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ELSEVIER

Artificial Intelligence in Engineering 12 1998) 121-134

0 1997 Elsevier Science Limited

Printed in Great Britain. All rights reserved

PII: SO954-1810(97)00011-3

0954-1810/98/ 19.00

Artificial intelligence approaches to determination

of CNC machining parameters in manufacturing:

a review

Kyung Sam Park Soung

Hie Kim*

Graduat e School o f Management, Ko rea Advanced I nsti tu t e of Science and Technology, 207-43 Choengryangri , Dongdaemun,

Seoul , K orea

(Received 1 March 1995; in revised version 13 May 1996; accepted 5 February 1997)

In Computer Numerical Control (CNC) machining, determining optimum or

appropriate cutting parameters can minimize machining errors such as tool

breakage, tool deflection and tool wear, thus yielding a high productivity or

minimum cost. There have been a number of attempts to determine the

machining parameters through off-line adjustment or on-line adaptive control.

These attempts use many different kinds of techniques: CAD-based approaches,

Operations Research approaches, and Artificial Intelligence (AI) approaches.

After describing an overview of these approaches, we will focus on reviewing

AI-based techniques for providing a better understanding of these techniques in

machining control. AI-based methods fall into three categories: knowledge-based

expert systems approach, neural networks approach and probabilistic inference

approach. In particular, recent research interests mainly tend to develop on-line

or real-time expert systems for adapting machining parameters. The use of AI

techniques would be valuable for the purpose. 0 1997 Elsevier Science Limited.

Key w ords: CNC machining, machining parameter, knowledge-based expert

system, neural network, influence diagram.

1 INTRODUCTION

Computer Numerical Control (CNC) machining is

widely used in mold/die industries and airframe

component manufacturing, because of its suitability

for high accuracy in machining complicated parts.’ In

the CNC machining, determining optimal cutting

conditions or parameters under the given machining

situation is difficult in practice. Conventional way for

selecting these conditions such as cutting speed and

feedrate, has been based upon data from machining

handbooks and/or on the experience and knowledge

on the part of programmer. The selected parameters,

in most cases, are extremely conservative to protect

excessive matching errors from tool failures such as tool

deflection, wear, breakage, etc. As a result, the metal

removal rate is low because of the use of such

conservative machining parameters.

As frequently encountered in complex or free-formed

surface machining, the geometry of the part or work-

piece prevents a constant depth and width of cut.

*Author to whom correspondence should be addressed.

127

Consequently, the conservative cutting conditions

assuming a constant depth and width of cut do not

perform high productivity. To overcome such a prob-

lem, the machining parameters should be adjusted

according to the current in-process part geometry.

The objective of this paper is to review prior work on

determining machining parameters in order to give a

better understanding to researchers and practitioners

in machining domain, since we have not found any

publication on the survey. In particular, we will focus on

examining Artificial Intelligence (AI)-based methods. In

the next section, we describe an overview of related

work that attempts to select an optimal machining

parameters, and present AI-based approaches in the

subsequent sections.

2 AN OVERVIEW

Recently, there have been several attempts to deter-

mine the optimal machining parameters from off-line

adjustment or on-line adaptive control, thus the part

programmer does not have to spend time and effort to



128

Ky ung Sam Park, Soung Hie Kim

calculate their optimal values. These attempts are

categorized as CAD-based approaches, Operations

Research (OR) approaches, and AI approaches.‘3

2.1 CAD-based

approaches

The off-line approach uses machining process models,

cutting force and tool wear models, based on a prior

knowledge gathered from off-line experiments. Based on

the process models, cutting force and tool wear are

calculated through computer machining simulation

(CMS) using information on NC-code with initial

machining parameters, tool shape and workpiece

geometry. Using the results, an optimum machining

parameter for each tool motion is achieved by maxi-

mizing the metal removal rate (MRR) without violating

machining constraints.

The basic concept of optimizing machining param-

eters is that when the cutting force is too large at the

large depth and width of cut, either low feedrate or high

cutting speed, or both can be added to the NC-code.

