Patent Publication Number: US-6985791-B2

Title: Grinding wheel system

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
   This application is a continuation of prior U.S. patent application Ser. No. 09/465,349, filed on Dec. 16, 1999, now U.S. Pat. No. 6,602,109 and the benefit of prior U.S. Provisional Patent Application Ser. No. 60/112,456, filed on Dec. 16, 1998. The contents of both of these applications are incorporated herein by reference in their entireties 

   STATEMENT AS TO FEDERALLY SPONSORED RESEARCH 
   This invention was made with Government support under DE-FG05-96OR22524 awarded by the U.S. Department of Energy. The Government may have certain rights in the invention. 

   FIELD OF THE INVENTION 
   The invention relates to grinding wheels. 
   BACKGROUND 
   Grinding is a widely used precision machining process, accounting for over 20% of all machining processes in the manufacturing industry. Referring to  FIG. 1 , one type of grinding process employs a rapidly spinning grinding wheel  10  bonded with abrasive materials  12  (e.g., diamond abrasive particles in a resin, vitreous, or metallic bond). The wheel  10  grinds workpiece  14  moving slowly underneath the wheel  10 . 
   Ceramic materials such as silicon nitride, silicon carbide, aluminum oxide, and zirconia are hard, low density materials with high wear resistance and the ability to withstand high temperatures. Grinding is often used to machine ceramic workpieces and workpieces made of other materials into their final shape. Costs associated with grinding include the cost of preparing a wheel (e.g., wheel truing and dressing). 
   Truing typically rounds a wheel by machining excess abrasive material off its periphery as the wheel rotates. Initially, a truing tool engages the rotating out-of-round wheel intermittently, removing material from protruding areas and progressively engaging more of the periphery as the wheel is rounded. 
   Dressing conditions the wheel surface topography to achieve a desirable grinding behavior. Typically, a bonded abrasive dressing stick is passed over the wheel periphery to expose the abrasive grains by eroding away binder and possibly removing and/or fracturing diamond grains. Re-dressing is periodically needed during grinding to recondition or resharpen a worn wheel surface. Severe and/or frequent dressing can result in excessive wheel consumption, whereas too gentle or insufficient dressing can result in a dull wheel. Dressing frequently can be time consuming and reduce the life of expensive abrasive materials. On the other hand, grinding with a dull wheel causes increased grinding forces which can lead to chatter vibration and damage to the workpiece. 
   For precision grinding operations, the wheel depth of cut may be comparable to or smaller than the wheel out-of-roundness. Therefore, wheel engagement with the workpiece can vary considerably during a single rotation. The wheel may even completely lose contact with the workpiece during part of each rotation. This unsteady behavior can have a deleterious effect on the wheel surface and the quality of the ground workpiece. 
   Material removal during grinding occurs when abrasive grains interact with the workpiece. This interaction generally involves both ductile flow and brittle fracture. As an abrasive grain engages the workpiece, initial cutting by ductile flow is followed by localized fracture if the grain depth of cut and the resulting force on the grain becomes sufficiently large. By analogy with indentation fracture mechanics, two principal types of cracks have been identified: lateral cracks which cause material removal and radial cracks which cause strength degradation. The implication of this observation is that strength degradation may be minimized by promoting ductile flow instead of fracture at the ground surface. For finish grinding operations, this would usually require extremely slow removal rates in order to achieve a small enough grain depth of cut and small enough force per grain. However, as a wheel is used and the abrasive material becomes duller, force levels increase, making it necessary to periodically re-dress the wheel. Periodic truing may also be necessary to restore the macroscopic shape of the wheel. 
   Typically, operators monitor the grinding and preparation processes to determine when the wheel is rounded and when the wheel needs to be dressed. Because of the practical difficulty in assessing the condition of a rapidly rotating wheel, operators typically manage wheel usage based on observation and experience. For example, an operator may periodically stop a grinding process to examine wheel characteristics (e.g., roundness and dullness) at intervals determined by the type of workpiece being ground. 
   SUMMARY OF THE INVENTION 
   Embedded force and acoustic emission sensors and on-wheel electronics enable an operator to continuously monitor wheel conditions using sophisticated real-time techniques without interrupting the grinding process. Processing electronics can be attached to the wheel using a modular adapter disk that enables operators to easily reuse, maintain, and modify the electronics. 
   In general, in one aspect, the invention features a grinding wheel system that includes a grinding wheel with at least one embedded sensor and an adapter disk containing electronics that processes signals produced by each embedded sensor. The adapter disk is constructed to attach to the grinding wheel and to connect to each sensor lead when attached. The electronics include a transmitter that transmits sensor information to a data processing platform. The data processing platform includes a processor, a receiver that receives sensor information transmitted by the electronics, and instructions that cause the processor to process the received sensor information. 
   Different embodiments can include one or more of the following features. The grinding wheel may include at least one force sensor which may be positioned near the grinding wheel periphery. The grinding wheel may include at least one acoustic emission sensor which may be positioned near the grinding wheel rim. The sensors may be piezoceramic sensors. 
   The electronics can include an analog to digital converter connected to a sensor and a digital signal processor fed by the analog to digital converter. The electronics can include a multiplexer connected to the embedded sensors. 
   The data processing platform instructions can compare sensor information collected from different sensors at substantially the same time and/or compare sensor information collected from a single sensor at different times. The instructions can cause the processor to process sensor information using at least one neuro-fuzzy network. 
   In another aspect, a grinding wheel system includes a grinding wheel with at least one piezoceramic sensor embedded near the wheel periphery for detecting wheel forces and at least three piezoceramic sensors positioned near the grinding wheel rim. An adapter disk containing electronics that processes signals produced by the sensors attaches to the grinding wheel and connects to each sensor lead. The electronics include a multiplexer fed by the sensor leads, an analog to digital converter fed by the multiplexer, a digital signal processor fed by the analog to digital converter, and a radio frequency transmitter fed by the digital signal processor. The data processing platform includes a processor, a radio frequency receiver that receives sensor information transmitted by the adapter disk electronics, and instructions that cause the processor to process the received sensor information. 
   In another aspect, an adapter disk that processes signals produced by at least one sensor embedded in a grinding wheel includes at least one lead for connecting to each embedded sensor and electronics for processing sensor signals. 
   In another aspect, a computer program, disposed on a computer readable medium, that analyzes data acquired via sensors embedded in a grinding wheel includes instructions that cause a processor to receive sensor data representing force sensed by each sensor and analyzing the received data. 
   The computer program may determine, for example, wheel dullness, grinding mode, roundness, and/or roughness. The computer program can implement at least one neuro-fuzzy network. 
   The invention provides several advantages. The grinding wheel system permits sophisticated real-time analysis of grinding wheel conditions. The positioning of the force and acoustic emission sensors prevents the sensors from producing responses to normal wheel events (e.g., vibrations routinely produced during grinding). By housing electronics in an adapter disk, operators can easily reuse, maintain, and modify the electronics. The system&#39;s data processing capabilities provide a wide variety of information regarding wheel characteristics. 
   Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting. 
   Other features and advantages of the invention will be apparent from the following detailed description, and from the claims. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
       FIG. 1  is a diagram of a grinding wheel. 
       FIG. 2  is a diagram of a grinding wheel system. 
       FIG. 3  is a diagram of sensor placement on a grinding wheel. 
       FIG. 4  is a diagram of a sensor. 
       FIG. 5  is a diagram of a force sensor. 
       FIG. 6  is a graph of force sensor response. 
       FIGS. 7A–7D  are diagrams of vibrational patterns routinely experienced by a wheel. 
       FIG. 8  is a diagram of an adapter disk and a dummy disk attached to a wheel. 
       FIG. 9  is a diagram of adapter disk electronics. 
       FIG. 10  is a diagram of printed circuit board layers used to provide the adapter disk electronics. 
       FIG. 11  is a flow-chart of processing performed by the adapter disk electronics and a data processing platform. 
       FIG. 12  is a flow-chart of a sensor calibration process. 
       FIG. 13  is a flow-chart of adapter disk electronics data processing. 
       FIG. 14  is a flow-chart of a process for determining a normal force based on a sensor response. 
       FIG. 15  is a diagram of a data processing platform. 
       FIG. 16  is a flowchart of data processing performed by the data processing platform. 
       FIG. 17  is a flowchart of a process for determining wheel roundness. 
       FIG. 18  is a flowchart of a process for determining wheel surface roughness. 
       FIG. 19  is a flowchart of a process for determining wheel dullness. 
       FIG. 20  is a flowchart of a process for determining a wheel grinding mode. 
       FIG. 21  is a flowchart of a process for determining wheel chatter. 
       FIG. 22  is a diagram illustrating tangential forces that act on a wheel. 
       FIG. 23  is a flowchart of a process for determining tangential wheel forces. 
       FIG. 24  is a diagram of an multiple adaptive neuro-fuzzy inference system (MANFIS). 
       FIG. 25  is a diagram of an adaptive neuro-fuzzy inference system (ANFIS). 
       FIGS. 26A–26C  are screenshots of a graphical user interface used to display wheel characteristics. 
   

