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TYPE: Article CC:CCL

JOURNAL TITLE: Journal of materials processing technology

USER JOURNAL TITLE: Journal of materials processing technology

ARTICLE TITLE: A knowledge-based system for electrochemical machining procedure

ARTICLE AUTHOR: Khairy, A.B.

VOLUME: 58

ISSUE: 1

MONTH:

YEAR: 1996

PAGES: 121-130

ISSN: 0924-0136

OCLC #:

Processed by RapidX: 10/13/2015 8:04:04 AM

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E L S E V I E R .~om'nal of Materials Processing Tedmotogy 58 ¢ 1996) 121 130

~q~lateri~ ~rf~e~ing Technglggy

A knowledge-based system for electrochemical machining procedure

A.B. Khairy Deparmwm of Productima Enghwer#ag, Alexandria UniversiO,, Alexandria. Egypt

Received 24 April 1994; accepted 7 February 1995

industrial summary

The paper addresses the concept and prototype development of a hierarchically structured knowledge-based system (KBS} fbr the optimized process operation of electrochemical machining. The KBS ~s developed for conditions whereby a near-true shaping and equilibrium material removal process hold in a true working environment The structare of the KBS permits updating and installation of individual function, database and modules such as those used for determining ECM variables and identifying constraints and parameters for optimum anodic-tool shape deemed to produce a certain workpiece geometry. The KBS is structured to select the feasible process parameter according to a given set of workpiece attributes. The KBS has a modular structm'e which can accommodate multiple configurations of workpiece shapes and sizes. Qualitative indices have also been implemented to lacilitate decision-making among candidates of materials and/or electrolytes. Initial testing has demonstrated the feasibility of integrated heuristic and algorithmic procedures for real-time applications. A fully implemented version of this KBS will enable the user to perform high quality machining with optimum rates of performances. Machining time is proportionally saved and deductive selection of machining variables can be made. The KBS software is written in the C language and has been tested for a real operation witll ECM cell environment.

geywonls: Electrochemical machining; Knowledge-based system

I. Introduction

The application of electrochemical machining (ECM) to exotic alloys to produce intricate shapes and high surface quality has signi!icantly increased in recent years. There has been a deeper understanding of the physics of the process as well as a wider spectrum of knowledge about it. To simplify and standardize the application of ECM, certain procedures have been compiled and recommended by research and profes- sional bodies [1-3]. However, most of the tentative procedures which have been adopted are based on personal knowledge, skill and judgement. Many deci- sions have to be made when handling even a simple ECM machining operation concerning the selection of many independent variables as shown in Fig. I. Many process parameters have interrelated actions that di- rectly or inversely affect the workpiece geometry and surface integrity, and may cause damage to the ca- thodic tool if not controlled judiciously.

A dynamic environment usually arises when using an ECM machining cell to perform a variety of operations,

that involve different configurations of t o o l workpiece and input parameters in practical situations. Most gen- eral process designers have limited proficiency in han- dling all the difl'erent ECM operations encountered and seek lhrther expertise from human experts and/or refer to literature. Information is not always readily obtained because the bulk of the know-how, based on practice over many years, is not conveniently recorded or easily consulted in documents or databanks. Training of per- sonnel to gain the missing skills and experience is rendered dillicult because of the loss of expertise as skilfui personnel leave work. Under such circumstances, if appropriate knowledge is not available from a suit- ably reliable source, the job in hand is likely to be delayed, and there is a risk of degraded machining quality, equipment damage, and increased down-time. To increase productivity and to enhance quality and provide an aid to design, planning and manufacture of ECMed components, a computerized knowledge-based system (KBS) is being developed. The knowledge base described in this investigation provides a knowledge consultancy system that guides the selection of the most

Elsevier Science S.A. SSDI 0924-0 i 36(95)02116-4

~'~ A,B. ghai~3'/ Journal t!f Materials Processittg 7~,clmoh~gy 58 (1996) 121-130

appropriate machining conditions for achieving specifi- cations of a given object based on its geometrical and physical description.

