Journal o f Intelligent Manufacturing (1991) 2,353-363

ITONUS: expert system for machining on a

lathe

T H O M A S L. W A R D l, P A T R I C I A A. S. R A L S T O N 2, W A L D E M A R K A R W O W S K I 3 and W. D A R R I N H A L L l

Department of Industrial Engineering ~, Engineering Mathematics and Computer Science 2 and Center for Industrial Ergonomics -~, University of Louisville, Louisville, KY 40292, USA

Received October 1989 and accepted March 1991

A microcomputer-based expert system for the diagnosis of lathe machining operations has been developed using the EXSYS shell on an IBM AT. Interviews with a journeyman machinist resulted in an initial knowledge base of rules containing basic concepts and relationships concerning set-up and control of lathe machining. The system provides the user with analysis and advice regarding the diagnosis and certain set-up aspects of common lathe operations, and has shown that it is feasible to elicit and mimic at least a portion of an expert machinist's knowledge and special skills.

Keywords: expert system, machining, diagnosis, knowledge representation

1. Background

Computers are used to operate and organize the operation of metal-cutting machines. In the case of computer numerically controlled (CNC) machines, the servo- mechanisms that control speeds and feeds receive inputs from a local computer located at the machine tool (Press- man and Williams, 1977). The computer memory stores numerical control (NC) programs that determine the part geometry, and machine feeds and speeds. In many factor- ies, machine tools communicate with a central supervisory computer. In the fully automated factory (one with zero or low displaceable labor), the central computer may also control the material-handling system that moves parts from one machine to the next (Grover, 1980).

Widespread use of CNC machines has left a gap pre- viously occupied by the intelligence of machinists. An intermediate controller is required to close this gap, Fig. 1. Such a controller should insure that operation is within the constraints imposed by the machine, guard against forced oscillations (chatter) of the tool, and provide for the sensing and measurement required for automatic tool replacement. Roughly, these are the intelligent functions once supplied by the human machinist. A similar view of the machinist has been described by Wright (1983) and Wright and Bourne (1988).

Two attempts have been made to fill this gap: first, actual

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NC programs are written conservatively; speeds and feeds are set low enough so that machine integrity is never threatened and tool chatter never occurs. Also, tools are replaced well before the end of their useful lives. Second, adaptive control (AC) systems have been developed in the research laboratory, but have not been applied in industry (Schmenk, 1984; Wick, 1977 and Uhlman and Schmenk, 1981). More recently, emphasis has been on improving total time utilization of machine tools and addressing such issues as unattended operation and elimination of idle time (Astrop, 1978; Stauffer, 1978 and Inaba, 1980).

Figure 1 shows two major functions of the journeyman machinist: sensing and control. The research described is concerned primarily with the control function. Sensing by human machinists has been considered elsewhere (Wright and Bourne, 1988; Dressman et al., 1987 and Ward et al., 1988).

In a flexible manufacturing system (FMS) where produc- tion batches include sequences of differing parts, the AC must recognize the appropriate constraints, and then insure that machine operation is within those constraints for each part. Similarly, tool wear sensing must estimate the cumulative effect of wear that results from each sequence of differing parts. Such performance has not yet been achieved.

Exceptions invariably occur, situations that cannot be handled by the central computer, and that must be cleared

354 W a r d et al.

Unexpected mofefiol variations

INPUT: Pad throughput De~ire a Tool life low cost Surface finish

high Ch~p management quolit'/ Energy cor~umption product

Fig. 1. Relation of the machinist's control and sensing functions to global lathe machining control system.

by human intervention. These exceptions include failure of parts to feed and/or be positioned in the machine or material-handling system, undesirable chip flow, tool crashes and breaks, and diminished quality due to tool wear and/or chatter.

Two approaches which may provide performances superior to AC are expert systems (ES) and fuzzy logic control (FLC). These control approaches center on incor- porating human experience rather than relying exclusively on process modeling. They suggest themselves because of the success human machinists have had in controlling metal-cutting processes. ES originated from the study of artificial intelligence (AI), whereas FLC had its origin in control theory and fuzzy set theory. Unlike ES, most FLC systems are formulated by the designer rather than being extracted from a human expert. The ES is frequently used for decision-making and may be accessed by a human operator using a computer work station. The FLC is frequently used for the control of technological systems and has hard-wired sensor inputs, and outputs that control hardware. But it is certainly the case that an ES that used fuzzy logic (as many do) (Negoita, 1985 and Gupta and Sanchez, 1982) and that controlled a technological system would be functionally equivalent to an FLC that relied upon a rule base extracted from a human expert.