   DETAILED DESCRIPTION 
   Referring to  FIG. 2 , a grinding wheel system  16  includes a grinding wheel core  18  (e.g., a reusable aluminum core) coated by an abrasive material  12 . The wheel core  18  includes embedded sensors (see e.g.,  FIG. 3 ) such as force and/or acoustic emission (AE) sensors. A removable adapter disk  24  attached to the wheel core  18  houses electronics that process sensor signals and transmit (e.g., via wireless transmission) the processed signals to the data processing platform  25  for analysis. A removable dummy disk  26  balances the weight of adapter disk  24 . As shown, shaft  32  supported by fork  30  rotates grinding wheel  18 . Rotation of wheel  18  may be electronically controlled by adapter disk  24  or data processing platform  25 . Other systems of shafts and forks can be used. 
   Aspects of this system are described in S. Pathare, R. Gao, B. Varghese, C. Guo, and S. Malkin, “A DSP-Based Telemetric Data Acquisition System for In-Process Monitoring of Grinding Operation,” I.E.E.E. Instrumentation and Measurement Technology Conference, May 1998; S. Malkin, R. Gao, C. Guo, B. Varghese, and S. Pathare, “Development of an Intelligent Grinding Wheel for In-Process Monitoring of Ceramic Grinding”, Semi-Annual Report #1, May 1997, available on-line at http://www.doe.gov/bridge. 
   Sensor Construction and Placement 
   Referring to  FIG. 3 , the wheel core  18  includes sensors such as force detection sensors  20   a – 20   k  (force sensors) and acoustic emission (AE) sensors  22   a – 22   d.  As shown, the core  18  includes eleven force sensors  20   a – 20   k  and four AE sensors  22   a – 22   d  symmetrically positioned about the core  18 . As shown, the wheel core  18  also includes bolt  44   a – 44   k  and dowel pin  46   a – 46   b  openings to permit the adapter disk  24  and the dummy disk  26  to sandwich the wheel core  18 . 
   A core  18  may have different numbers of force and AE sensors than the number shown. Additionally, the sensors need not have a symmetrical configuration, although a symmetrical configuration offers certain advantages discussed below. The use of both force  20   a – 20   k  and AE sensors  22   a – 22   d  permit data processing platform  25  to monitor a wide variety of wheel characteristics. 
   Referring to  FIG. 4 , glue can be used to hold each sensor (e.g., force sensor  20 ) in a pre-machined slot in the core  18 . A sensor  20  terminal connects to metal foil  32  which may be mounted flush against the core  18  surface. An insulating seal  34  provides a conductive strip  36  that electrically transmits a charge developed on sensor  20  in response to forces or acoustic emissions to adapter disk  24  electronics. A wide variety of other methods of embedding or affixing sensors can be used. Other sensors such as strain gages and/or magnetoelastic sensors can also be used. 
   Although a wide variety of sensors can be used, sensors that respond to the piezoelectric effect (e.g., sensors having piezoceramic chips) respond to both wheel forces and acoustic emissions. Sensor responses in the MHz range correspond to acoustic emissions. Responses in the ten to hundred kHz range represent dynamic forces. By using electronic filters, a sensor&#39;s response can be easily divided into force and acoustic emission components. 
   Referring to  FIG. 5 , a force sensor  20  produces a charge proportional to the impulsive stress waves generated when abrasive  12  grains interact with the workpiece  14 . Referring also to  FIG. 6 , sensor response  20  rapidly diminishes as the sensor rotates away from the point  38  where the abrasive  12  and workpiece  14  meet. That is, the amplitude of the force signal measured depends on the angle θ formed between a vertical line at the point  38  the abrasive material  12  and the workpiece  14  meet and a normal line determined by the outer curvature of the core  18  where the sensor  20  resides. 
   Referring again to  FIG. 3 , the wheel core  18  includes eleven force sensors  20   a – 20   k  symmetrically positioned around the wheel periphery to detect surface forces. As described below, the number and position of the sensors can be determined based on different factors. The proximity of these peripheral force sensors  20   a – 20   k  to the grinding surface increases their sensitivity to forces produced by the interaction between the wheel  18  and workpiece  14 . 
   The number of force sensors  20   a – 20   k  included in a wheel core  18  depends on a variety of factors such as wheel dimensions, rotational speed, the configuration of the abrasive material, sensor dimensions, the complexity of data processing electronics, and space restrictions. For example, abrasive materials  12  can be glued to the wheel core  18  in twenty-two adjoining sections. The number of force sensors  20   a – 20   k  may be a multiple or fraction of the sections to maintain symmetrical sensor arrangement and avoid discontinuity between sections. 
   The position of force sensors  20   a – 20   k  depends on sensitivity requirements, sensor overload protection, and angular coverage. For increased sensitivity, the force sensors  20   a – 20   k  are sandwiched between the wheel core  18  and the abrasive material  12 . Due to the high rigidity of the wheel core  18 , the orientation of a force sensor  20   a – 20   k  with respect to the wheel periphery does not have any measurable effect on the sensor&#39;s  20   a – 20   k  angular range of coverage. To protect the force sensors  20   a – 20   k , a two-component epoxy (e.g., Araldite AV1258 with hardener HV1258) can be used to attach the abrasive material  12  to the core  34 . 
