A neural network approach for datum selection in computer-aided process planning

COMPIITERS

INDUSTRY

ELSEVIER

IN

Computers in Industry 27 (1995) 53-64

A neural ne,twork approach for datum selection in computer-aided process planning J:iannan Mei a, Hong-C. Zhang aT*, William J.B. Oldham b a Department of Indush-id Engineering, Texas Tech Uniuersity, Lubbock, TX 79409-3061, USA b Department of Computer Science, Texas Tech Uniuersity, Lubbock l2! 79409-3061, USA Received 5 August 1994; revised 17 January 1995

Abstract The goal of proces,s planning is to convert design specifications into manufacturing instructions to make products within the specifications at the lowest cost. Therefore, for a computer-aided process planning system (CAPP) to generate a feasible and economical process plan, the tolerance information from design and manufacturing processes must be carefully studied. The geometric tolerances are usually specified in design only when higher accuracy of a feature (such as flatness, roundness, etc.) or a relationship (such as parallelism, perpendicularity, etc.) is required. For the relationships with dimensional tolerances or geometric tolerances with specified design datum(s), the selection of manufacturing datum and setup in process planning plays a very important role to make parts precisely and economically. This paper presents a neural network approach for CAPP to automatically select manufacturing datums for rotational parts on the basis of the shape of the parts and tolerance constraints. A back-propagation algorithm is used and some experiments are conducted. The results are analyzed and further research is proposed.

1. Introduction Automated tolerance analysis is one of the most critical problems for computer-aided process planning (CAPP) systems to be applied in the real manufacturing environment. The tolerance specifications from a design model should be used to select proper processes, machines, setups, manufacturing datums, tools, and cutting parameters and to generate process plans and inspection plans. Traditionally, CAPP uses only shape and nominal dimensions to generate an operation sequence, assigns tolerances to the operations, and then uses the dimensional tolerance chain

* Corresponding author.

method to check it. Neither setups nor manufacturing datum elements are selected based on the tolerance specifications and clearly specified. However, the economy of manufacture and the increase in the overall accuracy of many products can be greatly improved by the use of specified manufacturing daturns for positioning purpose [l]. Usually more than one selection of datums to locate a part exists, but one of them is better than the others. If manufacturing datums are selected properly, manufacture of parts within design specifications can be easier, require less accurate machine tools, and reduce cost. Or, with the same machine accuracy, parts can be made to closer tolerances, which will improve the quality and consequently the performance of the products. How to generate feasible and economical

0166-3615/95/$09.50 0 1995 Elsevier Science B.V. All rights reserved SSDI 0166-3615(95)00006-2

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process plans with specified manufacturing datums according to designed dimensional and geometric tolerances by means of computers automatically has not been well addressed. We present an experimental approach of automated manufacturing datum selection for CAPP systems. In this paper, the neural network approach is used. First, based on our knowledge and previous research [2,3], the manufacturing datums are selected in such a way that the operation tolerances can be maximized and the design tolerance specifications are met. Because the manufacturing error sources have been considered and the manufacturing errors are minimized through proper manufacturing datum and setup selection, the selections will be feasible and economical. Then the network is trained by the examples so provided to select datums for manufacturing according to the basic shape and tolerance specifications of the parts. The result can be used in the manufacturing industry which will be more competitive and can produce better quality products at lower costs. The work presented here is limited to rotational parts.

2. The selection of manufacturing cess planning

datum in pro-

Usually all features, for a rotational part, can be machined in two setups (left or right). A surface can be machined either from only one direction (in left or right setup only) or from either direction (in either left or right setup), depending on the relationship of the surface with others. We define the term orientation as the direction from which a surface can be machined. Tolerance specifications (dimensional and geometric) can be categorized into local tolerances (for example the diametric, flatness, straightness) and global tolerances (for example the distance between parallel surfaces, angularity, parallelism), many of which have specified datum features. A tolerance can be associated with a single feature or multiple features. When a tolerance is associated with a feature, such as the tolerance for the diameter of a cylindrical feature, it can be defined as a local tolerance which is usually taken care of by the process selection. The selection of manufacturing datum and setup does not influence the local tolerances. When a tolerance is