This work is the result of a first step in closing the gap left when CNC machines replaced the human machinist. ITONUS (the system is named for Itonus, a figure from Greek mythology who invented a method for polishing metal) as a stand-alone expert system could be used for troubleshooting NC programs. However, as it evolves, ITONUS will be used to replace or redesign the rule base of the FLC controller for a machine tool.

2. Fuzzy logic control

Fuzzy set theory was announced by Zadeh (1965) in 1965. FLC is an application of that theory. Chang and Zadeh (1972) first applied fuzzy set theory to control, but it was Zadeh (1973) who formulated the basic approach to fuzzy

control design for complex processes. It was in the latter reference that the concepts of a fuzzy relation and its underlying inference rule were defined, and their applica- tion to fuzzy controller theory set out. Fuzzy industrial controllers depend on linguistic control rules, which are a collection of actions to be taken given certain conditions. These statements, though imprecise, may still contain relevant and important information. Fuzzy controller algorithms are formed from the combination of such rules. Fuzzy control of processes is an alternative when systems cannot be well controlled by classical or modern control techniques (Maiers and Sherif, 1985).

Reports of applications of FLC to control of machine tools began with the work of Zhu et al. (1982). This work produced a fuzzy logic controller for grinding. The single- input, single-output loop controlled surface finish by varying the feedrate.

Sakai etal. (1984) and Sakai and Ohkusa (1984 and 1985) have considered the application of FLC to turning. They assume a cutting condition space and a cutting state space. The cutting condition space consists of speed, feed, and depth of cut. The proposed cutting state space is defined to control chip length, and to guard against chatter and tool fracture. By assuming that there is an analytical mapping from cutting condition to cutting state space (at least in a local region) a preliminary framework for computing fuzzy control statements is obtained. The framework is basically rule base construction. However, they have also offered an example that gives membership functions for chip length, feed rate, and cutting speed, and that has rules for adjusting feed and speed in response to chip length. Ralston and Ward (1987) have examined the issue of FLC of metal cutting on lathes within the context of computer numerical control (CNC) and adaptive control (AC), and with Dressman et al. (1987) they have considered the related question of expert systems for metal cutting.

Ultimately, ITONUS will provide a base set of rules for the intelligent (knowledge-based) control of a lathe using FCC.

3. Expert systems technology

An expert system is a computer program that embodies the expertise of one or more experts in some domain and that applies this knowledge to make useful inferences for the user (Waterman and Hayes-Roth, 1983). An ES program uses symbolic reasoning rather than simple algorithm execution. Knowledge is a collection of related facts, beliefs, and heuristic rules (Hayes-Roth et al., 1983). In expert system development, knowledge representation is the establishment of a correspondence between a symbolic reasoning system and the outside world.

This information constitutes the knowledge base. Know- ledge acquisition is the extraction of knowledge from

I T O N U S : expert system for machining on a lathe 355

sources of expertise (human experts, books, documents, computer files, etc.) and subsequent transfer to the know- ledge base. The process of knowledge acquisition is formally described by five stages (Hayes-Routh et al., 1983): (1) identification - problem is identified; (2) con- ceptualization- knowledge representation scheme is tenta- tively selected; (3) formalization - structure to organize knowledge is designed; (4) implementation - knowledge is mapped into representational framework, and (5) testing- prototype is evaluated by using a variety of example problems or cases.

In the development of expert systems, three people or groups of people are important. These are the domain expert(s), the knowledge engineer, and the user(s). The knowledge engineer elicits knowledge from the expert. The ultimate use of an ES is that it allows the user to make decisions as if he or she were the expert. The limitations of expert systems are due to the fact that they fall short on general intelligent behavior. For example, they are unable to recognize problems for which their own knowledge base is inapplicable or insufficient, they have no independent means of checking conclusions, and they have weak explanation skills.

