Download artificial intelligence methods in early manufacturing time estimation

Artificial intelligence methods in early manufacturing time estimation
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ARTIFICIAL INTELLIGENCE METHODS IN EARLY
MANUFACTURING TIME ESTIMATION
B. Mikó
PhD, Z-Form Tool Manufacturing and Application Ltd
H-1082. Budapest, Asztalos S. u 4 . Tel: (1) 477 1016, e-mail: [email protected]
M. Szántai
Ph.D. student, Department of Manufacturing Engineering, Budapest University of Technology and Economist
H-1111. Budapest, Muegyetem rkp. 5. Tel: (1) 463 2513, e-mail: [email protected]
Summary
In the early phase of the manufacturing process planning the estimation of manufacturing time and cost is often
needed for the preliminary planning of the manufacturing. The manufacturability analysis and some tasks of
production planning may require this estimated data.
The aim of the article is to present the basic concepts of the computer supported estimation of the manufacturing
time and cost data in this phase of process planning. The name of this project is ECoTEst.
Keywords: manufacturing time estimation, feature based part representation, expert system, case-based
reasoning, artificial neural network
1. INTRODUCTION
The aim of the manufacturing process planning is to generate all the required documents for
manufacturing. During the planning process the engineer defines the blank state and all steps which is suitable
for the production of the designed shape.
The first step of the manufacturing process planning is the preliminary process planning. The further
phases can be divided for the autonomous field: planning process of blank manufacturing, part manufacturing
and assembly. The part manufacturing process planning can be divided for further four levels: planning the
sequence of operations, operations planning, operation elements planning and post-processing ([1]).
The manufacturing time and cost estimation is the part of preliminary process planning ([2]), but these
data have important role in several tasks during production process. These data are indispensable for quotation,
for economical and manufacturability analysis during the design process ([3]), and the capacity analysis of
production planning and scheduling ([4]).
The aim of the article is to present the basic concepts of the computer supported estimation of the
manufacturing time and cost data. The name of this project is ECoTEst (Early manufacturing COst and Time
ESTimation).
2. CONCEPT OF ECOTEST
The aim of the project is to study the methods of artificial intelligence (AI) on the viewpoint of
manufacturing time and cost estimation. The selected AI methods are rule-based reasoning, case-based reasoning
and artificial neural networks. On the base of the results of examination we established the limits of these
methods (Table 1) and prepared the supported system layouts.
Strength
Weakness
Rule-based system
Small data base
Settled knowledge base
Complicate algorithms
Hard to supplement
Table 1 Limits of selected AI methods
Case-based system
Neural network
Robust work
Hidden connection
Simple algorithm
recognition
Simple algorithm
Great data base
Learning patterns needed
The core of the estimation software is a feature based part modeler module (ECoTEst-PM) which is
suitable to describe a non-axial symmetric part by limited set of geometrical objects (features). The designed
three estimation systems are built on the output of this module. The first system is basically a rule-based
estimation system (ECoTEst-FE). This system is able to generate the possible process plan on the base of
Dr Mikó Balázs – Szántai Mihály
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geometric feature list by a rule-base, and than heuristic functions estimates manufacturing time and cost data
([5], [6]). The FE module is suitable to generate the initial case-base and the learning pattern to the case-based
estimation system (ECoTEst-CE) and the neural network based system (ECoTEst-NN). In order to faster
developing a system was created for automatic generation of example parts consider geometric constraints.
Figure 1 ECoTEst systems
Figure 2 The user interface of the ECoTEst-PM
3. FEATURE-BASED PART REPRESENTATION
The first problem of the development was the description of the workpieces. There are several
possibilities to solve the problem of part representation, like standard CAD models, GT code, mathematic
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description of surfaces etc, but considering the aim of the system, the constraints of methods and the difficulties
of implementation the feature-based part representation was selected. A feature is a subset of geometry on an
engineering part, which has a special design or manufacturing characteristic. During the feature-based modelling
the workpiece is divided into simply individual stereotyped building blocks.