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associated with two or more features, such as the tolerance for parallelism, perpendicularity, or runout, it can be defined as a global tolerance relationship. The variations of the global tolerance relationships that can be obtained are influenced by the selection of manufacturing datum and setup. In manufacturing processes, there are three ways to obtain the specified global relationship between the features: (1) arrange them in the same setup, (2) use some of them as locating datum(s) and machine the other(s), and (3) use other feature(s) as locating datum(s) to machine them in different setups. In the first case, the setup errors are not included in the relationship. The geometric relationship of the features machined in the same setup will mainly depend on the geometry built into the machine tool. When CNC machine tools are used, the tool movements are controlled by the control unit through the coordinate measuring system on the machine tool. The dimensional relationship, such as the distance between two parallel features machined in the same setup, will be determined mainly by the accuracy of the control unit, which becomes a built-in capability of the machine tool and the tolerances of the features obtained in the same setup will not stack up. Therefore, in this case, the relationships (both dimensional and geometric) between the features, as the local tolerance specifications mentioned above, are mainly determined by the built-in machine/process capabilities. The relationship is the easiest to obtain and, therefore, the most economical. The second case is recommended by many books [1,4,5]. However, the setup errors will be included in the relationship. To control the tolerance of the relationship, the accuracy of locating the part has to be considered, which becomes a major part of the variation of the relationship. The clamping force may increase the setup errors, depending on the clamping mechanism of the fixtures. In the third case, the specified relationship is obtained indirectly through some other features. The variation of these features will be introduced to the relationship. Because the manufacturing datums are changed in different setups, in this case, and only in this case, a tolerance chain is formed. The tolerances will stack up in the specified relationship. It is not desirable when the tolerance for the required relationship is tight. If the manufacturing datums and

J. Mei et al. /Computers in Inahhy 27 (1995) 53-64

setups are not sele.cted according to the tolerance specifications from design, although the widely used tolerance chain me.thod can check the stackup of dimensional tolerances and adjust the allocation of the tolerances (not re-select the manufacturing daturns), the arbitrary selection of manufacturing daturns and setups may result in undesired stackup of tolerances in critica. relationships which may make the manufacturing impossible even with the best machine tools. To summarize, the following statements can be made. The relatiorrship between features on a part can be obtained (1) synchronously, (2) asynchronously, or (3) through a chain. The accuracy and economy of manufacturing a part will decrease in that order. No tolerance chain is formed for the relationship obtained in the same setup because the features obtained in the same setup are mutually datumed. Manufacturing process planning has been using rule-based expert systems for a long time. Human process planners use the knowledge from their experience to make the process plans. The knowledge is extracted by the knowledge engineers and implemented into computer-aided process planning (CAPP) systems. CAPP has yielded good results. A systematic tolerance analysis approach for automated manufacturing datum and setup selection in CAPP using rules is reported [2,3]. First, both dimensional and geometric tolerance specifications from design are analyzed and ranked according to manufacturing data which contain information about machine tools and locating and clamping mechanism of the fixtures. Since all features of a rotational part can be machined in two setups, the features to be machined are divided in three groups: those that can only be machined in one of the setups, those that can only be machined in the other setup, and those that can be machined in either setup (e.g., a through hole). Then an algorithm is used to check, from the most critical one, that if the features with specified relationship(s) can be machined in the same setup. Those features which can be physically machined in either setup may be assigned to a specific setup when necessary. If it is not possible to machine them in the same setup, some of the feature(s) will be used as manufacturing datums to locate the part for machining of the other(s). The manufacturing datums and setups

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can be so selected automatically to make the manufacturing more accurate and economical. The purpose is to exclude as many manufacturing error sources from the critical relationships as possible and leave the inevitable manufacturing errors (chain errors) to those unimportant relationships with larger tolerances. The approach can be implemented by means of rule-based expert systems as most other CAPP systems are. To cover the whole rotational part family, there will be a lot of rules. To deal with multiplevariable problems such as process planning, it will be very difficult, although possible, to develop rulebased systems if the variables are closely interrelated. Good human process planners can use their knowledge gained from experience to make the process plans better than present CAPP systems, although in many cases they cannot tell what rules they use, which we wish to implement to CAPP systems. Humans also use many heuristics to make decisions. Quite often, the heuristics cannot be expressed explicitly as rules. Even if the heuristics can be expressed as rules, the lack of understanding of the manufacturing process prevents the knowledge engineers from extracting the real knowledge and implementing it in CAPP. When a new process or technology is introduced into the manufacturing environment, CAPP cannot learn and has to be modified by the knowledge engineers. Neural networks are an emerging technique in artificial intelligence. They have the ability to learn [6,7] and, therefore, have great potential for application in CAPP. Neural networks are advantageous when it is difficult to extract the knowledge as rules. Since neural networks have the ability to learn, they can be applied in any specific manufacturing environment. A given network can adapt to the variation of the environment and adapt to new technology more easily than the knowledge-based expert systems. For example, if the hardness or surface finish requirement of a part is high, a grinding process is needed. Usually some simple rules are used to select the process, such as “if the hardness is more than A or the surface finish requirement is more than B, then grinding is selected.” On the plane of hardness and surface finish, the shadowed area is for the grinding process (Fig. 1). But in the manufacturing

