- Email: [email protected]
An Expert System Approach to Optimization of the Centerless Grinding Process
An Expert System Approach to Optimization of the Centerless Grinding Process Sridharan Venk, Rakesh Govind; University of Cincinnati/USA - Submitted by M. Eugene Merchant (1) Received on January 11,1990 Expert systems can serve as a powerful tool for problem formulation that can lead to hiqher optimization efficiency of any process. By using expert systems to formulate a aroblem, the total lead time to complete optimization is greatly reduced. In this paper we have presented our approach to develop and use an expert system to accomplish problem formulation and aid optimization of the centerless grinding process. A detailed case study has been presented to illustrate the actual working of our approach. In one case a productivity improvement of 173% was observed over 40 optimization cycles. And in another case (presented in a suimnarized form) an improvement of 540% in the parts variable cost over 42 optimization cycles was realized. Key words:
optimization, expert system, knowledge base, grinding, centerless grinding
Introduction The task of carrying out grinding operations is an area that relies heavily on expert heuristic. The extreme sensitivity of the performance of the grinding process to its process variables requires a careful and quick analysis to be carried out prior to each decision being implemented. Thus expert systems appear to be a responsive tool suited to address problems in the performance of grinding in manufacturing. Therefore, we instituted a research project aimed at applying an expert system approach to optimization of the grinding process. However, we quickly found that in order to reap the benefits from a true optimization, it was important to classify the grinding process variables into various types depending on their importance to the main objective. This makes it possible to keep track of important variables while still monitoring the effect of less important variables on the main objective and important process constraints. Hahn [l] has highlighted the need for setting up grinding parameter data bases which explain the input/output relationship between the process variables. Centerless grinding is a popular and widely used process for external grinding of small precision components. The process has a high capability-to-cost ratio [2]. However, a complete scientific basis for centerless grinding has not yet been established. Therefore, we chose the centerless grinding process for study in this research. The main objective of our research is to develop an approach of using expert systems to aid optimization of the centerless grinding process. Such a technique would involve two major steps. Step #I- To develop an expert system to carry out problem formulation in the domain of centerless grinding, and step #2- To carry out the actual optimization based on the problem formulated. In our optimization research, we have recognized and have tried to explore the concept of problem formulation, which seems to us to be an important link between qualitative understanding of the process and quantitative optimization. Basis for Grinding Knowledge We first proceeded to develop a knowledge base for the establishment of an expert system for the centerless grinding process. This contained both fundamental (basic) and expert knowledge related to grinding. For the fundamental knowledge we drew on the basic principles of grinding, based on experimental investigation, which have been generalized and published by Hahn and Lindsay [3], and Lindsay 141. Our knowledge base also included some specific expert information concerning roundness control [Z], cost analysis [5], and measurement of chatter marks [6] in grinding. Companies such as Cincinnati Milacron [7,8], Cincinnati, Ohio, and Metcut Research Associates, Cincinnati, Ohio, were also an expert source of grinding knowledge for this research. The CIRP Annals was an important source of grinding information for this research, as well. As for information specifically related to optimization of grinding processes, some guidance was sought through sources [ 9 ] , [lo], and [ll]. Organization of Grinding Knowledge We next proceeded to organize the knowledge in our data base in a manner suited to its application to optimization of the centerless grinding process, using Figure 1 as a guide. Figure 1 is based on the
reference Smits [ 7 ] . It represents an overall grinding system, consisting of three major branches namely, the machine tool, the grinding wheel, and the metal working fluid branch. Each branch has an associated list of variables. The list of variables under each branch is not complete, and as such may be extended through further research and development. From this we developed Figure 2, which shows a partial qualitative effect diagram of centerless grinding knowledge at a process level, as related to maximization of parts per hour (PPH). Figure 2 also provides the notation of various symbols used in this optimization research. The diagram aids in providing physical insight to the process. It could be developed for processes in relation to the various objective functions and the important process constraints. In Figure 2 the effects are mitigated along the direction of arrows. The arrow originates from the causal node, and terminates at the effect node. A "+" sign indicates that an increase (or decrease) in the causal variable will cause an increase (or decrease) in the effect variable. On a similar basis a ~ - nsign indicates that an increase (or decrease) in the value of causal variable will result in a decrease (or increase) in the effect
etc
etc
etc I
E -
