- Email: [email protected]
Intelligent materials processing
Robotics & Computer.Integrated
~Ianu]acturtng. ',ot. 3. No. 2. pp. I a 1 - 1 4 5 , t987
!)73~-55z5 87 $3.00 - 0.00 Pergamon Journals Ltd
Pnnted in G r eat Britain.
• Paper
INTELLIGENT MATERIALS PROCESSING BRUCE K R A M E R a n d H A R O L D LIEBOWITZ School of Engineering and Applied Science, George Washington University, Washington, DC, U.S.A. Current applications of knowledge-based expert systems in manufacturing involve decision-making that is based on information that is readily interpretable. In process control, the relationship between the underlying process behavior and the sensed information is not usually well understood. In addition, the required response speed of the system can be quite short; from a few milliseconds to a few seconds. The dual requirements for fast decisions and the incorporation of "fuzzy" knowledge requires new approaches to the development of expert systems for process control. Both advanced, fast executing artificial intelligence algorithms and improved, fundamental physical understanding of the underlying manufacturing processes that are being controlled are necessary. It is important to emphasize that neither the artificial intelligence nor the manufacturing engineering communities currently have the tools and techniques that are required to solve the full range of process control problems. It is essential that communication and cooperation between the two disciplines be increased to give a full appreciation for the current strengths and limitations of the respective science bases and to define a clear direction for advanced, fundamental research within each discipline.
systems to process control, however, it is important to remember that the performance of process expert systems increases dramatically as the formulation of the system behavior approaches a deterministic physical law. This improved performance is required for real time process control (see Fig. 1) and is a consequence of the fact that fundamental physical laws express experience with minimum information. Therefore, the speed and accuracy of the inferences which can be made by an expert or supervisory system from a given data structure improves as the rigor of the available formulation of fundamental process understanding increases. The resulting decrease in computational load allows a correspondingly more exhaustive search of the data structure and increased predictive capability. Thus, basic process research is an important requirement for the development of advanced expert systems for process control. The lack of integration of process understanding into automated manufacturing systems may be attributed to three main causes: • It is not widely appreciated that the level of process understanding that was sufficient to allow efficient manufacturing with human supervision is completely inadequate to perform the identical operations in an automated context. Most computerintegrated systems (CIMS) have been designed and developed from the top down, as computer systems,
INTRODUCTION The essence of intelligent manufacturing is the integration of the manufacturing science base into the manufacturing system software. Today's manufacturing systems may display intelligence in part scheduling and in the generation of programs for the production of parts using numerical control. However, these systems incorporate essentially no process understanding. The system software is employed to drive peripheral devices: the machine tools, transport, assembly and inspection devices of a computer-integrated factory. Since the software control of the system does not extend to the level of the actual processes that are employed to produce useful parts, the critical manufacturing process runs open loop, with no supervisory control from the CAD/ CAM system. The key to improving reliability and repeatability is to extend the level of software control further down the process loop in order to attain t r u e process automation. This need is generic and results from a lack of basic understanding of the underlying physical processes that are employed in manufacturing. THE NEED FOR IMPROVED PROCESS
KNOWLEDGE Expert systems have the potential to revolutionize the supervision and control of Computer-Integrated Manufacturing Systems (CIMS). In applying expert 141
142
Robotics & Computer-Integrated Manufacturing • Volume 3, Number 2, 1987 10 years 1 year I month information flow 1 day
1 hour 1 minute
t Geometrical behavior t
I
Plant design & prototyping Machine selection System simulation Product design (CAD), Process design, Fixture design, Production planning. NC programming Factor.,,' operation: part scheduling part shipping and receiving part transport part warehousing part inspection Part handling: loading/unloading assembly
I second Complex process behavior
Machine control Intelligent machines Process control
+
Limited understanding
3_
1 msec Fig. 1. Manufacturing control spectrum (after Cook)
by programmers. This has engendered a "black box" approach to the machine tools and/or processing equipment in the system which have, accordingly, been modelled as ideal elements which receive instructions from the system and perform their functions flawlessly. In the case of computerintegrated machining, the most widely applied CIMS technology today, this idealization is valid only for production speeds that are conservative relative to conventional production speeds, where the operator performs a fault monitoring and correction function. Therefore automated factories, involving massive capital investments, where the actual production processes are performed more slowly than those in identically equipped conventional plants. This contradiction is largely responsible for the questionable economics of the CIM concept today. • In general, the analysis of materials processing is complex. This is due, in part, to the fact that materials processing involves dramatic transformations in the shape, phase and/or chemical composition in the raw material that is being processed. This situation may be contrasted with assembly (see Fig. 2), where the geometries of the parts which are being assembled do not change during assembly and the dimensions of the component parts are known within well-defined limits. The precise knowledge of
the shapes of the component parts and the elastic nature of the force interactions among them allow a geometrical interpretation of assembly force signals (from a robot end-effector, for example) in terms of the orientation and degree of assembly mismatch. In contrast, the accurate modelling of materials processing requires the application of the most advanced analytical and experimental techniques by specialists. These methods are not easily generalized to allow for process design or modification by relatively novice analysts. Therefore, the manufacturing knowledge base is codified in a collection of guidelines, rules and approximate formulas which apply over a limited and, often, poorly defined set of Assembly
vs.