Since the early 1980s KBS have emerged as special- ized problem solvers which emphasize the use of the knowledge rather than algorithms, but in a rather nonspecialized-nonheuristic domain. Several protype KBS have been developed in the domains of manufac- turing, robotics and fault diagnosis and have proved to be cost effective.

The concept of using expert systems for optimizing the selection of machining processes stems from work done in the past ten years [4-71. An example is Hon's work [4] on laying down the foundations of adopting artificial intelligence techniques in planning a strategy to select the optimum machining process among many candidates. This was hindered by embodying too many machining methods in the knowledge base. Mill and his colleagues [5] proposed a generative planning technique that was based on generic object models for intelligent sequencing of machining operations. Khairy [6,7] has limited his consideration to the selection of a machining method from a family of nonconventional processes, via an expert systems environment.

The application of knowledge.based systems to guide selection of machining conditions for conventional n'la- chining methods has been rather limited [8,9], although these systems have been implemented more successfully in diagnostic engineering [10], There are databases al. ready available for the selection of materials [11,121, but these did not include any data for the class of nonconventional processes like ECM. There are also Pc, based systems to apply the concepts of concurrent engineering in design and manufacturing via clas, tcal methods of machining [13,14], Thompson et al. [15] suc~ssively tested the implemention of a prototy~ KBS for seam welding automation and control. Bonnet et al, [i6] reported ~aa~u~y diversified applications of expert systems particularly in CAD, diagnosis of break- down, process control and robotics.

~,g, !, EI~M independent variables.

2. E C M - - a candidate for KBS

The essence of KBS was not captured in the area of ECM, although some work has been reported about its application in EDM [17]. The task of optimizing the tool design and operating conditions requires complex computations and judgement, and a decision about them may not be unique, i.e. a number of possible tool geometries and operating choices could be viable. Fur- thermore, any adopted scheme in ECM depends on the expertise of the problem solver. The establishment of a neat methodology for tool design is particularly difficult because of the complexity of hydrodynamic reactions and variability in parameters during machining. The most important aspect of tool design as linked with selecting machining parameters is to arrive at a combi- nation of nominal tool geometry that leads to the production of the required object geometry through the highly turbulent machining gap in between. If the tech- niques of geometric modelling are applied, a frame of plane and isometric models for tool and accompanying workpiece can be obtained for any machining combina- tion. The formulation of KBS in this case should ultimately be based upon a full recognition of the following ECM features:

(!) For a typical machining operation, the entire process of selecting the machining parameters, deter- mining tile tool shape, finding the interdependencies between process variables, and issuing appropriate plan sequences is difficult to formulate and expertise de- manding.

(2) The knowledge related to ECM technology is wtst and needs a selective approach.

(3) Problems such as multiplicity of boundary limits in analytical tool design and finding, Ibr example, a realistic mode of dissolution may lead to a number of alternative choices if only an algorithmic strategy is adopted,

These aspects make a problem of selecting ECM operation parameters a prime candidate for the KBS approach. Apparently, no KBS has been developed to deal with the obscure and highly subjective ECM oper- ation. This is the first stage, being addressed here, in a series of disciplines aiming to computer customize the ECM process, In the following stage, the knowledge so selected for a typical ECM operation can be used to invoke a fully integrated manufacturing system.

3. Initial procedure for KBS

To address this problem properly, both factual and heuristic information for common combinations of tool-electrolyte-workplace and configurations of ma- chining zone should be assimilated into a KBS. This

A.B, K&:a'O' /,t~m~nu/o/Mawria/s l,¥ocessm,~ T~chnoa~gy 58 {~996) ~2~ L~O ~2~

IN~I'IAL

STA TE

Fig. 2. Procedure for conducting ECM.

8~'ATB

methods. For example, simple analyticat rutcs are used to assess the limitations of ECM machine tooi aad its power generatm', and more complex heuristics t~or de- termining the best combination of etectrotyte-toot for a given workpiece. The KBS also includes algorithmic procedures to estimate the material removal rate, pro- jeered area in the machining zone, maximum current density, and required applied voltage, and more com- prehensive procedures for the tool design. The flow chart in Fig. 3 summarizes the steps of the strategy developed.

system should then be able to guide the selection of optimal interaction between process variables on the basis of an expert's knowledge, and to explain how a particular conclusion {goal) was reached.