Fig 1: Overall Grinding System [71 variable. As such there may be more variables involved in the actual process. The process level is divided into four stages (in Figure 2 stages 1 through 4). In reality there can be an even higher number of stages. The idea behind using stages is to be able to track the nost fundamental variables. In this research the fundamental variables are regarded as those variables which the operator can adjust/change to tune-up the process. Selection of E x p e r t System Shell At this stage we carried out an expert system shell survey, and discovered PCPlus (a Texas Instruments based expert system shell)[:2!o be the
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goal can be instantiated for initial set-up, fault diagnosis, remedy, or a list of variables required under a problem formulation. An example of a rule ( I 001) is as follows: IF :: Check-Reql = Objective AND Obj-selc = "Parts Per Hour1tAND Conc-Selc = % a m b d a conceptn AND Opt-Selc = *'Input variables." THEN :: OUT-MAC = (Textname PPH-General) The snIF"part checks for user requirements, and the IfTHENI' part makes conclusions. In this case the user requirement is "Objectives1 selection, and the objective selected is "Parts Per Hour.Il The user further indicates that the problem formulation is to be based on the IILambda Concept," and that he is interested in knowing the list of "Input Variables." The system instantiates the variable "PPH-General" to goal OUT-MAC. The value of variable PPH-General is in the form of a textag that the system recommends to the user. The parameters Check-Reql and Opt-Selc are also contained in the frame OBJ-SEW. I I
I
I
1
DDI: Motablrq~~vedper parLhm/pPt
--poClsblegt--c clmobgtptkndondefhed
Fig 2
:
A Partial Qualitative Effect Diagam for the O r i K hg Process as ralated to PPH
most suitable shell for our application purpose. PCPlus combines a frame and rule based approach for knowledge representation. The use of frames aids division and sub division of knowledge, and enables focussing on a particular problem. Rules perform the actual reasoning under the umbrella of frames. PCPlus also has several other capabilities that aid efficient knowledge representation. For further details regarding PCPlus refer to the users manual [121. Structure of Knowledge Base We next proceeded to structure our knowledge base for use in an expert system. The nature of the specific knowledge base structure used in the optimization research being presented in this paper is illustrated in the following example: Each frame has its associated properties. The frame OW-SEL (used for carrying out objective selection) has the following properties: Frame DroDerties
I
I
guthor's comment OUTMAC is a goal to be fulfilled
Display results: : YES
Results are to be displayed
Promptlnd:: Would you like to try a new set of response?
Query posed to the user before he exits the frame
Promptever:: Under this frame you are provided a list of business/ Technical objectives
Information provided to the user each time the frame OUTHAC is invoked.
.
Identifier: : rrObj-Sel-'l Frame identifier for system internal purpose only.
*
Value of textag 8*PPH-Genera111 : The objective selected by the user is "Parts Per Hour.s8 The following are the associated input variables based on the "Lambda Concept" of grinding: 1. Regulating wheel speed (Vr), in m/ml rqtype-1, mode-H. 2. Grinding wheel speed (Vg), in m/miq type-1, mode- H. 3. Dress Lead (L), in mm/rev., tvpe-1, mode-H. 4. Dress Compensation (C), in mm, type-1, mode-H
.
TeXtagS form an important element of problem formulation. The basis and information contained by the textags incorporated in our knowledge base will be discussed in the following sections of this paper. Problem Formulation in General Having discussed an example of knowledge base structure, we present our general problem formulation approach that forms the basis of our knowledge base. The optimization research discussed in this paper primarily tries to investigate the effect of four input variables, namely the "Regulating wheel speed (Vr),I* "Grinding wheel speed (Vg),I1 "Dress lead (L),la and the "Dress compensation (C)" on productivity (parts per hour). In this research it is assumed that all the other input variables (that may exist due to machine variations) will be set at practical optimum levels by means of previous optimization studies (not discussed in this paper). It is also assumed that the grinding process can be tuned up for maximum productivity (parts per hour) by adjusting mainly the four input variables mentioned above. The expert system can carry out problem formulation using either the "General Concept," or the "Lambda Concept" of grinding [ 3 , 4 ] . As per the lambda concept the work removal parameter (WRP) and wheel wear parameter (WWP) are based primarily on the wheel dressing parameter (L and C) , workpiece hardness (HRC), grinding wheel speed (Vg), and the force intensity (FN1). The general concept differs from the lambda concept in the sense that it does not consider the individual machine stiffness variations in finding the work removal/wheel wear parameters. However the general concept is based on considering all grinding times, which include all the time involved in completing the grinding operation. The lambda concept does not consider grinding times. Formulation of the Specific Problem To accomplish formulation of the specific problem, two frames namely, the objective selection (OW-SEL) frame, and the constraint selection (CON-SEL) frame, are used. The user selects his objective using the O m - S E L frame, and constraints using the CON-SEL frame. Both the frames lead to a list of input and output variables. The input/output variable list contains information regarding the type of variable and their units. Figure 3 shows the type classification structure incorporated within the expert system. The most fundamental variables are classified as "type-ln (in Figure 2, stage 4 contains the fundamental variables). Further, type-1 variables can be adjusted by an operator controlling the process. The variables which are currently not under the operator control, but are however affecting the objective function or process constraints, are type classified as "type-2." These types of variables are important as often they are subject to further investigation in the event they exhibit significant effect on the main objective function or constraints. The type-0 variables are declared as global factors (considered to remain constant during the