Materials processing
Fixed geometry
Changing geometry
Shapes of mating parts are precisely defined
Starting shape may be poorly defined
Fixed boundary conditions/coulomb friction
Changing boundary conditions/flow stress limited
Linear, elastic force interactions between elements
Highly non-linear force interactions
Fig. 2. Analysis of materials processing is complex
143
intelligent materials processing • B. KRAMER and H. LIEBOWITZ
operation conditions. As a result, it is essential that process engineers and artificial intelligence experts collaborate both in incorporating the existing guidelines into expert systems and in refining the current state of understanding to allow for the accurate interpretation of the system status in terms of the underlying physical laws that dictate the material behavior. • The time spectrum that is relevant to process control extends from the relatively long times over which process guidelines are applied to the relatively short times, on the order of less than a second, over which process instabilities may develop. Therefore, the range of application of a given process control strategy is determined not only by the appropriateness of the decisions and/or corrections that are commanded by the system but also by the speed with which the control action can be taken. Manual production techniques use established guidelines as a point of departure for selecting process variables and allow for the experimental optimization of the process under the local conditions of each particular operation. However once set, the processing parameters are normally fixed for a particular operation and are not adjusted to optimize with respect to variations in properties within a batch of parts a n d / o r tools or to compensate for changes in the local environmental conditions. These variations typically occur over many production cycles and the programming techniques that are employed in conventional expert systems have clear application to the automation of the decision-making processes that are involved in tool design and selection and in the adaptive optimization of process speeds. High level AI systems are generally too computationally and memory intensive to provide sufficiently fast response to take corrective action for variations which occur within a given production cycle. The problem of system stabilization over short time periods has traditionally been the domain of control theory. However, most efforts to implement true process control have been hampered by an inadequate description of the "plant", in this case, the physical process that is under control. In the past, many researchers have hoped that adequate process models could be developed on-line, by the statistical analysis of sensor signals, without recourse to detailed mechanistic models. Although this approach was a reasonable one to attempt, the results to date have not been encouraging. To combine the best aspects of expert systems and conventional process control techniques, it is desirable to employ a computationally efficient algorithm
which incorporates the best available process model, including any applicable empirical expert knowledge, and which is capable of adapting the internal process model in response to the information derived from system sensors. Advanced Bayesian statistical control techniques allow the incorporation of all of these features and may potentially be adapted to provide for on-line fault detection and adaptive process optimization in manufacturing processes. Three strategies are compared in Fig. 3.
