1 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: miko@manuf.bme.hu 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: szantai@manuf.bme.hu 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 Table 1 Limits of selected AI methods Rule-based system Case-based system Neural network Small data base Robust work Hidden connection Settled knowledge base Simple algorithm recognition Complicate algorithms Hard to supplement Great data base Simple algorithm 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
2 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 3. FEATURE-BASED PART REPRESENTATION Figure 2 The user interface of the ECoTEst-PM 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
3 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, c, Figure 4 Demonstration of rules 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.
4 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 Input Indexing Interconnection weight Case-base Retriving Similar cases Choice Suggested solution Output Node or Processing element 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.
5 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. 8. ACKNOWLEDGEMENT Figure 7 A set of test workpiece 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.