   As shown in  FIG. 3 , the wheel core  18  includes AE sensors  22   a – 22   d  positioned near the wheel inner rim  40 . Acoustic emission sensors  22   a – 22   d  can be used to triangulate acoustic emissions produced by structural imperfections (e.g., microscopic cracks) in a wheel core  18 . Triangulation requires a minimum of three AE sensors, e.g.,  22   a – 22   c , to pinpoint a source of an acoustic emission. As shown, the core  18  includes a fourth AE sensor  22   d  for redundancy and increased measurement accuracy. 
   Referring to  FIGS. 7A–7D , locating AE sensors  22   a – 22   d  near the wheel rim  40  minimizes noise caused by the normal vibrational behavior of the wheel core  18 .  FIGS. 7A–7D , show four modes (i.e., harmonic vibration response) of a wheel core  18  in normal operation. The modes produce acoustic pressure maxima and minima  42   a – 42   d  near the center of the wheel core  18  annular region. These shapes  42   a – 42   d  represent areas where AE sensors  22   a – 22   d  function poorly. Thus, AE sensors  22   a – 22   d  are located near the wheel rim  40  as shown in  FIG. 3 . 
   Wheel Electronics 
   Referring to  FIG. 8 , a removable adapter disk  24  and removable dummy disk  26  fit around the wheel core&#39;s inner rim  40 . Bolts (e.g., bolt  44 ) and dowel pins  46   a – 46   b  secure adapter disk  24  and dummy disk  26  to the wheel core  18 . The adapter disk  24  holds electronics (e.g., transmitter, power supply, and a Digital Signal Processor (DSP)) that process sensor  20   a – 20   k ,  22   a – 22   d  information in a disk cavity  48 . The dummy disk  26  offers an identical mass distribution as the adapter disk  24  to maintain wheel symmetry and balance. The adapter disk  24  enables sensor signal processing to occur at the wheel core  18  with minimal structural modification of the wheel core  18 . The adapter disk  24  also facilitates easy access and maintenance of the electronics. That is, an operator can modify and/or update the electronics to measure other wheel-related parameters without dismounting the wheel  18 . Additionally, during maintenance or modification of the electronics, an operator can continue to use the wheel core  18  for conventional grinding. The modular design also offers operators the flexibility of using the same measurement electronics for a variety of wheels using different abrasives or having different thickness and/or widths. 
   Referring to  FIG. 9 , electronics  50  process signals (e.g. electrical charges) produced by the force  20   a – 20   n  and AE sensors  22   a – 22   n . The electronics  50  can be embedded in the wheel core  18  or housed in adapter disk  24 . As shown, the electronics  50  include an analog multiplexer  56  that selects between different sensors  20   a – 20   k ,  22   a – 22   d,  a charge amplifier  58  that transforms a sensor charge into a voltage, an anti-aliasing filter  60 , an analog-to-digital (A/D) converter  62 , a DSP  64  that performs filtering and other data processing tasks, and a transmitter/receiver  62 . Other implementations use different architectures. For example, a very basic implementation does not use a DSP  64  at all, but instead directly transmits the analog signal of each sensor  20   a – 20   k,    22   a – 22   d  to the data processing platform  25 . Such a system can place a heavy burden on the data processing platform  25  by transmitting such a large volume of information. The use of a DSP  64 , as shown, permits real-time signal processing at the wheel in addition to selective transmission of gathered information. 
   The electronics&#39;  50  architecture shown offers an efficient system powered by a compact, lightweight J size 6-V battery  52 . Diode protective circuitry  54   a – 54   f  connected to the input of each multiplexer  56  prevent damage due to high voltages from the piezoceramic sensors. The diodes  54   a – 54   f  offer high speeds and low reverse leakage currents. In addition, their low parasitic capacitance helps preserve signal quality. 
   Sensor input signals feed an analog multiplexer  56  (e.g., an ADG608). Channel selection is achieved by using a data latch configured as an output port of the DSP  64 . The multiplexer  56  shown requires a supply current of 0.1 uA with a channel switching time of 100 ns. 
   Multiplexing the sensor signals makes it possible to use a single charge amplifier  58 , anti-aliasing filter  60 , and A/D converter  62  to process the force  20   a – 20   n  and AE sensors  22   a – 22   n . The use of a single set of electronic components minimizes the influence of component variations (e.g., amplifier gain) on signals. 
   Charge amplifier  58  converts a sensor&#39;s electrical charge to a voltage signal proportional to either the amplitude of the applied forces or the acoustic emission. A high-speed operational amplifier (e.g., an AD-822) is configured as a charge amplifier  58 . The lower cut-off frequency (f L ) of the charge amplifier  58  is set to 25 Hz by proper choice of the feed-back resistor (R 1 ) and capacitor (C 1 ), as given by:
 