Expert system Computational requirements High
Bayesian statistical model
Deterministic control law
Moderate Low
Incorporation of physical models possible
Yes
Yes
Yes
Incorporation of expert opinion possible
Yes
Yes
No
Stability with respect to unmodelled system behavior (higher order nonlinearities)
Absolute Excellent
Poor
Fig. 3. Decision and control strategies
While problems of unpredictable quality and poor yield due to poor process control affect the metalworking, robotics, polymer processing, composites and semiconductor industries, the reliable automation of the machining process in computerintegrated machining systems must be viewed as a top priority. Industry, worldwide, has invested heavily in these systems and process control software is desperately needed to improve their economics. I N T E L L I G E N T CONTROL OF T H E MACHINING PROCESS Computer-Integrated Machining technology is absolutely key to the production, availability, and maintainability of the high precision, complex mechanical and optomechanical devices that are employed in modern technology. No manufacturing process has been studied more than machining. Tens of billions of dollars have been invested by machine builders and machine tool users in developing and installing flexible machining systems worldwide. This massive effort has resulted in the commercial availability of a broad and impressive range of capital equipment for the implementation of computer-integrated machining systems. It is sobering to note that today's commercial systems have few additional capabilities as compared to the
I44
Robotics & Computer-Integrated Manufacturing • '/oIume 3, Number 2, [987
Variable Mission Machining System that was demonstrated in the early 1970s. It is equally sobering to note that the world is following the lead of the Japanese in reducing machining speeds in the unmanned factory to improve the system reliability. This trend is largely responsible for the questionable economics of the " u n m a n n e d maching" concept today and represents a de facto admission that no rational algorithms exist for interpreting system sensor signals in terms of system status over the broad range of machining conditions. There is a general concensus concerning the key problems in automating the machining process: • Tool end life prediction. • Machine failure prediction. • 100% reliable chipbreaking. We are pursuing a multidisciplinary program at George Washington University to address these key problems. In each case, the results of advanced process modelling techniques are being combined with AI and Bayesian statistical formalisms to produce a range of software that is applicable to the problems of machining automation. The brief discussions which follow give an indication of the applicable modelling techniques and the expected application of the results.
TOOL WEAR MODELLING/LIFE PREDICTION Accurate tool life estimation is important to improving the productivity of current computer-integrated machining systems. At the present time, extreme variability in tool life is experienced, with the result that all tools must be removed from service before the shortest-lived tool is expected to fail. This places a severe limitation on machining speed and the effect of the limitation is felt most strongly when system utilization is maximized. Thus, as other aspects of the CIM are perfected, the tool end-life estimation problem becomes more critical. We have developed an analytical model that predicts the wear rates of tool materials as a function of cutting speed from the chemical properties and hardnesses of the tool and workpiece as a function of temperature. This model has been implemented in Lisp on a V A X 11/780 computer and is being employed as a tool material design and selection tool. We are investigating the possibility of using the model to estimate the time of tool failure from in-process measurement of the machining temperature through the use of Bayesian statistical techniques. The Bayesian approach has many advantages over traditional strategies for the control of manufactur-
ing processes in that the formalism allows for the almost unlimited adaptation of an initial "best available" process model that is based on analysis and expert opinion in changing process conditions, as indicated bv the system sensors. In many ways, the statistical approach avoids the disadvantages and combines the strengths of both conventional control strategies and expert systems in process control. Like an expert system, the Bayesian technique is capable of dealing with highly nonlinear systems with many relevant and ill-defined variables. Although a relatively computationally intensive control strategy, the Bayesian algorithms are still much faster than conventional expert systems. Bayesian statistics incorporate both adaptive modelling and forecasting. The statistical model employs the best available analytical model as a prior model and modifies the prior on the basis of experience as provided by system sensors to produce a posterior model with improved conformity to the system behavior. The value of the Bayesian discriminator at a given time indicates the