacturing cell (MRMC) 259
  3. 3. A. Manipulation modules The prototype system consists of linear manipulation modules of three different ranges of motion i.e. 30mm, 50mm and 70mm. The rotation module is capable of full 360o rotation. Among the different modular fixtures there are quick-build base plates, sample holders, and 90o angle brackets. Two different types of modular end-effectors, an electromagnetic gripper and a dispenser, have been designed and developed to achieve typical pick & place and bonding operations. Figure 2 shows the actual hardware test-bed at our laboratory. Figure 2. MRMC prototype at the UTARI facility These manipulation units use a 2.4Watt stepper motor with a lead screw that moves the stage. Standard 1/4th inch graphite code bearings house the two stainless steel shafts that guide the motion of the stage. The aluminum body of the manipulation stage has been CNC machined. Apart from these components, each manipulation module also consists of a novel and proprietary multifunctional interconnect that allows mechanical as well as electrical connectivity between modules, fixtures and end-effectors. This interconnect component consists of an orientation independent magnetic locking mechanism, which allows mounting of the manipulation modules in any of the four orthogonal orientations i.e. at 0o , 90o , 180o or 270o . Furthermore, the multifunctional interconnect component also includes a microcontroller circuitry to provide local processing capability. The end-effectors and other fixture also incorporate of the multifunctional interconnect in their design for seamless and rapid integration with the manipulation system. Several end-effectors also are a part of the MRMC. Among them there is an electromagnetic gripper, which uses an electromagnet and a permanent magnet. A balanced force based actuation principle allows smooth, bi-directional operation of the gripper. Among other types of end-effectors, the MRMC also has a quick-change dispensing head. B. Motion controller A custom designed motion control PCB interfaces the manipulation modules with motor drives, peripherals such as a touch screen panel and a vacuum generator, and the computer. As opposed to the 1:1 mapping between motor drives and manipulation units in case of commercial systems, this controller enables 1:8 mapping of motor drive and manipulator. Through a multiplexed signaling architecture, the MRMC controller enables a single motor drive to run up to 8 manipulators. Figure 3 shows a picture of the controller along with a few snapshots of the touch screen interface. (a) (b) (c) Figure 3. (a) MRMC controller box and (b, c) touch panel snapshots C. Communication protocols The MRMC uses four types of communication protocols to interface various modules of the system. The touch panel has been interfaced with the main controller via a full-duplex serial communication. Different manipulation modules are connected to each other and to the main controller over an I2 C bus. USB connectivity has been established in between the main controller and computer. Lastly a few time-critical communication channels have been hard-wired. D. Other peripheral units Different peripheral units such as a microscope, a vacuum pump, a data acquisition module etc. have also been integrated with the MRMC. These peripheral modules are used in different types of operations executing the custom automation plans. 260
  4. 4. E. Unique features The following features are unique in the presented modular and reconfigurable manufacturing cell: 1. Fully automated robotic prototyping system 2. Intelligent robotic modules with distributed intelligence a. Modules can automatically identify their position and orientation in the system b. Multiple modules can be operated via single controller via bi-directional communication channel c. Entire system can be programmed & controlled by a master controller without the need for a computer 3. Extremely portable architecture a. No nuts and bolts needed b. No complicated wiring needed c. Low form factor controller unit 4. Commercialization friendly, as compared to conventional manipulation modules in market a. Lower cost b. Competitive precision output c. Improved force output 5. Revolutionary design enabled extreme flexibility for rapid product transition IV. ANALYTICAL EVALUATION One of the major challenges in flexible manufacturing architecture, where system components are frequently reorganized to accommodate changes