 f   L =1/(2 πR   1   C   1 )  [1]
 
   Considering a time constant of the charge amplifier that is ten times as long, the lowest wheel rotational speed required for distortion-free force measurement is approximately 50 revolutions per minute (RPM). This number is much lower than that typically required for wheel preparation and/or grinding. Therefore, the charge amplifier  58  can accurately measure force and AE signals at the low frequency end. 
   The charge amplifier  58  also needs to respond fast enough to capture sensor signals. For this purpose, the highest frequency component of force signals is calculated by considering that as the point of contact  38  sweeps past a force sensor, a force impulse (T) is generated whose duration is related to the peripheral wheel velocity v s  by:
 
 T=w/v   s   [2]
 
where w is the width of the sensor and v s  is the velocity of the wheel perimeter. Thus, a wheel velocity of 60 m/s and sensor width of 3 mm, T=50 us. This corresponds to a signal frequency of about 20 kHz. Because AE signals are typically an order of magnitude higher, the highest signal frequency that needs to be processed by the charge amplifier is expected to be 500 kHz. The AD-822&#39;s bandwidth of 1.8 MHz can easily handle this range of frequencies. For input signal attenuation, the charge amplifier  58  is preceded by a capacitive charge attenuator. The transfer function of the charge attenuator-charge amplifier block is given by: 
               V   ⁡     (   s   )       =       Q   ⁡     (   s   )       ·       A   ⁡     (   s   )         [       A   ⁡     (   s   )       +   1     ]       ·       sR   1       [         sR   1     ⁢     C   1       +   1     ]       ·       C   2       [       C   2     +     C   3       ]                 (   3   )             
 