However, note that too high cutting speed can not be

selected since the tool life is largely due to the cutting

speed.5 Most CAD-based approaches belong to the off-

line adjustment. Advantages of these methods are that

they are easy and effective in practical applications.

Figure 1 shows a conceptual framework for simulation

and optimization of machining.

M achini ng process model s

The machining process models represent the rela-

tionships between the machining responses (i.e., cutting

force and tool wear) and the machining conditions in a

specific tool and workpiece. These models can be built

by prior knowledge obtained from field and laboratory

experiments. An example of machining process model

based on a multiplicative model is given by

Cutting Force (N), FC = al d2f a3dn4w aS,

Tool Life (min.),

TL = blv b2f b3db4w b5,

where V,

f, d

and w are, respectively, cutting speed

(mm/min), feedrate (mm/tooth), depth of cut (mm),

and width of cut (mm); and oi and J3i are the model

parameters.

The multiplicative model can be generated from

statistically planned machining tests (see Ref. 11). An

advantage of this model is easily obtained even though

geometry of cutting tool is complicated such as ball-end

mill. Furthermore, it is reported the accuracy of the

models is quite good.

‘J~J’ The above models will play a

role of constraints in optimizing machining parameters.

In addition, analytical process models for the prediction

of cutting force have been studied,7~8110V17nd their

application to on-line feedrate adjustment in end milling

has been found in Ref. 6. However, a difficulty may exist

in using such analytic models in practice because of their

high computational complexity.

Computer machining simulation

The main objective of CMS for determining machining

parameters is to compute the maximum depth of cut d)

and width of cut (w) for each tool motion from given

part geometry, NC-code and tool configuration. Why

compute the maximum point? The reason is machining

error from the tool failures is mostly occurred at the

maximum point. CMS of in-process workpiece can be

realized as a Boolean subtraction of the space occupied

by the tool movement along the tool path from initial

part geometry. Hence, it is first needed to represent the

part geometry for CMS.

Solid modeling’8>‘912’ r Z-buffer techniques22>23have

been used to model workpiece geometry for CMS. A

pape?’ has proposed a method of feedrate adjustment

using a swept volume generation technique based on

solid modeling. However, this method can calculate

only average cutting force, thus it does not provide the

instantaneous cutting force that is necessary for

estimating the tool failures. Z-buffer model is a

form of discrete nonparametric representation in

which the Z-values of the surface are given at grid

points on the XY-plane. More detailed description

on Z-buffer model and its application to control

and monitoring of machining can be found in the

literature.22’23

M achining parameter optimi zation

Based on the CMS and the machining process models,

feedrate

(f

)

and cutting speed (v) are determined in the

/,+omputer Machining Simulation

chining Process Models

I

Fig. 1.

A framework for simulation and optimizaiton of CNC machining.



Determi nati on of CNC machining parameters

129

optimization module. For increasing the productivity,

MRR has to be maximized while maintaining an

allowable load fluctuation on the cutting tool in spite

of variations in depth of cut and width of cut. The MRR

is expressed as

MRR = kvfdw,

where

k = n/ rD), n

is

the number of tooth, and D the diameter of the

tool.’ A mathematical model for such problem can be

formulated as follows:

Model 1: Maximize

MRR = kvfdw

subject to v,in 5 v 5 vmax

FC I JG

TLmin I TL 5 TL

HP 5 HPr

where HP represents

the spindle horsepower

(Nmm/min) as a constraint for the machine capacity,

and I’min and Vmax,

respectively, are minimum and

maximum allowable values of

V. HP

is expressed based

on the FC as HP = c - FC - v, where c is 0.041 as a

constant.5 Once taking natural logarithms in Model 1,

it is converted into the standard linear programming

(LP) form. Thus, the LP problem can be solved by

using a general algorithm referred to as the

Simplex

method.24

2.2 Operations research approaches

Of course, the use of the above LP technique can be

viewed as an OR approach. However, main research

interest of OR approaches is to minimize global

machining cost by considering multiple criteria related

to machining, thus which problem is to solve a multiple

criteria optimization problem (for an overview of the

multiple criteria optimization problem, see Refs 25,

26). These methods should be used for off-line

adjustment because of the restriction of computational

time. An advantage of these methods can provide a

reference model, i.e.,

a general model because an

exhaustive consideration on

selecting machining

parameters is involved. A typical research is found in

Ref. 27. According to the research, the model without

describing full mathematical form can be expressed as

follows:

Model 2: Maximize {MMR}

Minimize {surface roughness}

Minimize {machining cost}

subject to the constraints of Model 1.

On surface roughness, there are two methods of its

measuring:

average.2y3

root-to-crest roughness and roughness

The factors of measuring machining cost

per workpiece are cost of tool, cost of cutting, and costs

associated with machine idle time, due to setup, loading

and unloading and tool changes to replace worn-out or

damaged tools. Solving Model 2 is more complex

because the model have multiple objectives and con-

flicting between the objectives (a mathematical repre-

sentation and the solution method for Model 2 appear

in Ref. 27).

2.3 Artificial intelligence approaches

The on-line approach is an attempt to automatically

adapt and optimize the machining parameters based on

sensor information on machining responses in real time,

without CMS. Well-known sensor information is listed

as cutting force, tool wear, tool temperature and

acoustic emission. Note that the information of tem-

perature and acoustic emission can not be used in off-

line methods using CMS. These information, however,

can play very important role in machining control

or adapting machining parameters, praticularly when

occurring an abnormal machining due to unpredictable

variables such as unknown material properties, tool

conditions, etc.

For on-line control, the following components or

techniques are required: (1) sensing devises, (2) repre-

senting the information from the sensor, and (3)

optimizing machining parameters. A description of

sensing devises is not presented in this paper. For the

description, refer to Refs 4, 16.

AI approaches offer a possible technique in order to

handle the problems (2) and (3). One of the most

important factors for successive on-line control is the

execution time with respect to machining control or

determining optimal machining parameters. Reaction to

machining conditions by tool wear, machine break-

downs and other failures must be carried out within

seconds or milliseconds to guarantee the safety and

reliability of the machining process. However, a

simplistic adaptation of AI techniques to machining

control would be inadequate, because execution time of

these systems are generally too long as compared with the

reaction time required for the machining control,

particularly if the knowledge base becomes very complex.

There have been a number of studies on the

application of AI techniques

t

on-line control, which

we categorize into knowledge-based expert systems

approach, neural networks approach and probabilistic

inference approach. Each approach is described in the

next subsequent sections.

3

KNOWLEDGE BASED EXPERT SYSTEMS

APPROACH

3.1 An overview

Knowledge-Based Expert Systems (KBES) are intel-

ligent computer programs that capture the specific



13

Kyung Sam Park, Soung Hie Kim

knowledge of a particular domain and mimic the

problem-solving strategies of human experts to provide

recommendations?8-3o

They represent a new problem-

solving paradigm that utilizes many techniques developed

from AI research. The KBES can capture causal and

inferential knowledge about machining processes to

provide expert-level recommendations during decision-

making processes and hence are valuable aids to

machining operators who face increasingly complex tasks.

With the KBES technique, machining control deci-

sions using the sensor information can be made to

maintain the machining parameters within critical

constraints. Strictly speaking, on-line control with the

KBES is an adaptive control of satisfying machining

constraints, simply stated ACC, rather than an adaptive

control with optimization, ACO. Whereas AC0 systems

seek to adjust machining parameters in a direction that

optimize a predefined performance index, i.e., objective

such as MRR, the aim of ACC systems is that the

machining parameters are adjusted to their maximum

possible values given the constraints of the machining

process.’ Recent research on machining control using

KBES techniques has been found in Refs 31, 32. In the

next subsection, we will describe a KBES framework for

machining control and present an example of simple

production rules for the determination of machining

parameters.