1. Truly adjustable 2. Parameter some what adjustable 3. Cannot be adjusted
I Cf"-l
1
II
Oefne m e
wTm-cfudamentd c(xIsBS
,
/OPTION3
IS it a variable
Is this parameter
Type4
Type-2
Type-u
information, such as setting of initial regions to start optimization (to minimize the optimization time), and setting of constraints. Table 1 is a result of adding quantitative information to the case study as represented in Figure 4. It is a report with both the qualitative and quantitative infomation necessary to carry out actual optimization of the process.1n Figure 4, whereever as large a value as possible was desired, a large numerical value (not practical to achieve) was assigned to the higher constraint limit. The actual optimization was carried out using Ultramax [14], which is a sequential parametric It incorporates a hill optimization package. climbing strategy through fitting Taylor's second degree model to locate the practical optimum. The Ultramax user's guide indicates that a correct problem formulation will show patterns of improvement in the process within 4N cycles, where )INn is the number of input variables in the problem formulation (4 in thisMAXMZATEN OF PPH
to be changed according to prescribed rules NO
1 (ruled variable)I
Type-I
I(controlled or decision variable)
Fig. 3: Type Classification for Input Variables optimization study). There are two types of output variable used in this research, namely type-6 for an output variable that is an objective function, and type-5 for all other output variables other than the objective function. There are three types of variable modes (see column labeled ttMll in Table 1) considered in this research, namely, mode H for hand set variables, mode C for calculated variables, and mode A for variables that attain value as a result of actual running of the process.
3L 4 c 5Rn
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mn/mV
mn
0.05 0.03
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an
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0.2 0.2 0.8
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6 C 2 5 35 zo a Q u.03 6.58 (m3*m5)min5 c 0.03 ~0.07 10 FNl (NIM d m 5 A m.s ns.2 s.01 06.00 LIST OF QLOBAL FACTORS T : Type QWl)144.8 W a k speed m/mh M : Mode
nun
--
---
(3hw12) 0.51 €@dent Qmeter. m H : Hand set MwAL(3) 3.5 ead vdune c : CdaMed O W 4 1 0.051 Drcmetsr of whd, m A : A (K0EAL(5) 80. -W -H Hw:
case Study So far we have discussed our approach in developing
an expert system to carry out problem formulation. We shall now take a classical productivity improvement problem and illustrate the working of the expert system. The output reports generated by the expert system will be used for the purpose of illustration. The problem is concerned with maximization of parts per hour (PPH) subjected to a surface finish (Ra) constraint. Figure 4, represents the result of problem formulation using the expert system. The actual steps needed to reach a final problem formulation as shown in Figure 4 are quite many, and as such involve a large amount of knowledge coding, far too great to detail here. However, the fundamental components and the method of knowledge representation are in accordance with the example discussed earlier in the section %tructure of Knowledge Base." For further details regarding the user-system interaction steps leading to final problem formulation as shown in Figure 4 refer to Venk 1131. Quantitative Optimization Following the problem formulation achieved using our expert system, we now proceed to carry out an actual optimization based on the case study characterized above. To carry out actual optimization of the process the problem formulation requires quantitative
Fig 4: A R d t of Roblem FomAabn
ushg the Expert System
491
related to the output variable l'PPH.'t In summary, it was found that near the region of the practical optimum the effects shown by input variables were mostly linear, with very little curvature effect. Overall, the model shape report indicated that (for the case study considered) the grinding wheel speed (Vg) has the greatest effect on parts per hour (PPH), with the regulating wheel speed (Vr) being second. Among the grinding wheel dressing variables, the variable dress lead (L) had a greater effect though not found to be significant as an overall on parts per hour (PPH), than did the variable dress compensation (C). Additional case study Having discussed our approach to apply expert systems to aid optimization (and the potential productivity improvement that can result from following such an approach) we will conclude by highlighting the result of a second case study illustrating the significant improvements that can be realized by aid of the expert system. This case
Provisions should be provided to alter the type classification depending on the specific problem.
*
Group all the input variables associated with each output variable. While grouping, care should be taken to group (as far as possible) the fundamental input variables. It is desirable to break the process into various stages leading to the fundamental stage (The word fundamental Stage refers to the stage containing fundamental input variables).