   The charge, amplifier  58  is followed by a four-pole anti-aliasing filter  60 . The anti-aliasing filter  60  is designed using a high-precision, high band-width (300 MHz), current feedback amplifier AD-8011  60  having a cut-off frequency of 1 MHz. Compared to voltage feedback amplifiers, current feedback amplifiers do not suffer from speed limitations due to stray capacitance and internal transistor cut-off frequencies, and, hence, are inherently faster and cover a larger bandwidth. 
   The anti-aliasing filter  60  feeds an A/D converter  62 . The A/D converter  62  (e.g., AD-9223) has a resolution of 12 bits and can make three millions samples per second. The sampling rate was chosen to meet the Nyquist criterion for sampling signals with a bandwidth of 1 MHz. The A/D converter  62  has an on-chip voltage reference and separate power supply pins for the analog and digital sections. The analog and digital power supplies are decoupled using high value capacitors mounted near the supply input pins (not shown) A tri-state buffer and latch buffer the digitized output of the A/D converter  62 . A separate clock chip clocks the A/D converter  62  and communicates with the DSP  64  in interrupt mode. A flat ribbon connector (FRC) connects the output of the A/D  62  to the DSP  64 . 
   The DSP  64  analyzes the digitized sensor signals to remove noise and identify force and acoustic emission information. The DSP  64  analyzes the spectral characteristics of the signals in addition to their time domain behavior by performing wavelet analysis of the signals. Wavelet analysis preserves both the frequency and time domain information of a signal and allows simultaneous extraction of high and low frequency signals with different frequency resolutions. A conventional FFT (Fast Fourier Transformation) may also be used to analyze a signal. 
   As shown, the DSP  64  may be a TMS320C52, manufactured by Texas Instruments. The algorithms that implement wavelet analysis and other transforms are often computationally demanding. The RISC-based architecture (Reduced Instruction Set Computer) of a DSP  64  enables efficient computation of large amount of data for the multiple sensors. The DSP  64  shown includes multiple internal data buses and DARAM (dual access RAM) which enables simultaneous addition and multiplication operations. The DSP  64  shown is a sixteen-bit, fixed-point digital signal processor offering a low supply voltage requirement (3 V), multiple on-chip serial ports (3 ports), high speed calculation capability (100 MIPS), and a small package size compared to other floating-point DSPs. The DSP  64  also offers a large amount of on-chip RAM (32 kBytes), eliminating the need for external RAM and reducing the amount of space used,by the electronics. Other implementations may use a microcontroller or microprocessor to perform the functions of DSP  64 . 
   A transmitter/receiver  66  handles data transmission between the DSP  64  and the data processing platform  25 . As shown, the transmitter/receiver  66  is an RF transmitter. RF transmission may be carried out in the 900 MHz FCC license-free ISM (Industrial, Scientific and Medical) band. In one implementation, the RF transmitter  66  is a single-chip hybrid IC that uses amplitude modulation in an on-off keying mode and is capable of operating at 3V. The antenna of the RF transmitter can be mounted flush on the outer surface of the adapter disk  24 . 
   The data can be compressed and can be transmitted in digital or analog form. Compression can be configured to keep dominant frequencies while suppressing lesser ones. Digital transmission makes efficient use of the bandwidth, since the RF bandwidth for signal transmission has little relation with that of the base band. Additionally, error correction mechanisms of digital transmission permit optimum utilization of transmission power, making low power transmission possible. Further, digital transmission allows for easy time multiplexing to accommodate input signals from multiple sensors. Digital transmission also makes it possible to use multiple transmitters and receivers within the same frequency band by means of TDMA (Time Division Multiple Access) without introducing much complexity in the transmitter/receiver hardware. 
   The electronics  50  are fitted (of  FIG. 9 ) into an adapter disk  24  or into the wheel core  18  using a multi-layer design to reduce system noise and save space. As shown, in  FIG. 10  a six-layered PCB board (Printed Circuit Board)  68  holds electronics  50  in a shape constructed to fit within adapter disk  24 . Four layers (e.g.,  68   b – 68   e ) are used as signal processing layers and two layers hold power supply rails for Vcc and Ground (e.g.,  68   a ,  68   f ). Separating the processing layers  68   b – 68   e  from the power supply layers  68   a ,  68   f  significantly reduces the length of connection tracks between individual layers. The two dedicated power supply layers  68   a ,  68   f  also provide a ripple-free voltage supply to the circuitry. The design reduces cross-talk and other electronic interference. Appendix A includes detailed schematics of one possible implementation. A wide variety of other techniques may be used to shield the electronics from interference and noise (e.g., foil shielding). 
   Referring to  FIG. 11 , sensor responses  72  are processed by wheel electronics (e.g., electronics  50  in adapter disk  24 ) (step  74 ) before transmission (step  76 ) to the data processing platform for further processing (step  78 ). Although in this description computational processes areas performed by the wheel electronics  50  (step  74 ) or by the data processing platform  25  (step  78 ), other implementations can be used to distribute data processing functions differently. 