The procedure for conducting a typical ECM opera- tion is depicted in Fig. 2 as a sequence of one or more actions {machining operations) and resources {ECM machinery) that enable the goal state {producing a finished part) to be reached, given the initial state (stock). The following methodology is adopted to ac- complish this target:

(I) Identify the ECM machinery to be used and its power-rating.

(2) Obtain all factual information pertaining to the workpiece specifications such as hardness, geometry, required surface integrity and electrolysis parameters as well as the quantity (usually more than 30 pieces to justify expensive tooling).

(3) Determine initial values lbr appropriate machin- ing.

(4) Use a set of experience-ba,~ed rules to determine if the overall operation performance can be improved by reconsidering original choice{s) of machining parameters or if an originally infeasible choice of elec- trolyte mixture and/or tool material and shape can be made feasible.

(5) Produce the final operation procedure. Once the final machining parameters and the corre-

sponding tool shape have been determined by KBS, the information is displayed on the workstation monitor for all recommended choices. If the ECM machine tool is equipped with a numerically controlled facility, the recommended choices are converted into commands for setting the controllers of the machine such as the open- gap voltage, feed rate and electrolyte pressure. From the proposed strategy it can be seen that there are different types of knowledge and reasoning processes required for the complete planning of a given ECM operation. The knowledge base builds upon such facts as workpiece geometry and machine tool limitations and comprises both rule-based and algorithmic proce- dures. Rules are based on analytical as well as heuristic

4. Architecture of knowledge base

The knowledge base is developed to contain as much expertise relevant to ECM as possible. Most of this expertise, i.e. experience and knowledge, available to date is documented in technical reports and annals published by the CIRP, E&O STC-Committees, and in

E ~

RROII'IJ~GR ~ I ~ I{ROIRgl}GE

,at

............................ l In

' e J

,t

Fig. 3. Flow-chart of operation procedure strategy.

124 A, B, ghaio' / Journal of MatertaL~" Processing l't, chm.h~gy 58 (1996) 121 - 130

i

Fig. 4. Structure of knowledge base.

the publications of the Machinability Data Centre, typically the Machining Data Handbook [3]. Besides these two main sources, many other technical papers, reference books and case studies are also available in literature. A protocol analysis, however, was used to gather ECM in readily formatted records of informa- tion which were expressed afterwards as facts about concepts and practices, and rules for the handling of the facts. No knowledge was elicited verbally from experts since it proved to be time consuming and difficult. Fig. 4 shows the structure of the knowledge base for ECM. It has tbur working modules: infor- mation-input module, machining procedure module, tool design module, and process economy module. Each of these modules requires certain types of knowledge.

The software is structured in two levels of hier- archy, as shown in Fig. 5, with the third level being the databases required lbr the assigned modules. There are six databases used in the program, namely:

(i) ECM-candidature database: which contains in- ibrmation about the suitability of a given workpart for machining by ECM Ii'om the outset. The inlbr- marion is related to the material hardness or tough. heSS, complexity of part geometry, post-machining cold work, size of production, state of residual or imposed stresses, and surfac~ integrity.

Control ] prollram

1 _ [

I J

Fig, 5, Hierarchial ECM structure,

(ii) ECM-limits database: which has documented information pertinent to the range of power supply ratings, types of electrolyte, their concentrations and temperature, the flow rate, velocity, and pumping pressure, working gaps and dimensional tolerances.

(iii) Material database: which embodies informa- tion concerning workpiece and electrode materials designation (only according to AISI-SAE or ASTM), composition, electrochemical properties, and material removal rate for pure metals and alloys. At the time of writing, the database contains data on 80 alloys commensurate with ECM applications, along with data on 37 pure metals. Ten specific alloys are suit- able for tool electrode. Any other alloy can eventu- ally be included in this database.