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6

Fig. 1. Simplified finish.

relationship

between

(Surface finish)

hardness

and surface

practice, we know that if the surface finish is lower than B, then the tumable hardness may be higher than A; or if the hardness is less than A, then the obtainable surface finish may be more than B. We may further notice that when the hardness is too low, high surface finish may be difficult to obtain without grinding process. More rules have to be added to cover a curved area (Fig. 2). If cutting conditions, material properties, and other parameters are considered, deriving of the rules seems impossible, not to mention the changing manufacturing environment because of the introduction of new technologies. Neural networks may be more advantageous for this kind of problem. What knowledge engineers do is just to help decide and code proper input and establish the structure of the network. Process plan-

IF or or or or

(H>Al (H>A2 &S>Bl) (H>A3 (LS>BZ) S>B3 (Hb2)

of

iHBlj

THEN

(GRINDING)

A4

1

I 61

Fig. 2. The approximation and surface finish.

,S 8283

of the relationship

(Surface finish)

between

hardness

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ners train the network by providing the input and desired output. The network can learn the complicated relationship or mapping and give correct output in similar situations. If some changes occur because of new technologies, the network can be retrained, and retraining the networks is easier than modifying the rules of an expert system. It has been also noted from the above tolerance analysis that all the known or derivable conditions, such as shape of feature (vertical, cylindrical, or conical), orientations, specified relationships can be expressed as numbers. The setup arrangement can also be expressed as numbers, such as ‘1’ means a feature is a manufacturing datum for a setup and ‘0’ means it is not. These numbers are suitable for inputs and outputs for neural networks.

3. Application of neural networks in process plan-

ning Process planning is knowledge intensive by the nature of the problem. A productive CAPP system must contain a tremendous amount of knowledge, i.e., rules about arranging machine operations and facts about the machine shop. Furthermore, the system should have flexibility because rules and facts in the database require constant updating. This is especially true in today’s manufacturing environment. The expert system approach has been used to build such CAPP systems since the 1980s. However, the results have not been promising due to the knowledge acquisition bottleneck. Chang [8] hence suggested that machine learning techniques be used to automate knowledge acquisition. Knapp and Wang [9] applied machine learning techniques to derive if then else rules for process planning. While the results were promising, the success of the approach was limited by a number of factors [lo]: 1. The learning system suffered from large computational requirements which grew rapidly with prob lem size. 2. The system required specification of a large number of heuristic generalization rules. However, the set of rules did not produce all the necessary generalizations needed for planning of complex parts. The inadequacy of the explicit rule ap-

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preach became particularly evident with increasing complexity of the problem domain. 3. The learning system generated non useful inferences in addition to useful ones, because the learning process was not problem directed. The non useful inferences must then be stored, or sorted out and discarded manually. Neural network techniques can be used to overcome these limitations. The advantages of neural networks are [6]: - high processing speed through massive parallelism, - learning and adapting ability by means of efficient knowledge acquisition and embedding, - robustness with :respect to fabrication defects and different failures, and - compact processors for space and power constrained applications. The first attempt to use neural network techniques in process planing might be that of Osakada et al. [ 11-131. The authors applied neural network techniques to an expert system for the process planning of cold forging in order to increase the consultation speed and to provide more reliable results. A threelayer neural network is constructed to relate the shapes of rotationally symmetric products to their forming methods. The shapes of the products are transformed into 16 X 16 black and white pixels and are given to the input layer of the neural network. The back propagation algorithm is employed. After training, the network is able to determine the forming methods for the products that are exactly the same or slightly different from those used as training examples. To exploit the self learning ability, the authors further applied the neural network techniques to the prediction of the most probable number of forming steps by considering the shape complexity and material property, the prediction of the die fracture and surface defect in the formed product, and the generation of rules from the knowledge acquired from an FEM simulation. It is found that the prediction of the most probable number of forming steps can be made successfully and the FEM results are represented better by the neural network than by statistical methods. Knapp and Wan,g [10,14] applied neural network techniques for the automatic acquisition of process planning knowledge. In their approach, two cooper-