current state of the machining process within calculated confidence limits. A forecasting capability is introduced by employing Kalman filtering and an adaptive estimation procedure to predict the value of the process variable vector at future time periods. If, for instance, an evaluation of the Bayesian discriminator using the projected process variables implies a high probability of tool failure or of continuous chip formation, then the machining conditions can be modified before the workpiece or tooling are damaged. It is important to note that the availability of an accurate analytical model for the tool wear process would also allow the first accurate simulations of tool usage prior to the machining of test parts. Such a simulation should prove invaluable in the selection of the optimum number of tool compositions and in tool inventory control in the automated factory. SMART SENSORS FOR .MACHINE C O N D I T I O N MONITORING " S m a r t " sensing involves the interpretation of multiple sensor inputs to predict the system state (see Fig. 4). This is an appropriate strategy for monitoring of machine tool condition since it is not immediately obvious which parameters will be most relevant to the prediction of machine tool failure. Since an exceedingly large number of combinations and weightings of sensor inputs may conceivably be used to sense imminent failure, principal component analysis is being applied across the range or sensors. Such an analysis identifies combinations of sensor
Intelligent materials processing • B. KRASIER and H. LIEBOV,ITZ i. Multiple sensor inputs: acoustic emis>ion vibration spindle power surface finish 2. Principal component analysis: identifies functionally independent combinations of sensor inputs to reduce processing requirements Fig. 4. Smart sensing of machine tool condition
outputs that are functionally independent and identifies the relative contribution of each to the overall system stability. The resulting statistical model can then be incorporated into a Bayesian discriminator to monitor the system state and schedule machine maintenance when the predicted probability of machine failure exceeds a threshold level. MODELLING OF CHIP FORMATION The process of chip formation is too complex to yield a physically useful analytical model. Since 100% reliable chip-breaking is essential to the successful implementation of unmanned machining, we are modelling the chip formation process using finite
la5
element methods. Our model will be employed in developing an expert system for the design of improved chip-breaking geometries for tools and in specifying the optimum constitutive behavior for free-machining allovs, to dynamically control process conditions to insure consistent chip breakage. While the finite element analysis is expected to be a useful design tool. it is also necessary to detect the failure of the chips to break very quickly so that the process conditions can be modified before the workpiece is damaged by the continuous chip. Therefore, principal component analysis will also be applied to the chip formation problem.
CONCLUSIONS The number of available expert systems for manufacturing process control is limited. This is largely due to a lack of sufficiently accurate process models and the need for real time response to changes in process conditions. Close cooperation will be required between scientists in artificial intelligence and in manufacturing to overcome the theoretical problems that are obstacles to the implementation of broadly applicable expert systems for process control.
~Ianu]acturtng. ',ot. 3. No. 2. pp. I a 1 - 1 4 5 , t987
!)73~-55z5 87 $3.00 - 0.00 Pergamon Journals Ltd
Pnnted in G r eat Britain.
• Paper
INTELLIGENT MATERIALS PROCESSING BRUCE K R A M E R a n d H A R O L D LIEBOWITZ School of Engineering and Applied Science, George Washington University, Washington, DC, U.S.A. Current applications of knowledge-based expert systems in manufacturing involve decision-making that is based on information that is readily interpretable. In process control, the relationship between the underlying process behavior and the sensed information is not usually well understood. In addition, the required response speed of the system can be quite short; from a few milliseconds to a few seconds. The dual requirements for fast decisions and the incorporation of "fuzzy" knowledge requires new approaches to the development of expert systems for process control. Both advanced, fast executing artificial intelligence algorithms and improved, fundamental physical understanding of the underlying manufacturing processes that are being controlled are necessary. It is important to emphasize that neither the artificial intelligence nor the manufacturing engineering communities currently have the tools and techniques that are required to solve the full range of process control problems. It is essential that communication and cooperation between the two disciplines be increased to give a full appreciation for the current strengths and limitations of the respective science bases and to define a clear direction for advanced, fundamental research within each discipline.