   For example, referring to  FIG. 12 , due to potential variations in construction, different sensors may produce different responses to the same force. For example, a first force sensor may report a different peak charge in response to a 100 lb. load than a second force sensor bearing the same load. A calibration process  100  calibrates the different sensors to prevent differences from distorting subsequent analyses. This calibration process can be performed either by the electronics  50  in adapter disk  24  or by the data processing platform  25 . Calibration may include applying a known load (e.g., 100 lbs) to a wheel (step  102 ) and determining and storing the response of each sensor to the load (step  104 ). Such calibration can be repeated using different loads to determine a characteristic response curve. Thereafter, each sensor signal processed can be normalized based on the stored calibration data. 
   Referring to  FIG. 13 , as shown, the electronics  50  process  74  signals (x 1 (t), x 2 (t), x 3 (t)) from sensors  20   a – 20   k  and  22   a – 22   d  prior to transmission to the data processing platform  25  (step  76 ). The DSP  64  first selects a channel (i.e., a sensor) to process (step  106 ). A signal (e.g, a charge) of the selected sensor is digitized (step  108 ) (e.g., by the charge amplifier  58  and A/D converter  62 ) prior to DSP  64  analysis. 
   The DSP  64  continually determines the wheel&#39;s rotational speed (step  110 ). This value is needed in subsequent computations to accurately determine the force represented by a sensor signal. One method of determining wheel speed uses a low-pass filter to measure the duration between peak sensor pulses. This duration corresponds to the time it takes a sensor to make one full rotation about the wheel. Another method analyzes a signal in the frequency domain to find the most dominant frequency which corresponds to the RPM. Both methods can be used together to double-check RPM calculations. 
   As shown in  FIG. 13 , the wheel&#39;s rotational speed (i.e., RPM) is used to determine the signal amplitude produced by a force sensor (steps  112   a,    112   b,    114 ) (x low [n]). Referring also to  FIG. 14 , once the RPM is known (step  110 ), the signal from the force sensor of interest is recorded over a predefined period of time (e.g., a few seconds). The signal is then windowed such that only the portion which occurs when the sensor passes over the contact point  38  is kept. The windowed signal is then band-limited to 30 kHz (step  112   a ) and passed through a bandpass filter (step  112   b ). The bandpass filter used for this purpose is tuned by the wheel RPM data (e.g., the filter&#39;s output is made proportional to the measured force amplitude). Calculation errors are reduced by averaging the force value for the number of rotations of the wheel. The maximum measured force value is determined (step  114 ). The normal force for the each force sensor is obtained (nf[i]) and a normal force vector (nf) is formed and included in a formatted transmission message (step  122 ) along with the measured RPM value. 
   The DSP  64  processes AE sensor signals using a high-pass filter (step  116 ) to identify the high-frequency AE components. The DSP  64  may then use wavelet analysis, FFT, or other transforms to determine the frequency-domain response of a sensor (step  118 ) (X high [n]). The DSP  64  compresses (e.g., zips) the sensor data (step  120 ) (X comp ) for inclusion in the formatted transmission message (step  122 ). 
   The Data Processing Platform 
   Referring to  FIG. 15 , a data processing platform  25  (e.g., a standard PC or PC-compatible computer) includes a display  130 , a keyboard  132 , a pointing device  134  such as a mouse, and a digital computer  138 . The digital computer  138  includes memory  140 , a processor  142 , a mass storage device  144   a,  and other customary components such as a memory bus and peripheral bus (not shown). The platform  25  further includes a transmitter/receiver  136 . The transmitter/receiver  136  may be a single-chip hybrid RF device interfaced to the serial port of the platform  25 . 
   Mass storage device  144   a  can include operating system (e.g., Microsoft Windows 95™) instructions  146  and data processing instructions  78 . Data processing instructions  78  can be transferred to memory  140  and processor  142  in the course of operation. The data processing instructions  78  can cause the display  130  and input devices  132  and  134  to provide a user interface such as a graphical user interface  150  ( FIGS. 26A–26C ). Data processing instructions  78  can be stored on a variety of mass storage devices such as a floppy disk  144   b,  CD-ROM  144   c,  or PROM (not shown). 
   Referring to  FIG. 16 , after receiving data (step  76 ), the instructions  78  unformat (step  154 ) and decompress (step  156 )—(e.g., unzip) a received message into its components (e.g., RPM, normal force vector nf, and frequency information X comp ) Multi-resolution analysis (step  158 ) studies the signal at different frequency and time resolutions to recognize distinct patterns in the input signal. The data processing instructions  78  may use the data to monitor a variety of grinding phenomenon (step  160 ), such as roundness (step  162 ), wheel dullness (step  164 ), grinding mode (step  170 ), and/or chatter (step  172 ). Instructions  160  may also produce estimations of tangential force (step  166 ), surface roughness (step  168 ), and/or other grinding parameters (step  174 ) such as temperature. 