In the ECoTEst-PM module the part modelling is based on four types of geometric features, but the
number and position of the features are not limited. Next features can be applied: step, slot, hole and counter
bore (Fig. 3). Chamfers and rounds have not an effect on manufacturing time in this level, so these are no need to
define them. In spite of these constrain a wide range of workpieces can be defined as Figure 7 shows.
Step
Slot
Hole
Counter
Figure 3 Set of features
The part modelling process starts with the definition of the overall dimensions of the part in a rectangular
coordinate system. Then the features can be defined. In addition to geometric parameters of the feature the
surface roughness (Ra) and accuracy (IT) can be defined. These additional parameters have essential role in the
generation of the order of operation elements. Parallel with the definition the wireframe model of the part is
generated, which is rotateable, sizeable, colorable, so the right layout of the part can be checked in the monitor
(Fig. 2). In addition to visual supervision the text oriented date of features appear in the monitor too, which can
be saved, modified, and will be an input of the second module.
4. FEATURE-BASED APPROACH
The aim of the ECoTEst-FE module is to process the feature model of the part, and generate estimated
manufacturing time and cost data by analysis of possible manufacturing process plans. This system basically
rule-based but contains many heuristic steps so this is a hybrid solution of the problem.
The FE module (feature-based estimation) consists of the next steps: (1) opening and analyzing the
feature model, (2) generation of manufacturing features, (3) generation of precedence matrix, (4) generation of
potential solutions, (5) determine the number of set-ups, (6) elimination of number of solution (minimal number
of set-ups), (7) estimate the manufacturing time and data.
In the first step, geometric features, which disappear during the manufacturing, are deleted from the
model. For example if a step contains a slot, the slot must be deleted. Then each manufacturing steps of all
features are generated. The next step is the generation of precedence matrix, which is helped by a heuristic rule
base. The precedence matrix stores the interdependencies among manufacturing steps of each feature. Heuristic
rules can be classified three sets. The first set of rules prefers that feature which has larger cubage if two features
have intersection (Fig. 4.a). In that case if a feature holds another one, the 'mother' feature have to be
manufactured first (Fig. 4.b). Finally there are rules, which describe manufacturing practices (Fig. 4.c).
a,
b,
Figure 4 Demonstration of rules
c,
The order of operation elements is generated by the reduction of this matrix. This process is suitable for
detect all possible solution of the sequencing problem, but the user needs the cheapest solutions which contains
minimal number of set-ups. Accordingly the sequences, which contain minimal set-ups, are selected, and then
the manufacturing time and cost data are estimated by heuristic functions. This heuristic functions ([6]) estimate
the manufacturing time of generated operation elements on the bases of geometric data.
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5. CASE-BASED APPROACH
One of the important characteristics of engineering mentality, that the domain specific knowledge is
known as the solutions of particular problems. This behaviour of human thinking is modelled by the case-based
reasoning method, which solves a problem by retrieval and adaptation of solution of known problem [7].
The conditions of powerful application of case-based reasoning method are: fast retrieval of suitable
manufacturing process plan, estimate the similarity, possibilities for simple adaptation, continuous updating of
case-base. The process of case-based reasoning (Fig. 5.) is very simple. The case base consists of stored cases,
which are represented by solution of old problems. When we must solve a new problem, first of all the most
similar case is selected from the case base, then the solution is adapted and the new case is added to the case
base, which means the learning ability of the system.
The workpieces and their time and cost data mean the cases in the case-based manufacturing time
estimation system. They are real, known examples, which represents the profile of actual manufacturing system
and all time and cost data are known. That is mean, the powerful application is required an effectively large
example database and a large manual preliminary data processing work. During the research this data processing
work was eliminated by the feature based system, which is able to create the necessary examples.