(iv) Technological-merits database: this contains textual and numerical information concerning pro- cess optimal performance including certain machin- ing geometries, electrolytes, tool electrodes special materials and rigidity, surface integrity, tolerances, etc.

(v) Machining parameters database: which is al- gorithm-oriented information to compute the resul- tant machining parameters including the anode projected area, maximum current density, start and end amperage, equilibrium feed rate and working voltage (open-gap). The mathematical expressions for these parameters are concisely given in Appendix A.

(vi) Electrolyte database: which is application.ori- ented information tbr a given workpiece material. The in!brmation includes electrolyte type (composi- tion pure or mixed), concentration, temperature at inlet, 'lnd pumping pressure. At the time of writing the database is limited to only pure neutral elec. trolytes and 13 mixed electrolytes to suit various ap- plications. Other types of nonaqueous liquids, molten salts, or gaseous electrolytes will be added later.

A set of cautionary remarks concerning specific se. lections are implemented in the databases to guide the user. The structure of these databases is designed based on the principles of relational schema. The top level in Fig. 5 is the controlling program that governs the module sequence, calls to the database, and user interface. All of the main activators and menus are located at this level. The four modules at the interme- diate level receive information in a processable format from the top level. The modules have a look-up table structure for easy user consultation. The function of each of these modules is explained below.

4.1. h~brmation-input module

This introductory module is of informative as well as confirmative nature. Prior to receiving any user instruc-

A, B, Kha~'ry / ,hmrmd ~l" Mago'ia/s Pna~'~,s,s'i~g F,'~'hm&agy 5S ( 999¢# ~ 21 130 ~ 25

tions, the system starts by displaying a cosmetic cau- tionary note that the work material ought to be electri- cally conductive. This is followed by 1he system enquiring about the work geometry and material desig- nation to check its suitability for ECM. When imlbrma- fion (i.e. facts} is supplied Io the system it goes on to assess six properties concerning the material hardness, shape complexity, surface status with respect to cold working effects and general integrity, edge finishing and scale of production, by consulting the ECM-candida- ture database. The latter, through the relational schema structure of databases [22], can retrieve the required information from databases as structured rules which are produced, for example, as numerical values of hardness or a rated percentage of surface integrity. The overall assessment is shown in terms of a verdict about the part being most suitable, suitable or unsuitable for ECM. If the given part is classified as belonging to the first or second rank the system proceeds, through inter- active mode with the user, to show the typical limits for ECM operating parameters as tabulated in the process- limits database.

4.2. Mach#l#~g-proce&we mmhde

This module makes use of a combination of struc- tured rules and algorithms to set up the off-line proce- dure for ECM of the part pie-assigned to the previous module. On the basis of part features and material the module determines the appropriate machining parame- ters by consulting the material, machining parameters and electrolyte databases. The selected parameters are displayed (in menu-driven structure} to the user with an option to modify. A machining procedure is delined on the basis of the given part's information and the rules embedded in the knowledge base. This constitutes the 'primitive' choice of the machining procedure. All the restrictions of the process and machinery limits are taken into account at this stage. If the user wishes, another refined phase of machining procedure can be invoked which will show if a preferred selection of procedure items is available. This is accomplished using several technological rules (developed from nonroutine experiences and facts reported in the literature) formu- lated by consulting information contained in the tech- nological-merits database. Upon selection of the machining procedures, the details of various steps in- volved are structured with corresponding values of machining parameters tbr possible repeated use on similar jobs.

separate finite demem analysis iFEA} package ~o co~° struc~ ~he opthnum contouring of eIectrode tool that will match the part geometry, otherwise a simpte method of appro×imate tool shape design can be used. based on recommendations given m [3], and summa- rized in Appendix B. If the work part has been dealt with by the KBS before, the module will directly re- trieve its predesigned tool geometry from a machinery database 0f implemented).