If the ES makes use of a human expert, as most do, extraction of knowledge from the expert is the most time-consuming part of the ES development. Several methods are often employed to extract knowledge from human experts: interviews, protocol analysis, walk- throughs or observation of experts performing activities, questionnaires, decision analysis, or use of induction rule development by examining examples of problems the expert solves (Hart, 1986; Greenwell, 1988; Gaines and Boose, 1988 and Boose and Gaines, 1988).

The over-all construction of an expert system requires that the ES must be built satisfactorily in a reasonable time at a reasonable cost. Typically, seven steps are involved (Turban, 1990): (i) specify problem and determine feasibil- ity; (ii) select expert(s); (iii) conceptually design ES and complete feasibility study; (iv) select hardware and soft- ware; (v) perform knowledge acquisition, representation, and inferencing, (vi) build prototype, and (vii) evaluate performance and make improvements. These steps were followed in the development of ITONUS.

4. Knowledge acquisition

The task of gathering information, generally, from any source, is called the knowledge acquisition, while the task of gathering information from the domain expert is called the knowledge elicitation (Shadbolt and Burton, 1990). The main question in knowledge elicitation is how the knowledge engineer can get the domain experts to tell what they do. Knowledge acquisition (or elicitation) is the scientific and engineering problem of formalizing a domain

expertise for the first time (Kodratoff et al., 1988). The methods used to achieve this goal reflect the expert's ability to explain his/her behavior. Kodratoff et al. (1988) divided the knowledge elicitation process into three steps:

(1) Obtaining background knowledge through expert interaction and literature on the domain;

(2) Learning full description of high-level and inter- mediary-level concepts (concept formation);

(3) Learning diagnostic rules and meta-level knowledge (rule learning).

According to Buchanan (1982), the knowledge acquisi- tion (or elicitation) is the transfer and transformation of problem-solving expertise from some knowledge source to a program, with potential sources of knowledge being human experts, textbooks, databases, and even one's own experience. In addition to such knowledge elicitation techniques as expert interview, verbal protocol analysis and observational studies (Welbank, 1983), a number of psychological techniques, including the personal construct theory (Shaw and Gains, 1984), and the concept of sorting are being used. Recently, Belkin et al. (1988) have proposed the discourse analysis method of knowledge elicitation based on the collection of data consisting of natural language human-human interactions. Table 1 shows a taxonomy of knowledge acquisition methods modified from Santamarina and Salvendy (1989).

The main difficulty in knowledge acquisition is that in the process of human thinking one attempts to understand and the model is not subject to direct observation. Recently, Johnson et al. (1988) developed a framework for represent- ing expertise required to perform a given task. This framework, based upon inferences made from a record of problem-solving activities consists of the following:

(1) The expert can be viewed as a processor that has the capability of producing a certain problem-solving behavior using expertise. The task of knowledge acquisition is to determine this expertise;

(2) The expert develops a set of actions and abilities that are necessary to realize this expertise;

(3) Although one cannot observe the expertise directly, the invocation of the expert's actions and abilities in a record of problem-solving behavior can be observed;

(4) Since one can observe the invocation of actions and abilities by the expert, some representation of the expertise can be developed;

(5) A statement of the expertise required to perform a task serves as a specification of the requirements for a computer program that is designed to perform this task.

For example, the methodology for collecting and analyz- ing the protocol data leading to development of specifica- tion of expertise can be based upon identification of operations, episodes, and data cues which are the basic categories of behavior in the protocol records (Johnson et

356 Ward et al.

Table 1. Taxonomy of knowledge acquisition tools (Santamarina and Salvendy, 1989).

(I) Problemsolving (1) Protocol analysis (2) Retrospective probing (3) Procedural simulation or Problem analysis (Grover,

1983 and Waterman, 1986) (4) On-site observation (5) Forward scenario simulation (Grover, 1983)

(6) Introspective reports

(II) Domain dissection (1) Distinguishing events (Hart, 1986andKahnetal.,

1985) (2) Dividing goals (Grover, 1983 and Hart, 1986)

(3) Grouping symptoms (Grover, 1980 and Hart, 1986)

(4) ETS- Repertory grid (Boose, 1986) (5) Path division (Kahn etal., 1985) (6) Path differentiation (Kahn et al., 1985)

(7) Frequency conditionalization (Kahn etal., 1985) (8) Differentiation (Kahn etal., 1985)