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ating neural networks are utilized. The primary network is a three layer back propagation network. The second fixed weight network utilizes the MAXNET architecture. Parts to be planned are decomposed into machining features such as slots, holes and planes. Each feature type is associated with a set of characterizing attributes such as dimensions and tolerances. Every feature is represented by a vector whose elements identify the feature type and its attribute values. This vector forms the input pattern to the primary network. The network responds to the presentation of a feature vector by activating certain output nodes, corresponding to the proposal of particular machining operations. The response of the network is trained using example process plans and a back propagation learning algorithm. The second network is used to force a decision between competing operation alternatives. Its output is then fed back to the input layer of the primary network to provide a context for deciding the next operation in the machining sequence. The part is presented to the neural network feature by feature. Then the network generates a sequence of operations for machining each feature of the part independently (global sequencing of operations across all features is not considered). Hwang and Henderson [151 applied a perceptron network in feature recognition, which is the first step in automated process planning (i.e., to interpret the design data from a CAD model). The goal of feature recognition is to convert a low level representation such as face, edge, vertex to a semantically higher feature based model. The network training is accomplished by manually presenting exemplars of features the user considers important in an engineering analysis (for example, manufacturing related features for process planning). Their result showed that neural network approach took less time in feature recognition than other traditional approaches. Chen [16] proposed an unsupervised learning algorithm for setup generation and feature sequencing. In general, setups are generated based on tool approach direction of the features and precedence relationship among the features. It is possible for a feature to be assigned to more than one setup. Therefore, the final setups are determined by removing redundant features from different setups (readjustment). Chen’s approach eliminates the readjustment. In his approach, each feature has two attributes, a

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tool set (the set of tools needed) and a tool approach direction vector. An unsupervised learning network is then used to cluster features into setups based on their similarity. The network is divided into two sub networks, i.e., tool sub network and approach direction sub network. Each network has its own weight updating rule. In the tool sub network, the OR operation is used. While in the approach direction sub network, the AND operation is used to eliminate the ambiguity of assigning one feature into different setups. After setups have been determined, an Episoda1 Associative Memory (EAM) approach is used for feature sequencing. The author’s approach generates setups only based on tools needed and tool approach direction. He indicated that tolerance, material, and machining operation information should be considered in further research.

in Industry 27 (1995) 53-64 I -3, -2, -1. 0, 1, 2. 3,

left others Ien veltical Ieli cylindrical both (cylindrical) right cylindrical right verliil right others

1,0

isadatum

oj = 0. othemise

i

= 1. 2, . .. . 11

n

n

12

13

4. Neural network-Back-propagation Neural networks have been inspired both by biological nervous systems and mathematical theories of learning. Neural networks are defined by Kohonen networks [171 as “ massively parallel interconnected of simple (usually adaptive) elements and their hierarchical organizations that are intended to interact with objects of the real world in the same way as biological nervous systems do”. Neural networks attempt to achieve good performance via dense mesh of computing nodes and connections. The basic processing elements of the neural networks are called artificial neurons, or simply neurons. Often we simply call them nodes. Neurons perform as summing and nonlinear mapping functions. In some cases they can be considered as threshold units that fire when their total input exceeds certain bias values. They are often organized in layers. Each connection strength is expressed by a numerical value called a weight, which can be modified automatically during learning stage through certain learning rules. After learning stage, the weights are stored in the networks as if the networks memorized the inputs. When an input (even with noise) is fed to the networks, the networks will reproduce the corresponding output. The input and output of neural networks are often called patterns or vectors. Each element of the input pattern or vector corresponds to an input node and

’ 11 INPUT LAYER

Fig. 3. Multi-layer

HIDDEN LAYER(S)

feedforward

OUTPUT LAYER

neural network.

each element of the output pattern or vector corresponds to an output node in a pre-determined manner. Fig. 3 shows a neural network architecture. Neural networks are characterized by their leaming method. A supervised learning network adjusts its weights on the basis of the difference between the values of output units and the desired values given by the trainer, given an input pattern. The learning mechanism of a supervised neural network is as the following: 1. Start -the cycle by exposing the network to a certain input pattern; 2. Specify the desired output for this particular input (external information); 3. Collect the network’s output and compare it with the desired output; 4. Adjust the relevant weights so that a certain amount of the detected error is removed; 5. Go back to Step 1, until a satisfactory set of weights are found. Algorithm details can be found in [18-201.