*
For each variable assign a general prior region and constraint value if any. This will depend on the process nature, machine and cutting tool type, and other machinability aspects.
Following the stage of problem formulation by Using the expert system, a standard optimization Software capable of locating practical optimum region could be used to actually optimize the process. Acknowledgement The authors express their thanks to Dr. Carlos Moreno, President, Ultramax corporation, Cincinnati, USA, for providing us with an opportunity to use their software Wltramax.*I Finally, we express our gratitude to Dr. Eugene Merchant, Director, MetCUt Research Associates, USA, for his guidance in organizing and sponsorship of this paper.
80 70
References
PBO P
L
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LO
20
'
~.
0
5
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-
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= 0.013 NITI
other arlput levels (at 82 PPH)
~
10
15
20
25
30
35
40
45
RUN NUMBER
Fig 5: Plot Report for PPH study concerned with minimization of the variable cost of parts produced by centerless grinding (parts variable cost). In this case an improvement of 540% was observed in the parts variable cost. The parts variable cost reduced from 0.032 $/unit to about 0.005 $/unit. In this case the productivity variable (PPH) was treated as an important business constraint (lower constraint = 30 PPH, and upper constraint is open). Some of the other constraints are as follows: Surface finish & 0.8 pin, power g 135 kw, force intensity 45551.2 N/m, and grinding ratio 3 1 5 . Conclusions Although the performance improvement obtainable by the aid of expert system has been illustrated by only two case study summaries in this paper, during this optimization research the authors have investigated many other cases, and have observed similar benefits in almost all of them. We conclude that expert systems can be used as a qualitative analysis (problem formulation) tool to aid actual optimization of the grinding process. Such usage can significantly minimize the lead time required to achieve practical optimum results, when compared with the conventional approach of manual problem formulation. Such expert systems can be used as a powerful productivity improvement tool, at both machine and personnel levels. The approach discussed in this paper could be applied to other areas involving problem formulation. In general the steps for expert system building to assist such can be summarized as follows:
*
Express the process environment using qualitative effect diagrams. This will provide physical insight (qualitative understanding) of the process.
*
Divide the process into the various problem aspects that occur within the process. Allocate a "frame*' for each problem aspect. Express the nature of each problem aspect using **paramnteru." Analysis of the problem within the problem aspects can then be carried out using a set of "rules. 'I For a given family of processes, the type classification for associated input and output variables can be built into the expert system.
492
Hahn, R.S., 1985, Computer Aided Grinding Process Planning, Milton C.Shaw Grinding symposium, ASHE winter Annual Meeting, Miami Beach, Florida, Nov. 17-22, PED Vo1 16, PP: 933. Bhateja, P.C., 1984, Current State of the Art for Workpiece Roundness Control in Precision Centerless Grinding, Annals of CIRP, Vol. 33/1/1984., PP: 199-203. Hahn, R.s., Lindsay, R.P., 1971, Principles of Grinding, Part-(I, 11, 111, IV, V), Machining, July, PP: 3-59. Lindsay, P.R., 1975, Principles of Grinding Four Years Later, SME paper NO. MR75-60,presented at SME's Abrasive Machining Conference, Sept. Field, M., Kegg, R., and Buescher, S., 1980, Computerized Cost Analysis of Grinding IDperations, Annals of CIRP, Vol. 29/1/1980, PP: 233-237. Sakai, Y., Ogata, S., and Asai, S., Optical Keasuring Instrument for Measuring Chatter larks, Annals of CIRP, Vol. 33/1/1984, PP: 407412. Smits, A.C., 1988, A Course in Centerless grinding Technology Process, Cincinnati Kilacron, Cincinnati, Ohio, USA. Cincinnati Milacron., Operating Manual for Cincinnati Milacron (220-8, 230-12, 330-15, 35020) Centerless Grinding Machine with Acramatic 725G Control Model AE, Publication NO. 1-GR84293, Part No. 3358979, Cincinnati Milacron Marketing Company, Cincinnati, Ohio 45209-9988, USA. Malkin, S., 1985, Practical Approaches to IGrinding Optimization, Milton C.Shaw Grinding Symposium ASME Winter Annual Meeting, Miami Beach, Florida, Nov. 17-22, PED Vol. 16, PP: 289-299. Malkin, S., Koran, Y . , 1980, Off-line Grinding with a Micro Computer, Annals of CIRP, Vol. 29/3/1980, PP: 213-216. Moreno, W.C., 1986, Self Learning Optimization Control Software, Proceedings of ROBEX'S6 (Robotics and Expert System), Instrument Society of America, ISA, June 4-16. Personal Consultant Plus (PCPlus), 1988, Users Manual, Texas Instruments Incorporated, 12501 Research Blvd., Austin, Texas 78769. USA. Venk, S., 1988, Development of an Expert System for Centerless Grinding and Integration for Process Optimization, Masters Thesis, Nov. 1988, University of Cincinnati, Cincinnati, Ohio 45221. Moreno, W.C., 1988, Ultramax User's Guide, Ultramax Corporation, 650 Northland Boulevard, Cincinnati, Ohio 45240, USA.