systems to process control, however, it is important to remember that the performance of process expert systems increases dramatically as the formulation of the system behavior approaches a deterministic physical law. This improved performance is required for real time process control (see Fig. 1) and is a consequence of the fact that fundamental physical laws express experience with minimum information. Therefore, the speed and accuracy of the inferences which can be made by an expert or supervisory system from a given data structure improves as the rigor of the available formulation of fundamental process understanding increases. The resulting decrease in computational load allows a correspondingly more exhaustive search of the data structure and increased predictive capability. Thus, basic process research is an important requirement for the development of advanced expert systems for process control. The lack of integration of process understanding into automated manufacturing systems may be attributed to three main causes: • It is not widely appreciated that the level of process understanding that was sufficient to allow efficient manufacturing with human supervision is completely inadequate to perform the identical operations in an automated context. Most computerintegrated systems (CIMS) have been designed and developed from the top down, as computer systems,
INTRODUCTION The essence of intelligent manufacturing is the integration of the manufacturing science base into the manufacturing system software. Today's manufacturing systems may display intelligence in part scheduling and in the generation of programs for the production of parts using numerical control. However, these systems incorporate essentially no process understanding. The system software is employed to drive peripheral devices: the machine tools, transport, assembly and inspection devices of a computer-integrated factory. Since the software control of the system does not extend to the level of the actual processes that are employed to produce useful parts, the critical manufacturing process runs open loop, with no supervisory control from the CAD/ CAM system. The key to improving reliability and repeatability is to extend the level of software control further down the process loop in order to attain t r u e process automation. This need is generic and results from a lack of basic understanding of the underlying physical processes that are employed in manufacturing. THE NEED FOR IMPROVED PROCESS
KNOWLEDGE Expert systems have the potential to revolutionize the supervision and control of Computer-Integrated Manufacturing Systems (CIMS). In applying expert 141
142
Robotics & Computer-Integrated Manufacturing • Volume 3, Number 2, 1987 10 years 1 year I month information flow 1 day
1 hour 1 minute
t Geometrical behavior t
I
Plant design & prototyping Machine selection System simulation Product design (CAD), Process design, Fixture design, Production planning. NC programming Factor.,,' operation: part scheduling part shipping and receiving part transport part warehousing part inspection Part handling: loading/unloading assembly
I second Complex process behavior
Machine control Intelligent machines Process control
+
Limited understanding
3_
1 msec Fig. 1. Manufacturing control spectrum (after Cook)
by programmers. This has engendered a "black box" approach to the machine tools and/or processing equipment in the system which have, accordingly, been modelled as ideal elements which receive instructions from the system and perform their functions flawlessly. In the case of computerintegrated machining, the most widely applied CIMS technology today, this idealization is valid only for production speeds that are conservative relative to conventional production speeds, where the operator performs a fault monitoring and correction function. Therefore automated factories, involving massive capital investments, where the actual production processes are performed more slowly than those in identically equipped conventional plants. This contradiction is largely responsible for the questionable economics of the CIM concept today. • In general, the analysis of materials processing is complex. This is due, in part, to the fact that materials processing involves dramatic transformations in the shape, phase and/or chemical composition in the raw material that is being processed. This situation may be contrasted with assembly (see Fig. 2), where the geometries of the parts which are being assembled do not change during assembly and the dimensions of the component parts are known within well-defined limits. The precise knowledge of
the shapes of the component parts and the elastic nature of the force interactions among them allow a geometrical interpretation of assembly force signals (from a robot end-effector, for example) in terms of the orientation and degree of assembly mismatch. In contrast, the accurate modelling of materials processing requires the application of the most advanced analytical and experimental techniques by specialists. These methods are not easily generalized to allow for process design or modification by relatively novice analysts. Therefore, the manufacturing knowledge base is codified in a collection of guidelines, rules and approximate formulas which apply over a limited and, often, poorly defined set of Assembly
vs.