   Many wheel characteristics can be determined by comparing the output of different sensors collected at substantially the same time. For example, referring to  FIG. 17 , instructions  162  may determine wheel roundness by collecting nearly contemporaneous force measurements from different force sensors (step  176 ). If not performed by the DSP  64  prior to transmission, the instructions  162  may normalize the collected measurements based on sensor calibration data (step  178 ). In a round wheel, each sensor should report nearly equal normalized force measurements. The instructions  162  compare the different normalized measurements using a configurable threshold (step  180 ). Based on the comparison, the instructions  162  can determine whether the wheel is round (step  184 ) or misshapened (i.e., “out-of-round”) (step  182 ). The instructions  162  can also produce a value indicating a degree of roundness (step  183 ) instead of simply producing a binary round/not-round determination. 
   Referring to  FIG. 18 , instructions  168  can use a similar technique to determine wheel surface roughness. When a wheel becomes rough, different force sensors produce different-normalized force values. Again, by comparing substantially contemporaneously collected force values for different sensors (step  186 ), normalizing these values (step  188 ), and comparing the normalized values (step  190 ), the instructions  168  can determine whether a wheel is smooth (step  192 ) or exhibits varying degrees of roughness (steps  193 ,  194 ). 
   Referring to  FIG. 19 , a variety of wheel characteristics can also be determined by comparing the measurements of a sensor or sensors at different times. For example, as shown in  FIG. 19 , instructions  164  determine whether a wheel has dulled by comparing (step  200 ) a sensor&#39;s force measurement at a first time (step  196 ) with a force measurement of the same sensor at a second time (step  198 ). As a wheel dulls, the forces exerted on each sensor tend to increase due to greater friction. Thus, if, over time, a sensor reports an increase in force, the instructions  164  may determine the wheel is becoming increasingly dull (steps  203 ,  204 ). 
   One characteristic of a wheel is its grinding mode. For example, a wheel may be grinding a workpiece  14  in a continuous manner (e.g., ductile grinding) and/or by displacing discrete chunks at non-periodic intervals (e.g., brittle grinding). As shown in  FIG. 20 , by collecting force values produced by different sensors at different time periods (step  206 ) and determining the rate of change in these values (step  208 ), the instructions  170  can determine whether a wheel is grinding workpiece  14  in a ductile (step  210 ) or brittle (step  212 ) manner or some combination thereof (step  211 ). 
   Referring to  FIG. 21 , instructions  172  may also determine the degree of chatter a wheel experiences by comparing sensor responses collected at different time periods. In one technique, the responses of an AE sensor (or sensors) are collected at different time periods (step  214 ) and the signals are analyzed in the frequency domain (step  216 ). In the frequency domain, chatter appears as a strong frequency outside the bandwidth typically produced by a non-chattering wheel. By comparing the AE signals from different time periods in the frequency domain (step  216 ), frequencies corresponding to chattering can be detected (steps  219 ,  220 ). 
   Referring to  FIG. 22 , grinding produces a tangential force upon a wheel&#39;s surface  12 . As shown, a sensor  20  at point a on the wheel surface  12  experiences a tangential force  224 . An angle, α, is formed from a normal  221  formed by the curvature of the wheel at point a and a normal  222  formed by the curvature of the wheel at point b ( 38 ). The tangential force at point a equals (sine(α)×the force reported by sensor  20  at point b). 
   Referring to  FIG. 23 , the relationship described above is only one method of determining the tangential force. Instructions  166  can use one of these methods to compute the tangential force experienced by a portion of the wheel surface  12 . The instructions  166  may use depth of cut  226 , RPM  228 , and wheel geometry  230  (e.g., diameter) information to compute the tangential force (step  234 ) based on collected force sensor values (step  232 ). 
   Referring to  FIG. 24 , a Multiple Adaptive Neuro-Fuzzy Inference System (MANFIS)  160   a  can be employed to efficiently analyze sensor data. A MANFIS  160   a  is a collection of several Adaptive Neuro-Fuzzy Inference System (ANFISs) software networks  238   a – 238   n , each of which is trained to recognize a particular feature (e.g., roundness, grinding mode, estimated tangential force, surface roughness, and grinding temperature). Typically, each ANFIS  238   a – 238   n  will have three inputs, which include the normal force X, acoustic emission information Y, and the grinding conditions Z (e.g., previously determined information). 
   Referring to  FIG. 25 , an ANFIS  238  includes a network of different communicating software layers  240 – 248 . In a first layer  240 , each input is spanned by a set of membership functions  250   a – 250   f . A set of weights in layers  242  and  244  and node functions in layer  246  link the membership functions  250   a – 250   f  to an output layer  248 . A particularly suitable membership function for the ANFIS architecture is the generalized bell membership function described by three parameters: 
               μ   ⁢           ⁢   Ai     =     1     1   +              x   -   c     a            2   ⁢   b                   [   4   ]             
 