optimization, expert system, knowledge base, grinding, centerless grinding
Introduction The task of carrying out grinding operations is an area that relies heavily on expert heuristic. The extreme sensitivity of the performance of the grinding process to its process variables requires a careful and quick analysis to be carried out prior to each decision being implemented. Thus expert systems appear to be a responsive tool suited to address problems in the performance of grinding in manufacturing. Therefore, we instituted a research project aimed at applying an expert system approach to optimization of the grinding process. However, we quickly found that in order to reap the benefits from a true optimization, it was important to classify the grinding process variables into various types depending on their importance to the main objective. This makes it possible to keep track of important variables while still monitoring the effect of less important variables on the main objective and important process constraints. Hahn [l] has highlighted the need for setting up grinding parameter data bases which explain the input/output relationship between the process variables. Centerless grinding is a popular and widely used process for external grinding of small precision components. The process has a high capability-to-cost ratio [2]. However, a complete scientific basis for centerless grinding has not yet been established. Therefore, we chose the centerless grinding process for study in this research. The main objective of our research is to develop an approach of using expert systems to aid optimization of the centerless grinding process. Such a technique would involve two major steps. Step #I- To develop an expert system to carry out problem formulation in the domain of centerless grinding, and step #2- To carry out the actual optimization based on the problem formulated. In our optimization research, we have recognized and have tried to explore the concept of problem formulation, which seems to us to be an important link between qualitative understanding of the process and quantitative optimization. Basis for Grinding Knowledge We first proceeded to develop a knowledge base for the establishment of an expert system for the centerless grinding process. This contained both fundamental (basic) and expert knowledge related to grinding. For the fundamental knowledge we drew on the basic principles of grinding, based on experimental investigation, which have been generalized and published by Hahn and Lindsay [3], and Lindsay 141. Our knowledge base also included some specific expert information concerning roundness control [Z], cost analysis [5], and measurement of chatter marks [6] in grinding. Companies such as Cincinnati Milacron [7,8], Cincinnati, Ohio, and Metcut Research Associates, Cincinnati, Ohio, were also an expert source of grinding knowledge for this research. The CIRP Annals was an important source of grinding information for this research, as well. As for information specifically related to optimization of grinding processes, some guidance was sought through sources [ 9 ] , [lo], and [ll]. Organization of Grinding Knowledge We next proceeded to organize the knowledge in our data base in a manner suited to its application to optimization of the centerless grinding process, using Figure 1 as a guide. Figure 1 is based on the
reference Smits [ 7 ] . It represents an overall grinding system, consisting of three major branches namely, the machine tool, the grinding wheel, and the metal working fluid branch. Each branch has an associated list of variables. The list of variables under each branch is not complete, and as such may be extended through further research and development. From this we developed Figure 2, which shows a partial qualitative effect diagram of centerless grinding knowledge at a process level, as related to maximization of parts per hour (PPH). Figure 2 also provides the notation of various symbols used in this optimization research. The diagram aids in providing physical insight to the process. It could be developed for processes in relation to the various objective functions and the important process constraints. In Figure 2 the effects are mitigated along the direction of arrows. The arrow originates from the causal node, and terminates at the effect node. A "+" sign indicates that an increase (or decrease) in the causal variable will cause an increase (or decrease) in the effect variable. On a similar basis a ~ - nsign indicates that an increase (or decrease) in the value of causal variable will result in a decrease (or increase) in the effect
etc
etc
etc I
E -
Fig 1: Overall Grinding System [71 variable. As such there may be more variables involved in the actual process. The process level is divided into four stages (in Figure 2 stages 1 through 4). In reality there can be an even higher number of stages. The idea behind using stages is to be able to track the nost fundamental variables. In this research the fundamental variables are regarded as those variables which the operator can adjust/change to tune-up the process. Selection of E x p e r t System Shell At this stage we carried out an expert system shell survey, and discovered PCPlus (a Texas Instruments based expert system shell)[:2!o be the
Annals of the CIRP Vol. 3/l/lM
489