in tasks, is to guarantee necessary and sufficient precision metrics such as resolution, repeatability and accuracy. In our past work [12], we had investigated the effect of parametric uncertainties in a serial robot chain composed of prismatic or rotary modules on the overall positioning uncertainty at the end-effector. Two types of errors were considered: static errors due to misalignment and link parameter uncertainties, and dynamic errors due to inaccurate motion of individual links. Using common uncertainty metrics, we had compared the precision of different robot kinematic chain configurations and had selected the best suited ones for a generic peg-in-hole assembly task. A. Problem statement Using the above technique, in this work, we analyzed the MRMC, as shown in figure 1. A simple two-step pick-and- place task was chosen as the case study. The task has been depicted in figure 4. Figure 4. Assembly task for evaluating the MRMC B. Machine setup For the analysis purpose, we tested two different configuration of the modular and reconfigurable manufacturing cell (MRMC) to accomplish the task as shown in Figure 4. Both configurations are shown in Figure 5. The first configuration is a RPP robot with ψ-y-z degrees of freedom, whereas the second configuration is a PPP robot with x-y-z degrees of freedom. (R: revolute, P: prismatic) (a) (b) Figure 5. MRMC configurations for executing the pick and place task Robot configuration 1 assembles the product by moving individual parts from one station to another, as shown in Figure 5(a), with the rotation stage acting as a turret to position the end-effector over the three stations. On the other hand, robot configuration 2 (Figure 5(b)) assembles the product on a single platform by positioning the end effector in 3D space. In both cases, for the sake of simplicity, it is assumed that the parts are fixture in the sample holder(s) in such a way that there is no misalignment along the rotation axes, as shown in Figure 4. Kinematic details for the two robot configurations are given in Tables I and II. TABLE I. KINEMATIC SETUP OF ROBOT CONFIGURATION 1 Degree of Freedom (DoF) Order from the root of the chain DoF type DoF range of motion Accuracy ψ 1 Revolute 360o 7.5o y 2 Prismatic 50mm 8µm z 3 Prismatic 30mm 5µm TABLE II. KINEMATIC SETUP OF ROBOT CONFIGURATION 2 Degree of Freedom (DoF) Order from the root of the chain DoF type DoF range of motion Accuracy x 2 Prismatic 50mm 8µm y 1 Prismatic 70mm 12µm z 3 Prismatic 30mm 5µm As mentioned in the above Tables, different manipulation modules have different precision values. Furthermore, the position of these modules in the serial robot kinematic chain also impacts the error at the tip of the end-effector. C. Analytical model Assembly feasibility estimation was computed using the following analytical model (see [9] for detailed derivation). ( ) ( )[ ] ( )[ ]00 1 0 TeeT N n i iiN iiii         ⋅+≅ ∏= δθξθξ θξδθ   , (2) Part 1 Part 2 Part 3 Tolerance: σ12 = 15µm Tolerance: σ23 = 40µm x y z θ φ ψ 261
  5. 5. where θ  joint angle in case of revolute joints and displacement in case of prismatic joints; ξ  twist vector representing the instantaneous motion of a link; T  transformation matrix In equation (2), the additive term is the “static error” or error due to link misalignment, whereas the multiplicative term is the “dynamic error” or error due to joint motion. We used our proprietary iterative analysis software called “Design for Multiscale Manufacturability (DfM2 )”, as mentioned in [10], in order to build a statistical model of the common manufacturing metrics, such as production yield, cycle time and overall cost, for the two assembler configurations shown in Figure 5. A snapshot of the DfM2 software running the robot analysis, using the equation (2), is shown in figure 6. Figure 6. Assembly task for evaluating the MRMC D. Results After 1000 iterations in the analyzer, the statistical data for the two robot configurations suggest the following manufacturing metrics as shown in table III. TABLE III. MANUFACTURING METRICS OBTAINED FROM COMPUTATIONS VIA THE ANALYTICAL MODEL Robot configuration No. of iterations Overall Yield b Cost (% of optimum) a Time (% of optimum) a 1 (Figure 5(a)) 1000 83% 50% 77% 2 (Figure 5(b)) 1000 92% 48% 80% a. optimum desired cost: $3,600; optimum desired time taken in assembly: 4 minutes b. using the design tolerance values as mentioned in the Figure 4 for the parts As evident from the data in