3.2 Structure of KBES for adaptive control

A structure of the KBES approach for machining

control is shown in Fig. 2. It consists of three modules:

a knowledge base, an inference engine, and a sensor data

acquisition and processing module. The inference engine

drives the system and interfaces with the knowledge

base and hence supplies advice to the user and an

explanation to justify the system’s line of reasoning. The

knowledge base can provide near-optimal machining

control with experimental data. The methods for

inference can be modeled as rules, e.g., IF (antecedent)

THEN (consequence).

To achieve the near-optimal machining parameters

and machining control such as tool change, machining

Knowledge Base

stop, and so on, it is necessary that an adaptive control

algorithm that uses the recursive adaptive model and

the constraint rules is developed. For example, the

constraint rules can be expressed as shown in Fig. 3.

In KBES, many techniques for knowledge representa-

tion have been developed, for instance, production

rules, semantic nets, frames, etc. The type of knowledge

representation that is appropriate in a given situation

depends on what sort of knowledge is being represented

and how it is to be applied. In time-critical machining

control applications, it is imperative the knowledge

representation scheme is efficient. Among the KBES

approaches to machining control, in Ref. 31 a frame-

based scheme is used, and in Ref. 32 a production rule

representation is applied such as shown in the above

paragraph.

4 NEURAL NETWORKS APPROACH

4.1 An overview

Neural networks differ in various ways from con-

ventional expert systems to traditional computing. The

reasons are as follows. First, unlike traditional expert

systems where knowledge is made explicit, neural nets

generate their own knowledge by learning from domain

examples. This means that neural nets can easily

make the knowledge base by learning, and they do not

require additional knowledge acquisition processes

which require enormous time and efforts in the expert

systems. Supervised learning is achieved through the

learning rule which adapts the connection weights of the

network in response to the inputs and the desired output

pairs. Many other network learning rules have been

inverted also in Ref. 34.

Second, neural computing is both distributed and

associative in knowledge representation.33 The dis-

tributed and associative nature of neural net leads to

a reasonable response even when presented with

incomplete or previously unseen input. In particular,

multi-layer neural nets which register in their hidden

layers important features of the knowledge domain,

Fig. 2.

A KBES structure for machining control.




131

Rules or Finding MachiningSituation:

Rule 1: IF (NOT (A&c,,?,,, ‘: Acstic c AcsticJ)

/* Acstic = Intensity of Acoustic etmss~?n from sensor */

THEN (Stop the machining operatmn AND Change tool If necessary)

Rule

2: IF (NOT (Temp nil” < Temp - Temp,> ,,,” )

I* Temp : Temperature from sensor *I

feedforward neural net with one hidden layer is shown

in Fig. 4. Each node or processing element (PE) in every

layer is fully connected to other PE in the proceeding

layer, and ever PE sums its weighted inputs and passes

through some kind of transfer function such as linear

or sigmoid functions. The learning parameters are the

connection weights and the PE’s parameters, i.e.,

threshold values.

THEN (Stop the machining operation AND Change tool if necessary)

Rule : IF

((FC[k] J FC[k]J AND (feedm” c feed))

/* FC[k]-Cuttmg Force at axis k. k=x,y, from sensor. feed=Feedrate ‘1

THEN (feed = feed 0 01 until FC[k] L FC[kJ,J

Rule

6: IF

FC[k] -z

FC[k],J AND (t‘eed I feed”,,,\) AND (speed speed,,,J)

THEN (speed = speed + 0 02 until FC[k] < FC’[k],,,,,b)

Rule 7: IF

FC[k] -’

F(‘[k],,,,\) AND ( feed < feed,,,J)

THEN (feed = feed + 0 01 unti FC[k] : FC[k]“J

As a learning rule, Rumelhart and McClelland33 have

developed the generalized delta rule called back-

propagation algorithm that is basically a gradient

method. This rule aims at minimizing the global error

of the system by adjusting the learning parameters. The

backpropagation algorithm does not always find global

minimum but may stop at a local minimum. However,

in most cases, the system can usually be driven to the

global minimum or to the desired accuracy with an

appropriate choice of hidden PEs. The number of

hidden PEs must be large enough to form a decision

region that is as complex as required by the given

problem, and on the other hand is small enough that the

generalization ability remains good.