goal can be instantiated for initial set-up, fault diagnosis, remedy, or a list of variables required under a problem formulation. An example of a rule ( I 001) is as follows: IF :: Check-Reql = Objective AND Obj-selc = "Parts Per Hour1tAND Conc-Selc = % a m b d a conceptn AND Opt-Selc = *'Input variables." THEN :: OUT-MAC = (Textname PPH-General) The snIF"part checks for user requirements, and the IfTHENI' part makes conclusions. In this case the user requirement is "Objectives1 selection, and the objective selected is "Parts Per Hour.Il The user further indicates that the problem formulation is to be based on the IILambda Concept," and that he is interested in knowing the list of "Input Variables." The system instantiates the variable "PPH-General" to goal OUT-MAC. The value of variable PPH-General is in the form of a textag that the system recommends to the user. The parameters Check-Reql and Opt-Selc are also contained in the frame OBJ-SEW. I I
I
I
1
DDI: Motablrq~~vedper parLhm/pPt
--poClsblegt--c clmobgtptkndondefhed
Fig 2
:
A Partial Qualitative Effect Diagam for the O r i K hg Process as ralated to PPH
most suitable shell for our application purpose. PCPlus combines a frame and rule based approach for knowledge representation. The use of frames aids division and sub division of knowledge, and enables focussing on a particular problem. Rules perform the actual reasoning under the umbrella of frames. PCPlus also has several other capabilities that aid efficient knowledge representation. For further details regarding PCPlus refer to the users manual [121. Structure of Knowledge Base We next proceeded to structure our knowledge base for use in an expert system. The nature of the specific knowledge base structure used in the optimization research being presented in this paper is illustrated in the following example: Each frame has its associated properties. The frame OW-SEL (used for carrying out objective selection) has the following properties: Frame DroDerties
I
I
guthor's comment OUTMAC is a goal to be fulfilled
Display results: : YES
Results are to be displayed
Promptlnd:: Would you like to try a new set of response?
Query posed to the user before he exits the frame
Promptever:: Under this frame you are provided a list of business/ Technical objectives
Information provided to the user each time the frame OUTHAC is invoked.
.
Identifier: : rrObj-Sel-'l Frame identifier for system internal purpose only.
*
Value of textag 8*PPH-Genera111 : The objective selected by the user is "Parts Per Hour.s8 The following are the associated input variables based on the "Lambda Concept" of grinding: 1. Regulating wheel speed (Vr), in m/ml rqtype-1, mode-H. 2. Grinding wheel speed (Vg), in m/miq type-1, mode- H. 3. Dress Lead (L), in mm/rev., tvpe-1, mode-H. 4. Dress Compensation (C), in mm, type-1, mode-H
.
TeXtagS form an important element of problem formulation. The basis and information contained by the textags incorporated in our knowledge base will be discussed in the following sections of this paper. Problem Formulation in General Having discussed an example of knowledge base structure, we present our general problem formulation approach that forms the basis of our knowledge base. The optimization research discussed in this paper primarily tries to investigate the effect of four input variables, namely the "Regulating wheel speed (Vr),I* "Grinding wheel speed (Vg),I1 "Dress lead (L),la and the "Dress compensation (C)" on productivity (parts per hour). In this research it is assumed that all the other input variables (that may exist due to machine variations) will be set at practical optimum levels by means of previous optimization studies (not discussed in this paper). It is also assumed that the grinding process can be tuned up for maximum productivity (parts per hour) by adjusting mainly the four input variables mentioned above. The expert system can carry out problem formulation using either the "General Concept," or the "Lambda Concept" of grinding [ 3 , 4 ] . As per the lambda concept the work removal parameter (WRP) and wheel wear parameter (WWP) are based primarily on the wheel dressing parameter (L and C) , workpiece hardness (HRC), grinding wheel speed (Vg), and the force intensity (FN1). The general concept differs from the lambda concept in the sense that it does not consider the individual machine stiffness variations in finding the work removal/wheel wear parameters. However the general concept is based on considering all grinding times, which include all the time involved in completing the grinding operation. The lambda concept does not consider grinding times. Formulation of the Specific Problem To accomplish formulation of the specific problem, two frames namely, the objective selection (OW-SEL) frame, and the constraint selection (CON-SEL) frame, are used. The user selects his objective using the O m - S E L frame, and constraints using the CON-SEL frame. Both the frames lead to a list of input and output variables. The input/output variable list contains information regarding the type of variable and their units. Figure 3 shows the type classification structure incorporated within the expert system. The most fundamental variables are classified as "type-ln (in Figure 2, stage 4 contains the fundamental variables). Further, type-1 variables can be adjusted by an operator controlling the process. The variables which are currently not under the operator control, but are however affecting the objective function or process constraints, are type classified as "type-2." These types of variables are important as often they are subject to further investigation in the event they exhibit significant effect on the main objective function or constraints. The type-0 variables are declared as global factors (considered to remain constant during the