J. Mei et al. /Computers

5. Experiment A critical first step is to convert design data into a proper format for input into the network. For a rotational part, as discussed above, the orientation of a surface can be right, left, or both. We can use three values (such as -‘l, 1, and 0) to represent the orientations. A surface can be external cylindrical, internal cylindrical, vertical, or others. The first three are suitable for being selected as manufacturing daturns. We can assrgn a number to each type of surface. There will ;betwo setups for each rotational part. For each setup, a cylindrical and a vertical surface are needed a.s datums to locate and clamp the part. Different output values for each surface can represent the manufacturing datum selection. The output of the network will show which surfaces should be used as manufacturing datums. A back-propagation network with one hidden layer is used in this studly(see Fig. 3). If a part has n features, there will be n (when no tolerance constraint is applied) or 2n (with tolerance constraints) or even more input nodes and n output nodes (if m alternatives are possible, there will be m X n output nodes). The number of neurons in the hidden layer will be given different values to find the best one. First, a number will be assigned to each kind of surface as input. We assign ‘0’ to the cylindrical surface with both orientations, ‘1’ to other cylindrical surfaces with a isingle orientation, ‘2’ to vertical surfaces, and ‘3’ for other kinds of surfaces, such as conical, curved, or small cylindrical features that cannot be used as clamping surfaces. Those with the left orientation will have a ‘ - ’ sign and those with the right orientation will have a ‘ +’ sign. The arrangement is shown as follows: Left others Left vertical Left cylindrical Both (cylindrical) Right cylindrical Right vertical Right others

-3 -2 -1 0 1 2 3

If the number of surfaces of a part is less than n, the rest of input will be zero, The basic idea of the arrangement is that the cylindrical surfaces with both orientations and the extra input to the net should

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have no influence on the selection of clamping surfaces, and therefore, they have zero values. Because the ‘Both’ is also cylindrical, three kinds of cylindrical surfaces are arranged close to each other so that the classification may be easier for the network. For each rotational part, a cylindrical and a vertical surface should be used to locate and clamp the part in each setup. Assume all the surfaces should be machined. Since the surfaces for location and clamp cannot be machined in the setup, no surface can be used as a datum for both setups. Therefore, there will be four surfaces, two cylindrical and two vertical, used for the two setups of each part. For the four surfaces, the two on the right will be used to machine the surfaces on the left and the two on the left will be used to machine the surfaces on the right. On the basis of the analysis, the output ‘1’ means that the surface will be used for setup of the part and ‘0’ means that it has nothing to do with the setups of the part. First, the tolerance constraints are not used for manufacturing datum selection. Therefore, the number of input neurons I is the same as that of the output neurons K, which is equal to the maximum number of the surfaces a part may have. In the experiment, the maximum number of surfaces of a part is 11. The number of neurons J in the hidden layer will be selected from 5-15 according to the performance of the network. Nineteen (19) parts with 10 or fewer surfaces are selected (Fig. 4) and the surfaces are coded according to the rules above. A conical surface on a drawing means a surface that cannot be used to locate and clamp the part. It can be a cone, a torus, a cylindrical surface that is too small to clamp the part, a spline, a thread, or other complicated surfaces. For each part, the coding can be from either end. Therefore, there are two codes for each part. Then the desired outputs are generated according to some rules and our knowledge. In the experiment, dimensions of the parts are ignored and only the shapes are considered. An 11-digit number can represent the shape of a part or the datum selection for it in this group. For part #3, the shape will be coded as -2

0 212120

2

1

2

2

1 -2

0 0

0 0

0 0

0 0

J. Mei et al. /Computers in Industry 27 (1995) 53-64

60

D 3

DD D

n

0 17

The output for datum selection will be 1 1 0001111

1

0

0

0

0 0

0 0

0 0

0 0

This means that the first two features from the left will be used to locate and clamp the part in one setup and the third and fourth features will be used for the other setup (Fig. 5). First, a set of ten parts (#l-#lOI are selected as the training set. When the number of neurons J in the hidden layer is equal to 8, the output does not converge. When J is equal to 10, even a small momentum term will prevent the network from converging. When J is equal to 12, and without the momentum terms, it takes 3241 epochs to make the error E < 0.01. When J is equal to 14, it only takes 2472 epochs with same accuracy. Next, nine other parts (#ll-#19) are tested with the network. Less than 50% of the outputs are correct outputs. Most of the others are not feasible because they use some surfaces that are neither cylindrical nor vertical surfaces to locate and clamp the parts. The network can give correct output for the training set but cannot get the real knowledge to deal with larger number of parts. The question is whether the training set and coded input can be properly selected to cover the larger number of parts. Now two parts are added to the training set. When J is equal to 14, 16, 18, and 20, the training epochs are 2390, 2431, 1907, and > 20000 respectively. It is noticed that as long as a cylindrical surface and a vertical surface are selected to locate and clamp the part in each setup, it may be feasible, although it might not be optimal. Among the unmatched output, those using neither vertical nor cylindrical surfaces are not feasible. The reason why such surfaces are selected may be that they should not be given a number that is different from the extra input that will never be selected. Therefore, the code system is changed so that ‘0’ is given for the unfeasible surfaces, ‘1’ for vertical, ‘2’ for cylindrical, and ‘3’ for both. For example, the input code for part #3 will be: 1

Fig. 4. Selected parts.