Table III, robot configuration 2 offers better yield and cost efficiency at a marginal increase in cycle time. Therefore, although both configurations are capable of executing the specified task, robot configuration 2 is more suited for it. Furthermore, as the tolerance levels for the assembly task changes it also affects the manufacturing metrics. This is shown in table IV. TABLE IV. PERFORMANCE WITH VARING ASSEMBLY TOLERANCE Tolerance Configuration 1 yield Configuration 2 yield σ12:15µm, σ23:40µm 83% 92% σ12:10µm, σ23:30µm 54% 88% σ12:5µm, σ23:20µm 7% 76% It is observed from the Table IV that configuration 2 is a better option as the tolerance for the assembly gets tighter. V. EXPERIMENTAL VALIDATION A. MRMC hardware specifications In order to validate the analytical results, as mentioned in the previous section, we use the prototype modules for the modular and reconfigurable manufacturing cell (MRMC), as shown in Figure 2 and also in the video attachment. The prototype module specifications are given in Table V. TABLE V. MRMC PROTOTYPE MODULE SPECIFICATIONS Parameter Value Unit Resolution 4 to 15 µm Range of motion (linear module) 30 to 70 mm Range of motion (rotation module) 360 degrees Maximum thrust 10 lb Maximum speed 3 mm/sec Pull force limit of interconnects 20 lb Motor type Stepper - Motor power rating 5/0.25 Volt/Ampere System power rating 24/0.9 Volt/Ampere Typical configuration time < 2 minutes Typical calibration/ program time < 5 minutes Manipulation module cost ~ 300 $ Controller system cost ~ 500 $ Manipulators per controller 8 - Size (length x width x height) (90-185)x90x35 mm3 Weight 420 to 780 grams Individual cabling to manipulator Not required - Communication frequency 10 KHz Computation frequency 20 MHz Stand-alone interface Touch panel - PC connectivity USB - Configuration identification Automatic - Assembly automation mode Programmable - B. Experimentation steps The experimentation conducted to validate the analysis consists of the following steps: i. The parts are pre-fixtured on the sample holder prior to the assembly. ii. The experimentation begins with a blank base plate mounted to the optical table; this base plate will eventually hold the robot. iii. The system with the master controller is powered on. iv. The manipulation system is configured by placing the robotic modules in a serial order, starting with placing the first one on the base plate and consequent ones on to the previous ones. During this step, the master controller automatically identifies the module’s position and orientation with respect to a global coordinate frame. v. Once the desired robot configuration is achieved, a calibration command is sent from the controller by pressing a button, which initiates a multi-point calibration technique by each of the robotic modules under the field of view (FoV) of a fixed camera. It is assumed, and also experimentally verified that, during configuration of the manipulation system, the locking error in each robotic module is well within the size of the FoV and thus these errors are observable. 262
  6. 6. vi. After the calibration, the automation program is either retrieved from an on-board memory card of the master controller unit or coded by using the appropriate console buttons. vii. Finally, the program is executed by pressing the RUN button which moves the calibrated robotic modules to carry out the assembly task. During this step, the master controller implements a precision optimized path planning and control algorithm for the automation. viii. Success percentage of the assembly is the ratio of the number of parts assembled vs. total number parts in the device. ix. After assembly is completed, the manipulation modules are dismantled and main power is turned off. x. For each repetitions in the experiment, we go back to step ii and repeat the steps up to step ix. xi. Finally, after the desired number of iterations, the compiled data on manufacturing performances is statistically analyzed. C. Experimentation results Table VI shows the data from the experimentations conducted on the modular and reconfigurable manufacturing cell (MRMC) according to steps mentioned above. TABLE VI. MANUFACTURING METRICS OBTAINED EXPERIMENTALLY USING ROBOT CONFIURATION 2 (FIGURE 5-B) FOR 10 ITERATIONS Parameter Value Deviation from analytical model Overall yield 90% - 2% Average time/assembly 3 min. 36 sec. + 6% Total manufacturing cost $1,850 + 3.3% As seen in Table VI, the experimental