1. Truly adjustable 2. Parameter some what adjustable 3. Cannot be adjusted
I Cf"-l
1
II
Oefne m e
wTm-cfudamentd c(xIsBS
,
/OPTION3
IS it a variable
Is this parameter
Type4
Type-2
Type-u
information, such as setting of initial regions to start optimization (to minimize the optimization time), and setting of constraints. Table 1 is a result of adding quantitative information to the case study as represented in Figure 4. It is a report with both the qualitative and quantitative infomation necessary to carry out actual optimization of the process.1n Figure 4, whereever as large a value as possible was desired, a large numerical value (not practical to achieve) was assigned to the higher constraint limit. The actual optimization was carried out using Ultramax [14], which is a sequential parametric It incorporates a hill optimization package. climbing strategy through fitting Taylor's second degree model to locate the practical optimum. The Ultramax user's guide indicates that a correct problem formulation will show patterns of improvement in the process within 4N cycles, where )INn is the number of input variables in the problem formulation (4 in thisMAXMZATEN OF PPH
to be changed according to prescribed rules NO
1 (ruled variable)I
Type-I
I(controlled or decision variable)
Fig. 3: Type Classification for Input Variables optimization study). There are two types of output variable used in this research, namely type-6 for an output variable that is an objective function, and type-5 for all other output variables other than the objective function. There are three types of variable modes (see column labeled ttMll in Table 1) considered in this research, namely, mode H for hand set variables, mode C for calculated variables, and mode A for variables that attain value as a result of actual running of the process.
3L 4 c 5Rn
1 H 1 H
mn/mV
mn
0.05 0.03
5 c as1 m3* @ 5 C w4.0 ~ U 5L c 0.3 ~
7wwP 8Pl=+l
0.O 0.0 0.8
332.0
an
0.03
am 0.0 W.0 0.0
0.2 0.2 0.8
m3.0 221
6 C 2 5 35 zo a Q u.03 6.58 (m3*m5)min5 c 0.03 ~0.07 10 FNl (NIM d m 5 A m.s ns.2 s.01 06.00 LIST OF QLOBAL FACTORS T : Type QWl)144.8 W a k speed m/mh M : Mode
nun
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case Study So far we have discussed our approach in developing
an expert system to carry out problem formulation. We shall now take a classical productivity improvement problem and illustrate the working of the expert system. The output reports generated by the expert system will be used for the purpose of illustration. The problem is concerned with maximization of parts per hour (PPH) subjected to a surface finish (Ra) constraint. Figure 4, represents the result of problem formulation using the expert system. The actual steps needed to reach a final problem formulation as shown in Figure 4 are quite many, and as such involve a large amount of knowledge coding, far too great to detail here. However, the fundamental components and the method of knowledge representation are in accordance with the example discussed earlier in the section %tructure of Knowledge Base." For further details regarding the user-system interaction steps leading to final problem formulation as shown in Figure 4 refer to Venk 1131. Quantitative Optimization Following the problem formulation achieved using our expert system, we now proceed to carry out an actual optimization based on the case study characterized above. To carry out actual optimization of the process the problem formulation requires quantitative
Fig 4: A R d t of Roblem FomAabn
ushg the Expert System
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related to the output variable l'PPH.'t In summary, it was found that near the region of the practical optimum the effects shown by input variables were mostly linear, with very little curvature effect. Overall, the model shape report indicated that (for the case study considered) the grinding wheel speed (Vg) has the greatest effect on parts per hour (PPH), with the regulating wheel speed (Vr) being second. Among the grinding wheel dressing variables, the variable dress lead (L) had a greater effect though not found to be significant as an overall on parts per hour (PPH), than did the variable dress compensation (C). Additional case study Having discussed our approach to apply expert systems to aid optimization (and the potential productivity improvement that can result from following such an approach) we will conclude by highlighting the result of a second case study illustrating the significant improvements that can be realized by aid of the expert system. This case
Provisions should be provided to alter the type classification depending on the specific problem.
*
Group all the input variables associated with each output variable. While grouping, care should be taken to group (as far as possible) the fundamental input variables. It is desirable to break the process into various stages leading to the fundamental stage (The word fundamental Stage refers to the stage containing fundamental input variables).
*
For each variable assign a general prior region and constraint value if any. This will depend on the process nature, machine and cutting tool type, and other machinability aspects.