where μAi is the membership function value computed for an input value x, for particular values of parameters a, b and c (called premise parameters). The input (e.g., force x) is spanned by a set of these membership functions. For example, as shown, a set of two membership functions  250   a – 250   b  span the force input. Thus, layer  240  has two outputs for force which are fed further into the network. Similar layer  240  outputs are obtained for other inputs.
 
   Layer  242  sums the outputs from layer  240  and multiplies the sums by weights w i . Layer  244  sums the outputs of layer  242  and multiplies these outputs by normalized weights w i  such that: 
                 W   _     i     =       W   i       Σ   ⁢           ⁢     W   i                 [   5   ]             
 
In layer  246  outputs from layer  244  are combined using linear models  264   a – 264   b.  The output for each node  264   a – 264   b  in layer  246  can be described as:
 
 f   i   =p   i   X+q   i   Y+r   i   Z+s   i   [6]
 
where f i  is the node output for particular values of parameters p i , q i , r i  and s i . These parameters are called consequent parameters and are determined by training as described below. Finally, ANFIS  238  output is obtained as a combination of each output as:
 
 f=w   1   f   1   +w   2   f   2   +w   3   f   3   [7]
 
Thus, if normal force vector (X), high frequency content (Y) and machining condition (Z) are given, the above network can produce an output for specified values of weights, premise and consequent parameters.
 
   The procedure of finding the optimized parameters and weights is called ANFIS  238  training. This training involves determining a number of membership functions, values for weights, premise and consequent parameters such that the network can predict the outputs accurately. In other words, training enables the ANFIS  238  to recognize certain patterns in the input signal and accordingly predicts the most appropriate output. 
   Training can be performed by presenting the network  238  with a set of inputs having known outputs. The parameters and weights can then be adjusted so that output predicted by the ANFIS  238  matches the known output values. The set of known input-output values used to train the ANFIS  238  is called the training data set. The training data set can be formed from data collected by the grinding wheel system  16  in parallel with a calibrated, wired data acquisition system on the grinding machine. The data collected by the grinding wheel system  16  forms the input set for training, while the data collected from the calibrated, wired system forms the known output. The calibrated, wired system includes a force dynamometer to determine normal and tangential forces, a power transducer and thermocouples, together with measurements of geometric wheel form (wheel roundness, waviness etc.), and wheel surface topology. The training data can be used to train each individual ANFIS  238  of the inference system  236 . The optimized values of both the premise and consequent parameters obtained after training the MANFIS  236  in this manner is used for real-time monitoring of wheel preparation and the grinding process. 
   The instructions  160  may be used to implement a MANFIS  236  which collects different inference systems  238   a – 238   n  trained to recognize different wheel characteristics. The system  236  combines the power of neural networks capable of recognizing patterns with fuzzy logic which facilitates easy description of inputs and outputs. The system  236  can be trained both on-line and off-line. On-line training enables the system to recognize a new grinding phenomenon in any “new” environment (e.g, a new workpiece material, a new grinding wheel core, or a new grinding wheel abrasive). Further, the individual inference systems  238   a – 238   n  may share information with each other making them co-active adaptive inference systems. 
   Referring to  FIGS. 26A–26C , a graphical user interface  150   a – 150   c  provides operators with graphic representations of grinding wheel characteristics. The interface  150   a – 150   c  screens shown are merely exemplary. As shown in  FIG. 23A , the interface  150   a  may display wheel specifications  284  (e.g., the type of abrasive material, wheel diameter, and width) and grinding conditions  282  (e.g., the workpiece material). The interface  150   a  may also display other wheel characteristics such as wheel speed  270 , dullness  272 , roundness  274 , and the grinding mode  278 . The interface  150   a  also permits an operator to specify a file  280  to store data collected during a grinding session for further analysis. 
   Referring to  FIG. 26B , the interface  150   b  may also display the force  288  or acoustic emission  286  measurements made by different wheel sensors. Referring to  FIG. 26C , the interface  150   c  may also indicate wheel characteristics such as tangential force  294 , temperature  296 , spindle power  292 , and surface roughness  290 . 
   Implementation 
   The invention can be implemented in hardware or software, or a combination of both. The programs should be designed to execute on programmable computers each comprising a processor, a data storage system (including memory and/or storage elements), at least one input device, and at least one output device, such as a CRT or printer. Program code is applied to input data to perform the functions described herein and generate output information. The output information is applied to one or more output devices such as a CRT, as described herein. 
   Each program is preferably implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the programs can be implemented in assembly or machine language, if desired. In any case, the language can be a compiled or interpreted language. 
   Each such computer program is preferably stored on a storage medium or device (e.g., ROM or magnetic diskette) readable by a general or special purpose programmable computer, for configuring and operating the computer when the storage media or device is read by the computer to perform the procedures described herein. The system can also be considered to be implemented as a computer-readable storage medium, configured with a computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner to perform the functions described herein. 
   Other Embodiments 
   It is to be understood that while the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.