1

3

1

2

1

2

1

0

0

0

0

Using the same ten-parts training set and the same nine testing parts, the network gives 50% correct output, 25% feasible, and 25% unfeasible, compared

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selected for code ‘3’. For the two parts mentioned above, the inputs and outputs are: A: B:

input 12121312121 00121312100

output A00011011000 B: 00011011000 Fig. 5. An example of datum selection.

with previous 37.5%, 12.5%, and 50%. When part #lO in the training set is replaced by part #ll and when J > 8, 73.33% of the outputs are correct. It seems that which p:arts are put into the training set is important. The coding of lthe parts is from both ends and input from Z[l]. This is to say that each part has two codes. Therefore, i.t does not matter which end is used to code the features for the system to process. It is noticed that although some parts are different, they are similar in a couple of adjacent features. The similarity may result in a similar setup. But because the features are coded and input from the end, the similarity cannot be shown in the input patterns. For example, part A has two more features at each end than part B (see bfelow). If there are no tolerance restrictions, the manufacturing datums for setup will be the same. But th[e input patterns seem completely different. Therefore, the system has difficulty remembering and recognizing similar ones. A: B:

input 1 2 1 2.1 12131210000

A: B:

output 0 0 0 1 01101100000

When the number of inputs in the training set is 25 and that in the test set is 10, all the outputs are correct. The performance of the system is improved. Next, eleven more input nodes are added to add the tolerance restrictions to the datum selection. To simplify the problem and test the system, runout tolerances are specified to some or all the cylindrical features. If the cylindrical features with the specified runout tolerance have the same orientation, the part can still be chucked to machine the features in the same setup. If the cylindrical features with specified runout tolerances have different orientations, in manufacturing practice, center support is the best and common holding method to meet this kind of tolerance (see Fig. 6). If a feature has a runout tolerance, the corresponding additional input will be ‘l’, otherwise the additional input will be ‘0’. Given the runout tolerance(s), the output would be ‘1’ at both ends and ‘0’ for all other outputs. For example, the input for part #4 in the selected parts will be: 00 00 00

1

3

1

2

1

2

1

0

1

1

0

0

0

On the basis of the above observation, all inputs are put in such that the position of the largest diameter (coded ‘3’) is fixed in the input. In these experiments, there .are twelve input nodes. The first input ZIO] is ‘ - 1’ and the other eleven are ready for input of part features. Z[6] is the center one and is

121312121 (needs center support) 121312121 (does not need center 121312121 (does not need center

00010101010 00010100000 support) 00000101010 support)

Fig. 6. A typical part with tolerances that needs center support.

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The desired output will be 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 1 0 1 1 0 0 0 0 0 0 1 1 0 1 1 0 0 0

(center supported) (chucked) (chucked)

A part can have several different ways to specify the tolerances according to the functional requirements. Therefore, for the basic parts on Fig. 4, we have 77 inputs. When we put the first 40-5.5 inputs into the training set, it took more than 5000 epochs to converge and only about SO-60% of the output are correct. We found that there would be many ways to mean the center support method. To mean the center support, it is not necessary to use ‘1’ for the two ends which makes the output too complicated for the system to learn. As long as the output for the center support method is different from that for chucking, it would be easy for the system to learn and it would not be difficult to identify the end surfaces where the center holes need to be drilled. Therefore, we define ‘1’ for the first digit and the last digit in the output to mean that the center support method is needed for that part. The output for all parts that need center support will be the same. For the same part, the input will be: 00121312121

00010101010

The desired output will be 10000000001 When we put the first 45 to 55 of the 77 inputs in the training set, the training processes take less than 500 epochs to converge and 80% to 88.9% of the outputs are correct. When the random seed is changed, the correct percentage changes. Even if the percentage remains the same, parts that are correctly processed may be different. When the parts in the training set are changed, the percentage of correct outputs changes significantly. The problem may come from two factors. One is the coding. For similar inputs, the results may not be similar. Or, the results are similar, but the inputs are not. If the similarity can be found through proper coding of the data, the performance of the system could be improved. The second factor is the training set. We know that for a given part population, which parts are in the training set influences the perfor-

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mance of the system. There might be an optimal training set that has the least number of parts in it and can train the network to deal with the whole population correctly. It is not desirable nor efficient for knowledge engineers to find such an optimal set. For our research problem (manufacturing datum selection), the number of possible inputs may be quite large. It seems that it is difficult to predetermine a training set to train the network so that it can deal with the whole family of parts.