Following the stage of problem formulation by Using the expert system, a standard optimization Software capable of locating practical optimum region could be used to actually optimize the process. Acknowledgement The authors express their thanks to Dr. Carlos Moreno, President, Ultramax corporation, Cincinnati, USA, for providing us with an opportunity to use their software Wltramax.*I Finally, we express our gratitude to Dr. Eugene Merchant, Director, MetCUt Research Associates, USA, for his guidance in organizing and sponsorship of this paper.
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References
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Fig 5: Plot Report for PPH study concerned with minimization of the variable cost of parts produced by centerless grinding (parts variable cost). In this case an improvement of 540% was observed in the parts variable cost. The parts variable cost reduced from 0.032 $/unit to about 0.005 $/unit. In this case the productivity variable (PPH) was treated as an important business constraint (lower constraint = 30 PPH, and upper constraint is open). Some of the other constraints are as follows: Surface finish & 0.8 pin, power g 135 kw, force intensity 45551.2 N/m, and grinding ratio 3 1 5 . Conclusions Although the performance improvement obtainable by the aid of expert system has been illustrated by only two case study summaries in this paper, during this optimization research the authors have investigated many other cases, and have observed similar benefits in almost all of them. We conclude that expert systems can be used as a qualitative analysis (problem formulation) tool to aid actual optimization of the grinding process. Such usage can significantly minimize the lead time required to achieve practical optimum results, when compared with the conventional approach of manual problem formulation. Such expert systems can be used as a powerful productivity improvement tool, at both machine and personnel levels. The approach discussed in this paper could be applied to other areas involving problem formulation. In general the steps for expert system building to assist such can be summarized as follows:
*
Express the process environment using qualitative effect diagrams. This will provide physical insight (qualitative understanding) of the process.
*
Divide the process into the various problem aspects that occur within the process. Allocate a "frame*' for each problem aspect. Express the nature of each problem aspect using **paramnteru." Analysis of the problem within the problem aspects can then be carried out using a set of "rules. 'I For a given family of processes, the type classification for associated input and output variables can be built into the expert system.
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Hahn, R.S., 1985, Computer Aided Grinding Process Planning, Milton C.Shaw Grinding symposium, ASHE winter Annual Meeting, Miami Beach, Florida, Nov. 17-22, PED Vo1 16, PP: 933. Bhateja, P.C., 1984, Current State of the Art for Workpiece Roundness Control in Precision Centerless Grinding, Annals of CIRP, Vol. 33/1/1984., PP: 199-203. Hahn, R.s., Lindsay, R.P., 1971, Principles of Grinding, Part-(I, 11, 111, IV, V), Machining, July, PP: 3-59. Lindsay, P.R., 1975, Principles of Grinding Four Years Later, SME paper NO. MR75-60,presented at SME's Abrasive Machining Conference, Sept. Field, M., Kegg, R., and Buescher, S., 1980, Computerized Cost Analysis of Grinding IDperations, Annals of CIRP, Vol. 29/1/1980, PP: 233-237. Sakai, Y., Ogata, S., and Asai, S., Optical Keasuring Instrument for Measuring Chatter larks, Annals of CIRP, Vol. 33/1/1984, PP: 407412. Smits, A.C., 1988, A Course in Centerless grinding Technology Process, Cincinnati Kilacron, Cincinnati, Ohio, USA. Cincinnati Milacron., Operating Manual for Cincinnati Milacron (220-8, 230-12, 330-15, 35020) Centerless Grinding Machine with Acramatic 725G Control Model AE, Publication NO. 1-GR84293, Part No. 3358979, Cincinnati Milacron Marketing Company, Cincinnati, Ohio 45209-9988, USA. Malkin, S., 1985, Practical Approaches to IGrinding Optimization, Milton C.Shaw Grinding Symposium ASME Winter Annual Meeting, Miami Beach, Florida, Nov. 17-22, PED Vol. 16, PP: 289-299. Malkin, S., Koran, Y . , 1980, Off-line Grinding with a Micro Computer, Annals of CIRP, Vol. 29/3/1980, PP: 213-216. Moreno, W.C., 1986, Self Learning Optimization Control Software, Proceedings of ROBEX'S6 (Robotics and Expert System), Instrument Society of America, ISA, June 4-16. Personal Consultant Plus (PCPlus), 1988, Users Manual, Texas Instruments Incorporated, 12501 Research Blvd., Austin, Texas 78769. USA. Venk, S., 1988, Development of an Expert System for Centerless Grinding and Integration for Process Optimization, Masters Thesis, Nov. 1988, University of Cincinnati, Cincinnati, Ohio 45221. Moreno, W.C., 1988, Ultramax User's Guide, Ultramax Corporation, 650 Northland Boulevard, Cincinnati, Ohio 45240, USA.