(I11) Domain description (1) Problem discussion (Waterman, 1986) (2) Characteristics and decisions (Hart, 1986) (3) Problem description (4) Critical incident technique (Hart, 1986)

(IV) System improvements (1) System refinement (Waterman, 1986) (2) System examination (Waterman, 1986) (3) Performance feed-back (Gaines, 1986) (4) Systemvatidation (Waterman, 1986)

(V) Groups of assessors (1) Crawford slip method (Boose, 1986) (2) Delphi method

(VI) Other methods (1) Induction (Hart, 1986) (2) Constraint propagation

(3) Brainstorming

Expert solves the problem aloud Expert responds to specific probes after completion of the task Expert solves real problem while being occasionally probed for the reasoning process Expert is watched solving a problem on site Expert chooses a case and verbalizes the reasoning process in reaching the goal Experts try to explain their knowledge, skills, and decision process

Characteristics of a symptom are discovered by distinguishing the events that could cause it from those that could not Goals are successively broken down into sub-goals to the level of observable facts Symptoms are listed and successively grouped until the final goal is reached; alternatively, the interview is driven towards the construction of rules which help classify observations into more specific objects and activities Elicitation is led to develop a grid of constructs- hierarchical breakdown Seeks for a cause on the path linking a diagnosable event with an already reported symptom Seeks to find whether an event is the result of overlapping causal paths or non-overlapping ones Seeks for conditions that will make a symptom more or less likely to occur Finds symptoms that distinguish diagnosable events

Expert discusses information and procedures needed to solve problem Expert lists characteristics and decisions; then he is asked to match sets Expert describes characteristic problems for each type of answer Expert describes interesting or difficult cases that he recalls

Expert provides problems to be solved with elicited knowledge Expert examines system's knowledge and structure

Cases solved by expert and system are presented to other experts

Groups of individuals respond to questions on slips of paper Structured form of communication, with controlled feed-back for re-evaluation, oriented to facilitating consensus

Knowledge is extracted from a training set Constraints defining cases in a training set are propagated to establish the constraints that characterize the alternative solutions Individuals list potential ideas which are then evaluated

al., 1988). Operations are primitive activities of problem- solving that do not depend on a particular context, like collecting data or making computations. The patterns of operations that are repeated within and across different problems in the protocol data are called episodes. Data cues are operands which comprise the data processed in the problem-solving operations, and are indicated by domain n o u n s .

According to Santamarina and Salvendy (1989), the most common arrangement for knowledge acquisitioIi scheme involves the interaction between the expert, know- ledge engineer and the system under development. In this framework, the knowledge acquisition involves three steps: (1) the adaptation to the expert and his domain, and to the computer structure; (2) the extraction of relevant, correct and complete knowledge from the expert, and (3)

ITONUS: expert system for machining on a lathe 357

the implementation of this information in a computer system.

As pointed out by Gaines and Boose (1988), the knowledge acquisition bottleneck in development of ex- pert systems is not only a problem of accessing and translating what is already known, but the problem of formalizing models for the first time. The main problems of accessing the human expert's knowledge were outlined by Gaines (1988) as follows:

(1) Expertise may be fortuitous. Results obtained may be dependent on features of the situation which the expert is not controlling;

(2) Expertise may not be available to awareness. An expert may not be able to transmit the expertise by critiquing the performance of others because he is not able to evaluate;

(3) Expertise may not be expressible in language. An expert may not be able to transmit the expertise explicitly because he is unable to express it;

(4) Expertise may not be understandable when ex- pressed in language. An apprentice may not be able to understand the language in which the expertise is ex- pressed;

(5) Expertise may not be applicable even when ex- pressed in language. An apprentice may not be able to convert verbal comprehension of the basis of a skill into skilled performance;

(6) Expertise expressed may be irrelevant. Much of what is learned, particularly under random reinforcement schedules, is superstitious behavior that neither con- tributes nor detracts from performance;

(7) Expertise expressed may be incomplete. There will usually be implicit situational dependencies that make explicit expertise inadequate for performance;

(8) Expertise express may be incorrect. Experts may make explicit statements which do not correspond to their actual behavior and lead to incorrect performance.

The elicitation of knowledge from experts is time-con- suming and usually lacks systematic conceptual design methods (Cleaves, 1988). The requirement for knowledge elicitation is to ensure that the expert's best judgement is extracted. Cleaves (1988) points out that an important prerequisite for selecting the known experts and in choos- ing the method of knowledge elicitation is defining what is expertise.