4.4. Process ecommn' too&de

In this module, the cost elements pertaining to tooi- h~g material, electrolytes, sludge disposal and machin- ing time are considered at the sealing stage of operation planning. The choice of trepanning, for example, to reph|ce a conventional ECM drilling operation is easily picked up through a group of icons in a CAD menu and by consulting the technological merits database. Comparative electrolytes cost indices are also included in this database to facilitate their choice i f many are compatible with the job. If a question about part material is still open, a machinability index ststem, like that described in [23], can be implemented to rate the material choice fi'om other candidates in the material database. Although structuring the KBS into functional modules has considerably simplified the consultation process, on many occasions there has been a trade-off between system modularity and tile final approved choice by the user.

In summary, rich knowledge about the ECM process is formulated in the form of production rules IIF: THEN/ELSE) in the control program which functions as a recta-level governor. Depending upon the user°supo- plied information aboul the work pal'l, the control program invokes the main menu schema in the input information model. Having retrieved appropriate inlbr- marion for a given work part, the machining procedure module is invoked and interacts with the |ool design and process economy modules. These modules have generic rules and algorithms, which interface with their respective databases. The meta-rules in the controlling program feed the retrieved information from the ma- chining parameters, electrolyte, and technological mer- its databases to the machining procedure moduie which generates the machining procedure and parameters re- quired by the user. All transversal interactions between the four modules in intermediate level are governed by the controlling program.

4.3. Tool design module 5. Implementing the KBS for ECM

This module is invoked to perform one of two func- tions. If the work part is ne~ to the system the module goes on to consult, based on given part features, a

There are two distinct approaches to the implementa- tion of a rule-based KBS on micro-computers. The first approach is to focus on a selection from among the

126 A.B. hTlaip3' / Journal of Materials Processiog Techm~h~gy 58 (1996) 121-130

Struct MACHINING char metalp [10]; char metalp [10]; float size [10.10.10]; float tolerance [5.5.51 float ECP float mrr; int jmax: int volt; int amperage; int feedrate;

/* alloy standard design */ /* tool alloy design */ /* part size */ /* tolerances */ /* electrochemical */ ;* metai rate */ /* max current density */ /* open-gap voltage */ /* working current */ /* equilibrium feed */

Fig. 6. Data structure in C format.

many commercial expert systems shells that are avail- able on the market. These shells provide supporting routines for the development (induction) of production rules from examples (data) which serves to reduce considerably the labour involved in such an effort. Alternatively, we can use a development programming language that can handle both conventional and sym- bolic programming issues of the KBS. Although *,1 languages like LISP and PROLOG have attracted much attention in the expert systems environment for some time [23], the new trend, emerging recently, is closer to conventional programming languages like C. The primary advantage of building KBS using conven- tional programming languages is better control over the entire consultation/inference process, as well as the ability to modify, or enhance any portion of the sys. tern. The requirements for the KBS include structured and easily accessible databases, and several modules thai attain sets of domain-specific heuristics, rules lbr drawing inferences, algorithms for quantification, and data tbrmatting for information display. All these functions are highly interactive, and could be carried out in real time if ECM numerical controllers are availale, The data structure in C language satisfies all these requirements [24] and proved to be most efficient in problem solving even when competing with A! lan- guages [i 5,25].

The information that is generated for a work part is stored in pre-assigned data structures. The machining

parameters are systematically assigned to their appro- priate structure member by the machining procedure module. As an example of the data structure used in the KBS, the MACHINING structure in C format is illustrated in Fig. 6. The 'metal p' and 'metal t' arrays are C declarations of the standard designations work part and tool, respectively. The remainder of the struc- ture contains the necessary machining parameters. This data structure would be available for each module so that the required information for each work part can be easily cited. The user supplied information on part material type size, tolerances, etc., are passed onto the machining procedure module/tool design module which provides the interface between the material, machining parameters, technological merits and electrolyte data- bases and the user. Fig. 7 shows schematicaUy the schema for the machining parameters database. The relationships between attributes and parameters are then translated into a relational database and imple- mented. The machining procedure module operates by prompting the user to enter each attribute consecu- ti,~aly in a menu list. At each step a list of all possible knowledge is displayed. For example the first prompt asks for the material type of the part. A list of all this material's physical specifications in the database is dis- played for user's knowledge. This process is repeated for each attribute of the operation in hand.