finding for assembly using the proposed MRMC is close to the analytical predictions. The higher cycle time can be due to delays in image stabilization/processing during the calibration steps. The marginal increase in the actual cost is due to the labor associated with additional cycle time. VI. CONCLUSION In this paper, we presented a novel flexible manufacturing system offering rapid prototyping capability at low cost, which is ideal for low to medium volume manufacturing. The unique hardware, presented in this work, are of much lower cost in comparison to similar commercially available manipulation modules. Furthermore, they significantly reduce reconfiguration complexity; offer competitive performance in terms of range of motion, force output and precision; and allow easy transition between products. A novel and proprietary multifunction interconnect design enables quick-change mounting and untethered connection between different modules of the system. A 1:8 mapping in controller and manipulation module significant reduces the cost and also make the overall system much more compact and portable as compared commercial manipulators. Future directions in this research will focus on rigorous reliability and performance tests on the hardware modules and extending the software analyzer to also generate automation programs along with predicting manufacturing metrics. The discussed modular and reconfigurable manufacturing cells are envisioned to enable manufacturing at remote sites such as on-board ships and aircrafts; make low volume production of highly specialized and advanced products sustainable; and encourage new product development endeavors. ACKNOWLEDGMENT The authors are thankful to the research and support staff at the University of Texas at Arlington Research Institute (UTARI) for their invaluable help in this work. The authors would also like to extend their gratitude towards office of naval research (ONR) for supporting this work. REFERENCES [1] G. Chryssolouris, “ Manufacturing Systems – Theory and Practice,” in NY: Springer Verlag, 2nd edition, 2005. [2] T. Tolio, “Design of Flexible Production Systems – Methodologies and Tools,” in Berlin: Springer, 2009. [3] A. N. Das, J. Sin, D. O. Popa, and H. E. Stephanou, “Precision Alignment and Assembly for a Fourier Transform Microspectrometer,” in International Journal of Micro-Nano Mechatronics (JMNM), Vol. 5, No. 1-2, Springer Berlin, pp. 15-28, 2009. [4] R. Murthy, A. N. Das, D. O. Popa, “ARRIpede: An Assembled Die Scale Microcrawler,” in Journal of Advanced Robotics, The Robotics Society of Japan (RSJ), 2010. [5] D. O. Popa, R. Murthy, A. N. Das, “M³ - Deterministic, Multiscale, Multirobot Platform for Microsystems Packaging: Design and Quasi- Static Precision Evaluation,” in IEEE Transactions on Automation Science and Engineering (T-ASE), Vol. 6, Issue: 2, pp. 345–361, 2009. [6] A. N. Das, R. Murthy, D. O. Popa, H. E. Stephanou, “A Multiscale Assembly & Packaging System for Manufacturing of Complex Micro- nano Devices,” in IEEE transactions on Automation Science and Engineering (T-ASE), vol. 9, Issue 1, pp. 15–64, Jan 2011. [7] A. N. Das, D. O. Popa, “Precision-based Robot Path Planning for Microassembly,” in Proceedings of the 6th IEEE Conference on Automation Science and Engineering (CASE), Toronto, Canada, pp. 527-532, Aug 2010. [8] A. N. Das, D. O. Popa, H. E. Stephanou, “Automated Microassembly using Precision based Hybrid Control,” in Proceedings of IEEE International Conference on Robotics and Automation (ICRA), Anchorage, Alaska, page(s): 4106-4112, May 2010. [9] A. N. Das, D. O. Popa, “Precision-based Robot Kinematics Design for Microassembly Applications,” in Proceedings of the ASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE), Montréal, Quebec, Canada, pp. 857-862, Aug 2010. [10] A. N. Das, H. E. Stephanou, “Concurrent Engineering in A Microfactory,” in Proceedings of Commercialization of Micro-Nano Systems Conference (COMS), Greensboro, North Carolina, USA, August, 2011. [11] A. N. Das, H. E. Stephanou, “Design of Microassembly through Process Modeling in Virtual Reality,” in Proceedings of Microtech conference and expo, Boston, MA, USA, June, 2011. [12] A. N. Das, D. Popa, H. E. Stephanou, “Precision Evaluation of Modular Multiscale Robots for Peg-in-Hole Microassembly Tasks,” in Proceedings of International Conference on Intelligent Robots and Systems (IROS), San Francisco, California, USA, pp. 1699 – 1704, Sep. 2011. 263