Rule n: IF FC[k] :’ FC[k],J AND (feed 1 feed”,,,\) AND (speed ? speed,,>J)

4.3

Optimal control phase

THEN (Sto p machinmg operatmn)

Fig. 3. An example of rules for adapting machining parameters.

can use this

hidden knowledge to generate non-trivial

generalizations.

Assuming that a neural network has been trained by the

procedure mentioned previously, then the objective of

optimal control phase is to determine an appropriate

machining parameter that optimizes a performance

index, given machining constraints on the network

outputs.

In machining domain, neural nets can possess abilities

to learn from experience and to use the knowledge

gathered during the learning process to optimize the

machining control. Experience is represented by input-

output data, where input variables are machining

parameters such as feedrate and cutting speed and

output variables are signals from sensors such as cutting

force, tool wear, temperature and acoustic emission. The

aim of learning is to establish a generalized mapping

between the input and output, where note that this

statement is a supervised learning. This section deals

with a supervised learning approach (see Refs 35,36). For

unsupervised learning approaches refer to Refs 37, 38.

Let us consider a trained neural net with

n

input PEs

and m output PEs. Let ai and dip espectively, be actual

and desired output of the ith output PE. For k of the m

output nodes, the di represent the desired outputs of the

PEs, whereas for the remaining m - k PEs, the di is the

maximum allowable outputs of the PEs (e.g., cutting

force and horsepower). Then, a performance index PI

is defined by

PI = w 1 ERR - w 2. M RR,

where

MRR

represents metal removal rate (see Section 2.1)

ERR = 5 di - ai )2/2,

i=l

In addition, there are several techniques for repre-

senting the input-output

relationship:

multiple

regression (see Section 2.1), the group method of data

handling, and the neural network (see next subsection).

A study4 has reported that the neural network is the

most effective method for tool wear identification

through a comparative analysis among the above

techniques. Furthermore, neural net knowledge-base

acquired during the learning phase can subsequently be

used for determining optimal machining parameters.

4.2

Learning phase

For building machining knowledge base, a two-layer

Fig. 4. A neural net structure for representing machining

knowledge.

output

layer

Hidden

layer

Input

layer



132

Kyung Sam Park, Soung Hie Kim

and w1 and w2 are constants that represent the relative

importance of

ERR

and

MRR.

Machining optimization problem is to find the n

inputs, denoted by pi, that minimize PI subject to the

following constraints:

Pmini I

Pi Pmaqr

i=

1,...,n,

di - ai > 0,

i=k+l,...,m.

The solution method of this constrained minimization

problem can be found in Ref. 35.

5 PROBABILISTIC INFERENCE APPROACH

Agogino

et a1.43

have proposed an influence diagram

as a framework for integrating machining operator’s

expertise, first-principle knowledge and experimental

data for the wide range of sensors possible for in-process

monitoring and control. The use of multiple sensors

reduces the sensitivity of the system to any specific

sensor’s drawbacks. The non-deterministic or prob-

abilistic nature of the inference problem and noisy

sensor data is handled by operations with Bayesian

probability.

Influence diagram has been developed for represent-

ing complex decision problems based on incomplete and

uncertain information from a variety of sources.39~44

Knowledge of the interrelationships between variables is

represented in a compact graphical and numerical

framework which identifies the critical variables and

explicitly reveals any conditional independence between

them.

The knowledge representation using influence dia-

grams can be viewed from three hierarchical levels:

topological, functional and numerical level.39 At the

topological or relational level, the nodes in the diagram

represent the key variables in the system being modeled,

and the arcs or arrows identifies conditional influences

or functional relations between the nodes. In the CNC

machining, examples of key variables are machining

parameters and machining responses from sensor

information. The nature of the influences is specified

at the functional level and further quantified at the

numerical level.