Once all the queries have been put, the operating parameters are uniquely defined in the domain of the current database. The appropriate parameters corre- sponding to each query are found from menu assign- ments and are displayed on the monitor screen before being placed in the current machining structure, Fig. 6. The parameters may then be examined and altered by the user if desired, or refined further by consulting the technological merits database.

The consultation process continues to occur smoothly through the tbur intermediate modules, their elicitation of data from database, and under full coor- dination of a stepped procedure by the controlling program.

Fig, 7. Schema of machining parameters database.

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J - 7

~5 ~15

..... T k L~ 30 . . . . . . .

(Tolerance = +_0.05 R, = 0.5 pm Dims, in ram),

Fig. 8. Part geometry and specifications.

6. Experimenta| verification and discussion

In order to verify the KBS discussed above for ECM applications, a computer workstation (HP-RC20) is interfaced with an ECM machining cell. The worksta- tion hosts the software for KBS and acts as the super- visory controller and decision-maker for the cell environment. The following illustrative example will be used to demonstrate how the system provides off-line consultancy in practical situations. The ECM machine tool used is a Charm(lies A3000 which will be used to verify the machining procedure tailored by the KBS.

Fig. 8 shows a part with a through-hole which is the only surface required to be machined by ECM. The part general specifications (attributes, Fig. 7) are given including material type, dimensions, tolerences, surface roughness and cy!indericity. Values for the last three parameters are assumed, lbr the moment, to lie with- in the typical ECM values, as shown in a supplied drawing.

The part material, AISi 4340, is a high strength steel that is typically used in critical applications such as connecting rods, landing gears and many precision engineering components. To set up the KBS the user must assign the alloy name to answer a query from the initial menu. The system issues a cautionary notice emphasizing that the material ought to be electrically • ~ ,° v conductl e. Although humble, this wanting message is useful for users not accustomed to ECM and who may confuse it with other processes, e.g. plasma machining, or EDM, which can occasionally handle semiconduc- tive materials and ceramics. The first prompt checks the candidature of the given material for ECM. If not overridden by the user, the knowledge-base will check first the material hardness as obtained from the mate- rial database by rule 05.

Rule 05 IF:

THEN:

ELSE:

The material vickers hardness is less than 500 Material is thought to be unsuitable for ECM, Apply rule 06

If the above rule conclusion (right-hand side) is de- nied, it will work as a meta-rule for rule 06:

Ruk 06 IF:

THEN-

Diffictdt contours are involved, and more than 30 pieces ~vill be produced Part is thought to be suitabte for ECM

Once more rule 06 can be used as a meta-rule for further inference to achieve the 'most suitable' ECM applicability, i.e.

THEN:

Rule 07 IF: Part is thought to be suitable for

ECM, and The surl:ace to be stress-flee, and The edges to be burr-free, and A high surface integrity is required Part is most suitable for ECM

which was tile conclusion accepted for the given exam- pie. A few other rules can be triggered prior to the preceding rules for matching data provided by the user concerning machine tool capabilities with that of a machine already in the knowledge base. The system can now provide the user with a display of alloy properties as extracted fi'om the materia~ database as follows:

alloy type = AIAI 4340 composition = 0.40% C, 2.0 Ni, 0,8

Cr, 0.7% Mn, 0.25% Mo

chemical equivalent = 2.65 (calculated by the percentage by weight method) assumed valency ..... (could also-- 3)

theoretic~d removal rate = 2.18 cm~/min, (tabulated, assuming 100% dissolution current ~fl~cle~ cy)

The values displayed for chemical equivalent and theoretical removal rate are shown at this early stage only for guidance. The actual values of these parame- ters will be rectified flora calculations of machining parameters. The user can have further guidance through displaying the contents of ECM limits data- base; as shown in Table 1. In fact the information about process limits can be restored rapidly any time