6. Future development From the experiment above, it can be seen that the neural network has the potential to be used in computer-aided process planning (CAPP). It is not practical to put every possible input into the training set at one time. The learning process of the network should not be limited to training before the system is put into the real manufacturing environment. Tsatsoulis and Kashyap [21] pointed out that any serious artificial intelligent program should not be a static, unchanging problem solver, but that it should also be able to adapt its knowledge to new expertise. They proposed a case-based process planning system. Underlying cased-based planning is the idea of planning as remembering [22]. A typical case-based process planner must be capable of [23]: * retrieving past experiences from the plan memorY, * modifying the old solution fragments for the new part, and * abstracting and storing the newly generated plan in the plan memory. Although the case-based system developed by Tsatsoulis and Kashyap 1211 is a knowledge-based system, the concept of keeping learning is very important and can be used in neural network approach in CAPP. The past experience of knowledge is retrieved by putting a new pattern into the neural system as an input. Rules can be used to identify the correctness of the output. If the system cannot give the correct output, the output will be modified by the human process planner and the input and desired output will be put into the training set to retrain the network. The new knowledge will be abstracted and stored in the system through the retraining. Gradu-

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ally, the system can learn the real knowledge with fewer numbers of patterns in the training set to cover the largest area. From the experiment with a neural network, we can see that the neural network cannot get the explicit knowledge. When a new pattern is input to a neural network system, no feasible output is guaranteed. It is one of the limitations of neural networks. Therefore, some rules may still be needed to help guarantee that correct outputs are given. It is possible that a combination of neural networks and expert systems-a hybrid system-can be used to solve the problem. First, a neural network is structured. A number of coded inputs and desired outputs should be put into a database to train the system. Human supervision and training are still needed when the system is in use. When a coded input is given to the system, the outpue is compared with the desired output. If they are not identical, the input and output are put into the database and the system is trained again with all data in the database. If conflicts occur so that the system cannot converge, the conflicting data will be retrieved and one of them will be deleted. When the ‘conflict is caused by the obsolescence of the old knowledge, the previous will be deleted. This may happen when the manufacturing environment or strategy changes. Many functions such as checking the feasibility of output, retrieving of conflicting data etc. can be realized by an expert system but desired output can only be provided by a human expert. Therefore, the hybrid system will be, at least at the beginning, a computer-aided manufacturing function. After a considerable usage by a human expert, the system may become more and more close to a human expert and finally perform the job just as a human expert.

7. Conclusion Automatic manufacturing datum selection is one of the critical probl.ems for CAPP systems to generate feasible and economical process plans. An experimental approach using neural networks for automated manufacturing datum selection for CAPP is presented. The shape information of rotational parts is coded as basic input to the network and the tolerance specifications are also used in input as

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constraints. We still can see the experimental approach has the potential to automatically select manufacturing datum based on the tolerance specifications. Future research will include proper coding of the part shape and tolerance specification, hybrid model of expert-neural approach, and case-based approach.