5. Knowledge representation

Human knowledge can be declarative, i.e. expressing the state of the world through a set of specific statements, or procedural, illustrating how to do things (Conway and Wilson, 1988). The main difference between these two

types of knowledge is that procedural knowledge cannot be as easily described or retrieved as in the case of declarative knowledge. This difference has a direct bearing on the knowledge representation schemes for expert systems. For example, semantic nets and other schemata are used to represent the easily describable declarative knowledge, while frameworks and production systems are utilized for representing the procedural knowledge,

According to Duce and Ringland (1988), the knowledge representation problem can be described using three main components. The first one is to find a knowledge representation language or formal language in which the knowledge domain can be described. The second com- ponent of the knowledge representation problem is the one that can perform automatic inferences for the user. The third component is how to develop a knowledge base that accurately represents the understanding of the domain area.

The main sub-problems of knowledge representation were summarized by Duce and Ringland (1988) as follows:

(1) Expressive adequacy: is a particular knowledge representation scheme sufficiently powerful? What know- ledge can and cannot particular schemes represent?

(2) Reasoning efficiency: like all representation prob- lems in computer science, a scheme that represents all knowledge of interest and is sufficient to allow any fact of interest to be inferred by no means guarantees that it will be possible to perform the inference in an acceptable time. There is generally a trade-off between expressive adequacy and reasoning efficiency;

(3) Primitives: what are the primitives (if any) in know- ledge representation? What primitives should be provided in a system and at what level?

(4) Meta-representation: how do we structure the know- ledge in a knowledge base and how do we represent knowledge about this structure in the knowledge base?

(5) Incompleteness: what can be left unsaid about a domain and how do you perform inferencing over incom- plete knowledge and revise earlier inferences in the light of later, more complete, knowledge?

(6) Real world knowledge: how can we deal with atti- tudes such as beliefs, desires and intentions? How do we avoid the paradoxes that accompany self-reverential propositions?

Many psychological studies investigated different know- ledge representation schemes used to develop structures for representing the human experts' knowledge. The forms of knowledge representation can be described with respect to their relevance to human information-processing para- digm (Conway and Wilson, 1988). In general, there are three forms of knowledge representation, i.e. (1) procedu- ral; (2) propositional; and (3) analogical. The procedural representation focuses on the control of the representation within the context of some general structure of knowledge.

358 W a r d et al.

The procedural representation may also combine the knowledge control and representation into a single form. The propositional representation uses abstract content patterns to organize the propositional information (like schemata and frames). Other propositional representa- tions of concepts may use the semantic nets as representa- tion schemes.

6. Applications of expert systems

There have been a number of attempts to aid or even' supplant the human CNC part programmer. Preiss and Kaplansky (1984) have used AI techniques to convert a part drawing to a part program for CNC milling. Kamvar and Melkanoff (1985) have addressed the same problem in the case of turning. Altom and Houtzeel (1987) describe an ES that generates NC instructions for turning, milling, drilling, taping, and boring from manual input or directly from a CAD database.

The selection of tools, speed, and feeds has also received attention. Barkocy and Zdeblick (1984) described a know- ledge-based system to select cutting tools, pass sizes, and speeds and feeds for the detailed planning of machining operations. Wang and Wysk (1986) developed an ES that generates efficient machining parameters. Melkote and Taylor (1988) developed an ES that consults an external commercial database to select milling cutters, feed rates, and spindle speed. Chang et al. (1988) have developed an integrated design/manufacturing/inspection system that includes a part programming system for a machining cell.

The real time control of metal cutting whether by a human or a computer is a natural ES application. Koval (1987) proposes a future production facility that includes intelligent machine control together with expert systems, expert databases, and large scale simulation via CRAY- class supercomputers. Dressman et al. (1987) describe a framework for ES control of metal cutting.

7. Development of the ITONUS system

The system developed and described in this paper is a rule-based ES which aids in diagnosing problems during lathe operations and suggests set-up parameters based on user input about material, type of operation, and part description. In response to system prompts the user supplies information about the operation, material, part shape, occurrence of chatter, chip color, etc. The system then provides suggestions to correct a problem or to avoid a potential problem. The ES is a result of 15 intensive discussions and walk-throughs with an expert machinist who also had a keen interest in computers and automation.