~,-,,v-~,,'-" claecks during the KBS consulting ses- sion. The knowledge base will continue prompting the user to accomplish the steps of the machining proce- dure. In the next phase the user should input the dimensions of the projected area, Ap (the tool area perpendicular to the feed direction) within the machin- ing gap (Appendix A), or this r, hould have been (den- tiffed earlier from data stored about the part in the

128 A,B, Khalo' / Journal of MateriaLs' Processing Technoh~gy 58 (1996) 121 - 130

CAD interface (an ASCII/DXF or IGES.file). To cope with either possibility two declarative rules are fired to the user

Rule 09 IF:

THEN: ELSE:

and

Dimensions of the part are unknown Restore data from the user Restore data from DXF, or IGES file(s)

Rule 10 IF:

THEN:

Dimensions of the part are shown Apply algorithm 5.1 to calculate the projected area

Algorithm 5. I enables the machining procedure mod- ule to calculate the projected area at the frontal ma- chining gap. A set of algorithms are used consecutively to calculate the maximum current density (J,,,,0 avail- able from the machine power generator, equilibrium feed rate (f), current at equilibrium (1), and open-gap voltage (V). Typical values for the part shown in Fig. 8 are, respectively,

Ap ~. 0.196 cm -~ J,,,,,~ ~ 15 300 A/cnY' (rate ampere = 3000 A) f ~ I 1.000 era/rain (current efficiency = 100%) ! ~ 2999 A V ~ 10:~20 V(dc)

These values will be reconsidered by the system lbr further refinement when consulting the technological merits database as explained below.

The design of electrode tool for the given problem is a simple one, however, the user may opt to consult either of the algorithms explained above in the tool design module. For this particular part the KBS system is instructed to accept the goal of designing the tool electrode shape as a mirror image of the paws internal hole less the average along the hole side walls, i,e, side gap or overcut, This is cited by the knowledge base at the technological merits database for drilling or trepan- ning operations. The system will display the dimensions of suitable tool geometry (beside an extra length for tool clamping on machine head) to be

Total tool length = 32 (for drilling engagement)+ 18 {for tool clamping)= 50 mm

Total diameter = 4,98 (0,02 mm dimensional tolerance)

The tool clamping length and a rigidity index, based on length/diameter ratio for selected tool material, are both determined by system rules and displayed to the user, Now the tool material will be selected from 10

recommended alloys for tool electrode material avail- able in the material database. For our particular exam- ple the system selected naval brass (60 Cu = 39 Zn) as the best general purpose electrode material. Since drilling is done only at the frontal machining gap, the side walls of the tool are coated with flexiglass and the reason for this conclusion is given as 'being suitable for insulating the tools used for production of a small number of parts'. All the choices made by the knowl- edge base can be explained, and extra useful remarks or hints are provided for the user.

The work materials of cathode and anode are both well identified up to the present stage. It is only the electrolyte that needs to be selected. The selection pro- cess shows that 12'/,, (wt./vol.) NaC! solution will be a good choice, or a mixture of 12'¼, NaC! and 6% NaNO~ electrolytes can be used to minimize surface pitting along the side walls of the hole as well as reducing the taper. The recommended inlet temperature of the elec- trolyte is from 35 to 38 °C, inlet pressure being 1.0 MPa and outlet pressure 0.3 MPa. All the conclusions made by the knowledge base so far are written to a file that keeps all these choices in procedural form suitable for testing, and maybe modification, by the user. Before this procedure is finally produced the system can revise its earlier choices for the current efficiency, and can give hints of expected deviations in hole geometry due to approximations in tool shape design, if required. The conclusions concerning tool material and mixed elec- trolyte are drawn by process economy rules. The tool material is indicated as easily repairable and cheap, whereas the mixed electrolyte, although more costly than pure NaCI, provides better surface integrity and tolerances,

Tile machining procedure with the machining parameters assigned to it by the KBS was subjected to a test run on the Charmilles.A 3000 machine tool. Five specimens of 4340 steel were ECMed using the system- atic step-by-step procedure and resulted in excellent proximity between dimensions and tolerances of speci- mens. The surface roughness of two specimens ma- chined in a mixed NaCI, NaNOs electrolyte was, on average 0.7 of that sustained for machining in pure NaCI electrolyte; R~ = 0 i mm and 0.07 mm, respec- tively. The system finally brings to the attention of the user the fact that the current efficiency of 100% can now be changed to 90% for pure electrolyte and 60% for mixed electrolytes, hence the pre-assigned feed rate should be modified.