The description of a “new problem” is done in the PM module. For the case-based system the output of
the PM module is transformed. On the base of geometric feature list the system generate a parameter list, which
contains only numeric information like maximum size of worpiece, number and volume of each feature type,
area and volume of blank and finished workpiece etc. This transformation process is the “indexing”. The
retrieval algorithm searches a similar workpiece. The similarity means the equal of numeric parameter lists; of
course some deviation is admissible. Because cases contain the feature list too, during the “choice” and
“adaptation” the user is able to compare the geometric models of workpieces.
New problem
Indexing
Input
Interconnection
weight
Retriving
Node or
Processing
element
Similar cases
Choice
Case-base
Output
Suggested solution
Adapting
Learning
Solution
Figure 5 The process of case-based reasoning
Figure 6 Artificial neural network
6. NEURAL NETWORK BASED APPROACH
Artificial neural networks are forms of fine-grained parallel processing designed to imitate the
interactions of brain neuron and their information processing capabilities ([8]). The artificial neural network is a
tool for computational tasks, which has biological analogy. An artificial neural network is defined by the
topology, the characteristic of nodes and the learning algorithm. An artificial neural network is a hierarchical net
of simple elements, which called nodes.
We applied a perceptron based neural net. In this case nodes summarize the weighted inputs and
transform by a non-linear function.(Fig. 6). If appropriate numbers of nodes are organized to three layers, where
the first contains the set of input data, the third contains the set of output data and they are connected by the
hidden layer, the network able to discover the hidden connection between the input and output set of data.
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The neural network based system uses the same numeric parameter list as input like the case-based
estimator, so the description process of the machined part is similar. This parameter list is transformed by neural
net to estimated time and cost data. In order to successful work the neural net must be learned to the above
mentioned hidden connection, which means adjustment of the interconnection weights by examples called
learning patterns. The role of the learning patterns is same like in the pervious case: represents the profile of the
manufacturing system.
7. CONCLUSIONS
The PM and FE modules have been implemented from the demonstrated system layouts, and the CE and
NN modules are under development. Several example workpieces were generated for testing. Although only four
types of feature can be used in the definition process, as Fig. 7 shows, wide range of workpieces can be
generated. In the close future the development of CE and NN module will be finished and the testing and
analysing phase of the ECoTEst project will start.
Figure 7 A set of test workpiece
8. ACKNOWLEDGEMENT
Authors acknowledge the support of the Research Found of the Hungarian Academy of Science (OTKA
T032732) and the indispensable work of MSc students: Krisztián Novák and Gábor Tóth.
REFERENCES
[1] Horváth M.: Planning of part manufacturing process; DSc Thesis, Budapest, 1984. (in Hungarian)
[2] J. Papstel, A. Saks: Time and cost estimation in the preplanning stage; Proc. of 8th International DAAAM
Symposium, Dubrovnik, 1997., pp. 253-254.
[3] S.K. Gupta, W.C. Regli, D. Das, D.S. Nau: Automated manufacturability analysis: a survey; Research in
Engineering, 1997/9. pp. 168-190.
[4] D. Ben-Arieh, J.P. Lavelle: Manufacturing cost estimation: application and methods; Journal of
Engineering Valuation and Cost Analysis, Vol.3 No.1 2000. pp. 43-55.
[5] B. Mikó, K. Novák, G. Tóth: Early manufacturing time and cost estimation – A feature based concept;
Proc. of microCAD 2001. Miskolc 2001 pp.119-124.
[6] B. Mikó: Early manufacturing time estimation by heuristic functions; Proc. of FMTÜ 2001, Kolozsvár,
2001. pp.35-38.
[7] J. Kolodner: Case-based reasoning, Morgan-Kaufmann, 1993.
[8] J.F. Shepanski: Artificial neural systems; in Encyclopedia of physical science and technology Vol.2.
Academic Press Inc. 1992. pp.65-77.
Dr Mikó Balázs – Szántai Mihály
GÉPÉSZET 2002