7.1. Knowledge elicitation

Turban (1990) notes that potential sources of knowledge include human experts, textbooks, databases, special re- search reports, and pictures, and that these sources can be divided into two types: documented and undocumented. Since the primary short-term goal of this research was to capture the domain expertise of a journeyman machinist, only sources of undocumented knowledge, the sort that resides in people's minds, was considered. The knowledge elicitation process and prototype development using EX- SYS is summarized (Sprague and Ruth, 1988) in five steps: (1) identify subject area and find expert; (2) extract expert's knowledge; (3) transfer knowledge into facts which can be used as decision rules in the knowledge base; (4) build the knowledge base, and (5) test problem.

Once an experienced and knowledgeable journeyman machinist was identified, an initial domain description was extracted using a questionnaire. Questionnaire items (Fig. 2 as applied to lathe machining) were used to stimulate problem discussion by the domain expert (Waterman, 1986). The responses to these questions were analyzed and the first interview planned. The first few interviews were used to identify the main areas in lathe machining to be defined for later development of specific concepts and relations. These were the most difficult interviews because of the difficulty in defining and narrowing the domain. Each interview was tape-recorded and transcribed for analysis and follow-up questioning. A major difficulty encountered was in guiding the course of the interview. This task was easier as the expert learned the capabilities and limitations of the knowledge representation system and his role in developing it.

An excerpt from an early interview is shown in Fig. 3. Some interesting problems were encountered when the sessions were transcribed. The expert's comments con- tained many partial sentences, contradictions, omissions, and repetitions. The absence of body language sometimes made the recordings difficult to interpret. These problems complicated the procedure of formalizing and categorizing the information given in an interview. A future improve- ment would be to videotape the sessions.

The early interviews also suggested the type of know- ledge representation scheme and inference mechanism to

Lathe machining question 1. What are the inputs or problems? 2. What are the outputs or solutions? 3. Which types of inputs cause difficulties for the expert? 4. How are the problems characterized? 5. How are the solutions characterized? 6. What sort of knowledge is used? 7. How are problems or methods broken into smaller units?

Fig. 2. Initial questionnaire items as applied to lathe machining.

ITONUS: expert system for machining on a lathe 359

Questions (Q) by the knowledge engineer and answers (A) by the domain expert Q. What kinds of lathe operations are especially difficult and what do you have to

pay special attention to when performing them?

A. Length of part always has an effect on how the machine operation must proceed. The longer the material the greater chances are for chatter problems. Speeds, feeds, tool height, and cutting depth also must be considered. Also, tolerances that are very close add to the machining time.

Q. What are some common symptoms and conditions that you look for?

A. Chatter is a common problem in turning operations. It can be caused by a number of conditions - flexibility of materials, tool set at wrong height, wrong tool, feeds too fast or slow to match toohng, or a combinations of many things.

Q. What kinds of decisions do you have to make regarding these symptoms?

A. Chatter can be reduced in a lot of ways. A tool can be selected that has a smaller radius on the point, or could be shaped differently. Cutting speeds may have to be reduced or feeds could be increased, or decreased. Tooling height could be raised or lowered.

Q. What are the consequences of the actions you take when a condition arises that requires actions?

A. The consequences are that you begin to make well machined parts at a consistent rate with very. few bad parts and with very little wear and tear on equipment.

Fig. 3. Excerpt from an early interview.

use. This resulted in the appropriate shell selection. A rule-based, backward chaining system was indicated since the expert's knowledge most often assumed the form of rules-of-thumb and IF-THEN-ELSE statements. The most common methods and actions performed involved monitoring, diagnosis, and control.

In the final sessions before actual rules were developed, and the knowledge base entered, specific areas in lathe machining were identified for application to the expert system. The prototype incorporated the areas shown in Fig. 4. These machining aspects are dependent on several factors, including type of operation, material, workpiece shape, type of cut, and other variables. Protocol analysis (step-by-step explanation of problem solving by the expert) was used in the last interviews to produce specific concepts and relations for actual rule development. The expert explained his problem-solving approach to a variety of test cases in lathe machining. Over the course of several

Area Aspect Planning tool selection

speed selection feed rate selection chatter recognition

Diagnosis chatter problem correction avoidance of excessive tool wear maintenance of finish and tolerance requirements avoidance of heating problems

Fig. 4. Specific areas identified for application to the expert system.

protocol analysis sessions, it was possible to apply the concepts and relations developed and construct a set of rules to place in the shell.