References t11 E. Buckingham,

Dimensions and Tolerances for Mass Production, The Industrial Press, New York, 1954. PI J. Mei and H.C. Zhang, “Tolerance analysis and automated setup selection for CAPP”, ASME WAM (1992) 211-220. 131 H.C. Zhang and J. Mei, “Tolerance analysis for automated setup selection in CAPP”, J. Adv. Manuf Technol., to appear. [41 L.E. Doyle, Metal Machining, Prentice-Hall, New York, 1953. t51 F.W. Wilson and P.D. Harvey, Manufacturing Planning and Estimation Handbook, McGraw-Hill, New York, 1963. [d D. Barschdorff and L. Monostori, “Neural networks-Their applications and perspectives in intelligent machining”, Computers in Industry 17 (1991) 101-119. expert sys[71 A. Ben-David and Y.-H. Pao, “Self-improving tems: An architecture and implementation”, In: Manage. 22 (1992) 323-331. [81 T.C. Chang, Expert Process PIarming for Manufacturing, Addison-Wesley, Reading, MA, 1990. [91 G.M. Knapp and H.P. Wang, “Inductive learning in computer-aided process planning”, Proc. 1989 HE Integrated Systems Conf and Society for Integrated Manufacturing Conf, Atlanta, GA, 1989, pp. 398-403. [lOI G.M. Knapp and H.P. Wang, “Acquiring, storing, and utilizing process planning knowledge using neural networks”, I. Intell. Manuf 3(5) (1992) 333-344. Dll K. Osakada, G.B. Yang, T. Nakamura and K. Mori, “Expert system for cold forging process based on FEM simulation”, Ann. CIRP 39(l) (19901249-252. WI K. Osakada and G.B. Yang, “Neural networks for process planning of cold forging”, Ann. CIRP 40(l) (1991) 243-246. [I31 K. Osakada and G.B. Yang, “Application of neural networks to an expert system for cold forging”, Inr. .I. Mach. Tools Manuf 31(41 (19911 577-587. [I41 G.M. Knapp and H.P. Wang, “Neural networks in acquisition of manufacturing”, in: A. Kusiak (ed.), Intelligent Design and Manufacturing, Wiley, New York, 1992. [15] J.-L. Hwang and M.R. Henderson, “Applying the perceptron to three-dimensional feature recognition”, .I. Des. Manuf 2(4) (1992) 187-198. [16] C.L.P. Chen, “Setup generation and feature sequencing using unsupervised learning algorithm”, 1993 NSF Design and Manufacturing Systems, 1993, pp. 981-986.

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.I. Mei et al. / Computers in Industry 27 (1995) 53-64

[17] T. Kohonen, “An introduction to neural computing”, Neural Nefw0rk.s 1 (1988) 3-16. [18] D.E. Rumelhart, G.E. Hilton and R.J. Williams, “Learning internal representations by error propagation”, in: D.E. Rumelhart and J.L. McClelland (eds.), Parallel Distributed Processing: Explorations in the Microslructure of Cognition, Vol. I: Founaiztions, MIT Express, Cambridge, MA, 1986. [19] B. Wu, “An introduction to neural networks and their applications in manufacturing”, J. Intell. Manuf 3 (1992) 391403. [20] J.M. Zurada, Introduction to Artificial Neural System, West Publishing Company, 1992. [21] C. Tsatsoulis and R.L. Kashyap, “A case-based system for process planning”, Robot. Comput.-Integr. Manuj 4(3/4) (1988) 557-570. [22] C.K. Riesbeck and R.C. Schank, Inside Case-based Reasoning, Lawrence Erlbaum, Hillsdale, NJ, 1989. [23] M. Marefat and R.L. Kashyap, “Automatic construction of process plans from solid models”, IEEE Trans. Syst. Mand Cybern. 226) (September 1992) 1097-1115.

Hong-Chao Zhang is an associate professor in the Department of Industrial Engineering at Texas Tech University. Dr. Zhang received his Ph.D. in Manufacturing Engineering from the Technical University of Denmark in 1989, and his MS in Mechanical Engineering from the University of Aalborg, Denmark in 1986. His research and teaching interests are in the areas of concurrent engineering (CE) and computer integrated manufacturing (CIM) including computer-aided process planning (CAPP), CAD/CAM, application of artificial intelligence (AI) in manufacturing, automated tolerance analysis, as well as manufacturing processes and systems. Dr. Zhang has published more than 40 technical articles in a variety of journals and conferences. One of his recent books, titled Computerized Manufacturing Process Planning Systems, was published by Chapman and Hall in 1993.

Jiannan Mei received his B.S. and MS. degrees in Mechanical/Manufacturing Engineering and Control Engineering from Tsinghua University, Beijing, China in 1983 and 1986, respectively. Currently he is a Ph.D. candidate in the Department of Industrial Engineering, Texas Tech University. He has been working on tolerance analysis for computer-aided process planning (CAPP) and has published several papers on tolerance analysis using neural network and graphical methods. His industrial experience includes design, process planning, manufacturing, and assembly of machine tools and control systems. His research interests are tolerance analysis for precision maching and assembly, process improvements, and process planning to improve quality and productivity.

William J.B. Oldham received a BSEE in 1956, a MS in 1958 and the Ph.D. degree in 1965 in Physics, all from Texas A & M University. He is a Professor of Computer Science and Graduate Advisor in Computer Science at Texas Tech University, Lubbock, TX. From 1961 to 1964, he was at the AFCRL Hanscom Field, Bedford, MA. He then spent ten years at the Johnson Space Center, NASA, Houston, TX. He later spent eleven years at E-Systems, Greenville Division, where he was Director of Research and Development. He joined Texas Tech in 1987 and current research interests include neural networks, distributed systems, non-linear systems, and system optimization.