7. Concluding remarks

A knowledge-based system (KBS) has been devel- oped to aid planning ECM operations. The major

A, B, Kh~ArV i J~mrsa'd ~/" M<~ser~als Pr~J~'ess#~g 7"e~'km,t~>ly 58 £ l ~)9(~j ~2 ~ 130 I 2 ~)

accomplishments of the research reported in this paper a r e :

(1) The developed syslem is modular, which allows for the addition of more detailed function modules or databases without allering the resl of the program.

(2) Botli heuristic and algorithmic procedures have been implemented to enjoy complete consideration of ECM practice. Step-by-step procedures have been de- veloped to set out the machining parameters, supported by an explanation mode of the conclusions reached by the system, and a facility to refine it.

(3) An experimental verification 'test run' has been conducted on an ECM machine. The KBS has been able to provide the necessary support required and successfully guided the part machining process.

For the knowledge base to be comprehensive, data on non-neutral electrolytes ought to be introduced. The system is user-friendly and can be used either by those having much ECM experience, or those who need a lot of guidance. The family of nonconventional machining processes are extremely good candidates lbr KBS be- cause of the considerable shopfloor expertise needed.

Perhaps in the next sta~,e of research a k~ov:le~e- based system can be developed ~o aid process selection ~o sui~ a particular material or vice-versa.

Appendi× A

The basic mathematical relationships between ECM parameters are as follows:

(1) Specific metal removal rate = C (chemical equiva- lent/specific weight) where C = 1/96 500.

(2) Maximum current density = rated amperage of equipment/projected electrode area.

(3) Equilibrium feed rate = (material removal rate × current efliciency)iprojecicd area,

Appendix B

The approximate design of c o n t o u r cutting electrodes (with insulated periphery) is given by [3], as shown in the Figure below.

+ Feed. v I

-.-,.=-I I-::.i l!', / ' F ( ~ I . i lo i I < 40

P ..... " X l b ~ i a ' i !ion ["~ ~lllOtjir il le Co~

,% angle,

I..i--, Projected ,,orlial area. Aw " - * ' l

• ANGLE 0 - - O" to 20" (o = 70" Io 90") Use mirror or conjugate image of parl and uniform gap. Sc ,s Se.

® ANGLE 8 - - 20" 1o 40" (a - 50 a Io 70") Correct tool shape from unilofm gap Io Sc : Se sec 0 or Sc" ~ sin a"

• ANGLE ~ -- greater than 40 ° (o less than 50") Develop iDOl shape by trial cuts and correction.

• An Approximation: AGap ~ Se (sec e - cos e }

130 A.B. Khairy / Journal qf Materhds Processing Te,'hm,h,gv 58 (1996) 121 -130

Appendix C

Table C! Typical values for ECM operating parameters [3]

Power supply current density: 8 to 465 A/cm: type: dc ratings: 50 to 40 000 A voltage: 5 to 30 V

Electrolyte type and concentration: most used: NaC! at 60 to 240 g/I frequently used: NaNO~ 120 to 480 g/I less frequently used: Proprietary mixtures velocity: 1500 to 3000 m/min flow rate: 0.95 l/min/lO0 A

Frontal working gap: 0.076 to 0.76 mm Side overcut: 0.127 to I mm Electrode material: Brass. copper, bronze

Tolerance two-dimensional shapes: _+0.025 mm

Surface roughness. R,, frontal: 0,2 to 1.6 mm

inlet pressure: 1.37 to 2060 kPa outlet pressure: 0 to 310 kPa

temperature: 26 to 46 °C

three-dimensional shapes: +0.051 mm

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