7.2. Expert system shell

Analysis of eight PC-based shells resulted in selection of EXSYS, version 3.2.5 (EXSYS, 1985). EXSYS uses production rules for its knowledge representation. A certainty factor can be associated with each rule outcome. The rule base may be searched by backward or limited forward chaining. It has a menu-driven knowledge-base editor that provides some control over screen formatting. Why/how questioning is available to the developer and user, and there are related explanation and on-line help facilities. Examples may be saved for use in sensitivity analyses.

7.3. Knowledge base

The knowledge base of ITONUS consists of three com- ponents: rules, qualifiers, and choices. Rules are the IF-THEN statements which contain the relationships and heuristics to be manipulated by the inference mechanism. Qualifiers are conditions which comprise the IF portion of the rules, and consist of a statement ending in a verb. Several values may be assigned to each qualifier corres- ponding to different states of that qualifier. For example:

Qualifier Value

the operation is turning facing boring parting

EXSYS permits values to be negated by preceding them with NOT, and it permits the selection of two or more values connected by the logical OR. Choices are the conclusions which form the THEN portion of the rules. The appropriate choices are printed at the end of a consultation as a list of recommended actions to be taken by the user. For example:

Choice

use a steady or follow rest inspect tool increase speed chatter is possible

EXSYS rules are frequently of the form, 'IF qualifier value THEN choice.' So that, based on the above examples, 'IF

360 Ward et al.

the operation is turning T H E N chatter is possible,' would be a valid (though not particularly helpful) rule.

Two categories of machinist control: (1) planning or set-up, and (2) diagnosis or troubleshooting, governed rule development. Planning was considered to involve the choice of initial feeds and/or speeds based on material type.

There are 46 rules�9 Fourteen rules are related to speed and feed selection for set-up based on material type and operation. Fifteen rules relate to set-up conditions to avoid chatter or what to do if chatter occurs. The remaining 17 rules pertain to control and diagnosis of certain problems�9 Advice to increase or decrease feeds and/or speed to avoid wear, tool failure, and part quality is given�9

A list of common work materials was divided into categories according to their effects on the tool. The hard materials suggested a slower set-up speed, while less demanding materials suggested a faster speed and/or feed. Within these groups, specific recommendat ions are given for each material. For different operations such as parting or boring, or for workpieces with other than cylindrical shapes, rules were made regarding tool position, possibility of chatter, and need for extra workpiece support.

When rules pertaining to troubleshooting or diagnosis fire early in an EXSYS execution, the values of qualifiers in the IF part of the rules may not be known�9 When this occurs, the user is prompted for the required value. For example if a rule of the form ' IF Material type is Brass T H E N . . . ' fires and the type of material is not known, the user will be prompted with 'Material type is' as shown at the top of the consultation in Table 2. At present EXSYS has 19 qualifiers and each qualifier can take on from 2 to 13

Table 2. The following sample consultation is based on a steel- boring operation�9 The part is 41 cm long and 10 cm in diameter. The workpiece is cylindrical, chatter is not present, and chips are flowing freely. However, the load on the machine is increasing and the chips are beginning to turn blue. Tolerances are still being held and the status of tool wear is unknown. The bore diameter is 3.18 cm and the length of the bore is 15 cm. A rough cut is being taken. Ellipses ( . . . ) indicate that the session has been edited by deleting values.

Material type is 1 Cast iron 2 Bronze 3 Aluminum 4 Stainless steel

11 Forging 4 Operation is

1 Parting 2 Turning 3 Facing 4 Boring

Table 2--contd.