From the discussion in the preceding two paragraphs,

influence diagram is defined by an acyclic directed-graph

G = (N, A) with A c N x N: It contains three types of

nodes in the node set N. The chance node, which is

circular shape, represents uncertain or certain states

(e.g., cutting force, tool wear, acoustic emission), the

rectangular-shaped decision node (e.g., feedrate, cutting

speed) reveals a variables whose value is chosen by the

decision maker, and the diamond-shaped value node

(e.g., metal removal rate) represents the objective to be

maximized in expectation by the decision analysis. It

should be noted that influence diagrams on the

topological level do not need a mathematical or

probabilistic basis to justify themselves. Their influences

are justified by mathematical or probabilistic repre-

sentation at the functional level. At the final level,

numerical level, utilities of the decision maker, and

probability distributions from prior information by

experiments are assessed numerically for each node.

Shown in Fig. 5 is a simple example of an influence

diagram for machining optimization.

Once a complete influence diagram is generated, the

diagram is manipulated and evaluated for determining

the optimal decision strategy. A direct solution proce-

dure to automate influence diagrams has been proposed

in Refs 40-42. This algorithm consists of the value-

preserving translations, node removal and arc reversals,

which correspond to the rollback procedure in deci-

sion tree models.45 For more detailed description on

applied influence diagrams to machining monitoring

and control, see Ref. 43.

6 CONCLUDING REMARKS

This paper presented a survey of prior studies on

determining an optimal machining parameter and

machining control. We particularly focused on reviewing

Fig. 5. An influence diagram for determining machining parameters.




133

AI-based methods for on-line adaptive control, i.e.,

KBES, neural networks and probabilistic inference

approaches. These approaches are commonly based on

the sensor information of machining responses in real

time, and based upon the prior knowledge from

machining experiments in field and laboratory, such

as machining constraints, machinability data (i.e.,

mechanical and material data of cutting tool and

workpiece), etc. The prior knowledge can be elicited

from data of machining handbooks (e.g., see Ref. 14)

or the experience and knowledge of the part

programmer.

Observe that the difference that underlies AI

approaches is only the style of knowledge base being

represented in the system and its reasoning (or inference)

for machining optimization and control. That is: The

KBES approach uses a knowledge base such as

production rules (see Ref. 32) and frame-based tech-

niques (see Ref. 31), with an inference engine. The

neural networks approach uses a feedforward network

model (see Refs 35, 36) based on supervised learning

rules or self-organization network model (see Refs 37,

38) based on unsupervised or competitive learning

rules, without an additional inference engine. The prob-

abilistic approach uses an influence diagram model (see

Ref. 43) with a probabilistic reasoning engine.

In building an intelligent machining control system, a

principle problem is how to represent knowledge in a

way that is suitable for a particular machining domain.

There have not been a specified guideline for the

choice of a knowledge representation scheme. The

general or conceptual guidelines are efficiency and

sufficiency, where sufficiency means that the precision

of the predictions we obtain meets our requirements,

and efficiency refers to their practical applicability. All

AI approaches mentioned in this paper may be sufficient

for representing a machining knowledge, although their

knowledge representation styles are different. If the

above statement is true, the most important criteria

becomes efficiency.

There may be some factors for measuring efficiency in

quantitative or qualitative manner, for example, easy for

naive user and reaction time. Among them, reaction

time would be the most important factor in real-time

machining control systems. Namely, reaction to

machining failures and unfavorable machining has to

be made within seconds or milliseconds to guarantee the

safety and reliability of the machining process. In this

point of view, we prefer a knowledge representation

scheme in which reaction time is less sensitive as the size

of the knowledge base increases.

Apart from the sufhciency, what kind of knowledge

representation technique is better than the others in a

time-critical machining control system? This paper can

not give an answer for this question, because there

have not been a comparative analysis among the AI

approaches. The problem in order to address this

question is a promising further research issue.

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