Materials processing
Fixed geometry
Changing geometry
Shapes of mating parts are precisely defined
Starting shape may be poorly defined
Fixed boundary conditions/coulomb friction
Changing boundary conditions/flow stress limited
Linear, elastic force interactions between elements
Highly non-linear force interactions
Fig. 2. Analysis of materials processing is complex
143
intelligent materials processing • B. KRAMER and H. LIEBOWITZ
operation conditions. As a result, it is essential that process engineers and artificial intelligence experts collaborate both in incorporating the existing guidelines into expert systems and in refining the current state of understanding to allow for the accurate interpretation of the system status in terms of the underlying physical laws that dictate the material behavior. • The time spectrum that is relevant to process control extends from the relatively long times over which process guidelines are applied to the relatively short times, on the order of less than a second, over which process instabilities may develop. Therefore, the range of application of a given process control strategy is determined not only by the appropriateness of the decisions and/or corrections that are commanded by the system but also by the speed with which the control action can be taken. Manual production techniques use established guidelines as a point of departure for selecting process variables and allow for the experimental optimization of the process under the local conditions of each particular operation. However once set, the processing parameters are normally fixed for a particular operation and are not adjusted to optimize with respect to variations in properties within a batch of parts a n d / o r tools or to compensate for changes in the local environmental conditions. These variations typically occur over many production cycles and the programming techniques that are employed in conventional expert systems have clear application to the automation of the decision-making processes that are involved in tool design and selection and in the adaptive optimization of process speeds. High level AI systems are generally too computationally and memory intensive to provide sufficiently fast response to take corrective action for variations which occur within a given production cycle. The problem of system stabilization over short time periods has traditionally been the domain of control theory. However, most efforts to implement true process control have been hampered by an inadequate description of the "plant", in this case, the physical process that is under control. In the past, many researchers have hoped that adequate process models could be developed on-line, by the statistical analysis of sensor signals, without recourse to detailed mechanistic models. Although this approach was a reasonable one to attempt, the results to date have not been encouraging. To combine the best aspects of expert systems and conventional process control techniques, it is desirable to employ a computationally efficient algorithm
which incorporates the best available process model, including any applicable empirical expert knowledge, and which is capable of adapting the internal process model in response to the information derived from system sensors. Advanced Bayesian statistical control techniques allow the incorporation of all of these features and may potentially be adapted to provide for on-line fault detection and adaptive process optimization in manufacturing processes. Three strategies are compared in Fig. 3.
Expert system Computational requirements High
Bayesian statistical model
Deterministic control law
Moderate Low
Incorporation of physical models possible
Yes
Yes
Yes
Incorporation of expert opinion possible
Yes
Yes
No
Stability with respect to unmodelled system behavior (higher order nonlinearities)
Absolute Excellent
Poor
Fig. 3. Decision and control strategies
While problems of unpredictable quality and poor yield due to poor process control affect the metalworking, robotics, polymer processing, composites and semiconductor industries, the reliable automation of the machining process in computerintegrated machining systems must be viewed as a top priority. Industry, worldwide, has invested heavily in these systems and process control software is desperately needed to improve their economics. I N T E L L I G E N T CONTROL OF T H E MACHINING PROCESS Computer-Integrated Machining technology is absolutely key to the production, availability, and maintainability of the high precision, complex mechanical and optomechanical devices that are employed in modern technology. No manufacturing process has been studied more than machining. Tens of billions of dollars have been invested by machine builders and machine tool users in developing and installing flexible machining systems worldwide. This massive effort has resulted in the commercial availability of a broad and impressive range of capital equipment for the implementation of computer-integrated machining systems. It is sobering to note that today's commercial systems have few additional capabilities as compared to the
I44
Robotics & Computer-Integrated Manufacturing • '/oIume 3, Number 2, [987
Variable Mission Machining System that was demonstrated in the early 1970s. It is equally sobering to note that the world is following the lead of the Japanese in reducing machining speeds in the unmanned factory to improve the system reliability. This trend is largely responsible for the questionable economics of the " u n m a n n e d maching" concept today and represents a de facto admission that no rational algorithms exist for interpreting system sensor signals in terms of system status over the broad range of machining conditions. There is a general concensus concerning the key problems in automating the machining process: • Tool end life prediction. • Machine failure prediction. • 100% reliable chipbreaking. We are pursuing a multidisciplinary program at George Washington University to address these key problems. In each case, the results of advanced process modelling techniques are being combined with AI and Bayesian statistical formalisms to produce a range of software that is applicable to the problems of machining automation. The brief discussions which follow give an indication of the applicable modelling techniques and the expected application of the results.