4 Please input part length : 16 Please input part diameter : 4 Chatter is

1 Present 2 Not present 3 No machining has been performed yet

2 Workpiece is

1 Square or hexagonal 2 Cylindrical

2 Chips are

1 Flowing freely and consistently 2 Flowing poorly (long, 'stringy', non-uniform)

1 Ampere reading is

1 Increasing 2 Decreasing

1 Tool wear is

1 Present 2 Not present 3 Not known 4 No machining has been performed yet

3 Chip color is

1 Straw 2 Blue coming off the workpiece 3 Same as workpiece 4 Changing from straw to blue 5 No machining has been performed yet

4 Part

2 Please : 1.25 Please : 6 Cut is

tolerance is 1 Incorrect 2 Correct

input bore diameter

input bore length

1 Finish cut 2 Rough cut

Values based on 0 - 10 system 1 Inspect tool 2 For setup, select a speed that is slower than normal 3 Tool wear is likely

10 9 7

I T O N U S : expert system for machining on a lathe 361

values. The consultation in Table 2 asked for values for 10 of the 19 qualifiers.

Rules containing recommended corrective or preventive action were developed considering the various input condi- tions. Possible reasons for problems such as tool wear are given based on user input. Appropriate actions that are recommended appear as choices in the THEN part of rules. At present EXSYS has 27 available choices. Three of the 27 choices ('Inspect tool,' etc.) appear at the end of the consultation in Table 2.

The recommendations given in the EXSYS execution have values assigned from zero to ten. This numerical confidence factor, a feature of EXSYS, is an average of the confidence factors which appear in all the rules that fire. The factor for each rule is selected by the domain expert. These confidence factor values represent, on a scale of zero to ten, the relative importance of a recommendation or the likelihood of a problem occurring, depending on the particular rule category.

7.4. Verification and validation

Verification and validation is a continuous process that is applied during development for system improvement and toward the end of development for evaluation of a particu- lar prototype or improved version of the system (Turban, 1990). Difficulties that have been pointed out by Assad and Golden (1986) are centered around these questions:

(1) What characteristics should be evaluated? (2) How should performance be evaluated? (3) How should test problems be selected? (4) How should one evaluate the system's mistakes?

Because of the imprecise nature of the answers, the solutions adopted may be a compromise between formal methods and pragmatics (Brul6 and Blont, 1989). Santa- marina and Salvendy (1989) have suggested solutions that can be arranged in a hierarchy:

(1) System examination in which the expert examines the system's knowledge and structure (Waterman, 1986).

(2) System refinement in which the expert provides problems to be solved with the elicited knowledge (Water- man, 1986).

(3) System validation in which cases solved by the expert and system are presented to other experts (Waterman, 1986).

(4) Performance feed-back in which the system is moni- tored in a controlled and limited environment (Gaines, 1986 and Brul6 and Blont, 1989).

ITONUS was subjected to system evaluation (item 1 above) during the last five interviews and system refine- ment (item 2 above) during the last two interviews.

EXSYS has a facility for testing which allows comparison of one consultation (EXSYS execution) with a new one.

Old and new results are displayed together showing changes in values and recommendations. This facility is helpful in rule development. Development of the rules evolved over the course of the interviews. Rules were progressively modified, added or deleted. During the last two interviews four test cases (Hall, 1988) were evaluated and resulted in further modification of the rule base. System validation (item 3 above) and performance feed- back (item 4 above) can be expected to produce further improvement.

8. Conclusions

ITONUS is an ES for lathe machining that represents a first step in capturing and eliciting the unique knowledge of an expert machinist. Basic concepts about planning and control of lathe operations have been applied in an ES environment. ITONUS gives practical advice on a limited number of lathe operation scenarios. A non-expert, having only a limited knowledge of lathe machining, can receive suggestions for set-up, operation, and diagnosis of certain common problems. Obvious limitations are the lack of robustness of the knowledge base and the fact that only one expert was consulted. Future work will aim to involve more experts, especially those familiar with CNC machines; rules will be developed to deal with specific types of sensor feed-back and CNC programming concepts; the know- ledge base will be expanded to include specific tool selection, material, and speed/feed relationships for set- up; testing will be done in an industrial setting; and further attention will be given to human perception of heat effects. Ultimately, the expert machinist as embodied in the expert system will be used to redesign or replace the rule base and controller of an FLC machine tool. The ultimate goal of this research is to develop a metal-cutting process control that will permit unattended but reliable operation of a lathe.

Acknowledgements

The initial concepts and planning for the project was done with support from the National Science Foundation re- search award R118610671. The actual research was funded by a grant from the University of Kentucky Center for Robotics and Manufacturing Systems. Special thanks are due to journeyman machinist James Hartlage, the domain expert.

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