TOOL WEAR MODELLING/LIFE PREDICTION Accurate tool life estimation is important to improving the productivity of current computer-integrated machining systems. At the present time, extreme variability in tool life is experienced, with the result that all tools must be removed from service before the shortest-lived tool is expected to fail. This places a severe limitation on machining speed and the effect of the limitation is felt most strongly when system utilization is maximized. Thus, as other aspects of the CIM are perfected, the tool end-life estimation problem becomes more critical. We have developed an analytical model that predicts the wear rates of tool materials as a function of cutting speed from the chemical properties and hardnesses of the tool and workpiece as a function of temperature. This model has been implemented in Lisp on a V A X 11/780 computer and is being employed as a tool material design and selection tool. We are investigating the possibility of using the model to estimate the time of tool failure from in-process measurement of the machining temperature through the use of Bayesian statistical techniques. The Bayesian approach has many advantages over traditional strategies for the control of manufactur-
ing processes in that the formalism allows for the almost unlimited adaptation of an initial "best available" process model that is based on analysis and expert opinion in changing process conditions, as indicated bv the system sensors. In many ways, the statistical approach avoids the disadvantages and combines the strengths of both conventional control strategies and expert systems in process control. Like an expert system, the Bayesian technique is capable of dealing with highly nonlinear systems with many relevant and ill-defined variables. Although a relatively computationally intensive control strategy, the Bayesian algorithms are still much faster than conventional expert systems. Bayesian statistics incorporate both adaptive modelling and forecasting. The statistical model employs the best available analytical model as a prior model and modifies the prior on the basis of experience as provided by system sensors to produce a posterior model with improved conformity to the system behavior. The value of the Bayesian discriminator at a given time indicates the current state of the machining process within calculated confidence limits. A forecasting capability is introduced by employing Kalman filtering and an adaptive estimation procedure to predict the value of the process variable vector at future time periods. If, for instance, an evaluation of the Bayesian discriminator using the projected process variables implies a high probability of tool failure or of continuous chip formation, then the machining conditions can be modified before the workpiece or tooling are damaged. It is important to note that the availability of an accurate analytical model for the tool wear process would also allow the first accurate simulations of tool usage prior to the machining of test parts. Such a simulation should prove invaluable in the selection of the optimum number of tool compositions and in tool inventory control in the automated factory. SMART SENSORS FOR .MACHINE C O N D I T I O N MONITORING " S m a r t " sensing involves the interpretation of multiple sensor inputs to predict the system state (see Fig. 4). This is an appropriate strategy for monitoring of machine tool condition since it is not immediately obvious which parameters will be most relevant to the prediction of machine tool failure. Since an exceedingly large number of combinations and weightings of sensor inputs may conceivably be used to sense imminent failure, principal component analysis is being applied across the range or sensors. Such an analysis identifies combinations of sensor
Intelligent materials processing • B. KRASIER and H. LIEBOV,ITZ i. Multiple sensor inputs: acoustic emis>ion vibration spindle power surface finish 2. Principal component analysis: identifies functionally independent combinations of sensor inputs to reduce processing requirements Fig. 4. Smart sensing of machine tool condition
outputs that are functionally independent and identifies the relative contribution of each to the overall system stability. The resulting statistical model can then be incorporated into a Bayesian discriminator to monitor the system state and schedule machine maintenance when the predicted probability of machine failure exceeds a threshold level. MODELLING OF CHIP FORMATION The process of chip formation is too complex to yield a physically useful analytical model. Since 100% reliable chip-breaking is essential to the successful implementation of unmanned machining, we are modelling the chip formation process using finite
la5
element methods. Our model will be employed in developing an expert system for the design of improved chip-breaking geometries for tools and in specifying the optimum constitutive behavior for free-machining allovs, to dynamically control process conditions to insure consistent chip breakage. While the finite element analysis is expected to be a useful design tool. it is also necessary to detect the failure of the chips to break very quickly so that the process conditions can be modified before the workpiece is damaged by the continuous chip. Therefore, principal component analysis will also be applied to the chip formation problem.
CONCLUSIONS The number of available expert systems for manufacturing process control is limited. This is largely due to a lack of sufficiently accurate process models and the need for real time response to changes in process conditions. Close cooperation will be required between scientists in artificial intelligence and in manufacturing to overcome the theoretical problems that are obstacles to the implementation of broadly applicable expert systems for process control.