5th International DAAAM Baltic Conference “INDUSTRIAL ENGINEERING – ADDING INNOVATION CAPACITY OF LABOUR FORCE AND ENTREPRENEURS” 20–22 April 2006, Tallinn, Estonia CUSTOMIZATION OF CAD SYSTEM FOR DECISION SUPPORT OF

MOULD DESIGN PROCESS

Hermaste, A., Sutt, A., Kyttner, R.

Abstract: Today, there is a clear trend to use more plastics and composites in engineering industry. The time to market for plastic injection products is becoming shorter and more crucial, thus the lead time available to making the injection moulds is decreasing. Proper design of the injection moulding is crucial to producing functional and quality plastic component. This will need an integrated optimizing of mould, process and material properties. To achieve mould and process optimization, early decisions in design process must be supported. This paper introduces a decision analyze approach to examine the best praxis’s of companies and use the results of analyzes to improve the computer supported mould design process. The pattern recognition technique is used together with the questionnaires developed by the authors to evaluate technically and economically acceptable variations of different plastic product and injection mould parameters. To collect the information about best praxis’s from different Estonian companies, the developed questionnaires were used. Keywords: injection mould design, decision analysis and support system, questionnaires to evaluate the best engineering praxis’s, pattern recognition methodology, Knowledge Based Engineering 1. INTRODUCTION Nowadays, mould design faces increasing deadline pressure and the design itself is

predominantly based upon the experience of the mould designer. Mould designers are required to possess thorough and broad experience, because detailed decisions require knowledge of the interaction between different parameters. Today, many CAD systems provide the geometric modeling functions which facilitate the drafting operations of mould design, but do not provide to mould designers the inexpensive solution of necessary knowledge to develop good mould designs. Computer-aided engineering packages are usually good at data processing for information-intensive problems. However, in mould design a substantial practical knowledge is needed. Therefore the methods, how to describe the data of best practices and to put it into the CAD system to develop the good mould designs, must be taken under consideration. Previous works in applying computer techniques in the field of injection moulding covered many aspects of injection moulding and mould design activities. The knowledge based engineering systems [1,5] have demonstrated great potential to assist designers in interacting with a CAD system for conceptual design as well as for the final engineering design of a mould by using engineering rules of thumb with extensive analytical procedures. The major advantage of a knowledge based engineering system for mould design is the explicit representation and manipulation of knowledge, representing human judge-ment, such as rules of thumb and best practices of companies. Knowledge Based

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Engineering is fundamentally about design re-use [2]. It concerns being able to take advantage of any experience, expertise and other information relevant to each phase of the engineering life cycle of a mould design. These knowledge bases can exist in many forms, such as: – spreadsheets – engineering formulas – handbooks – proprietary software In this paper an approach to develop a supportive technique for CAD-based mould design is described. The Unigraphics NX MOLDWIZARD has been chosen as base. 2. APPROACH AND METHODOLOGY Engineering Decision Analysis is a process that allows examining decisions in a structured and efficient manner. Decision Analysis uses techniques such as Pattern Recognition [8], Artificial Intelligence [7], Machine Learning [7] and others in order to discover decision patterns and relationships in data, to integrate existing analytical design methods and instruments. It can be viewed as computer automated analysis and exploration of data and knowledge (best practices) used in companies to design of plastic parts, their manufacturing technologies, including the design of injection moulds. Knowledge discovery, machine learning or data mining often involve large databases. However, the databases are usually built for purposes other than knowledge discovery, and there are often many redundant attributes in the databases. This increases the complexity of knowledge discovery and decreases the quality of the discovered knowledge. The knowledge discovery requires the following steps:

• Pre-processing of continuous attributes, optimizing the values and eliminating useless attributes.

• Finding a good reduct (sometimes called feature selection) from attributes of a decision system.

• Extracting knowledge from a decision system.

• Testing the rules and applying them to make decisions.

Our approach was to develop a helpful system to support effective and rational decision making in a mould design process that uses existing CAD elements described through MS Excel spreadsheets to represent collected knowledge. Therefore standard MS Excel techniques were used for modeling decisions. Our proposed decision analysis technique helps:

• To model plastic part, technology planning and solve mould design problems as decision trees.

• Analyze decision based models & make better design decisions based on best praxis of companies and using pattern recognition technique.

• Customize CAD system (UG NX MOLDWIZARD with UG Knowledge Fusion technique) for plastic part and mould design

• Communicate results of effective design options, underlying analysis, best design decisions and supporting assumptions

The approach is deeply integrated into the system processes and not just a simple add on, or interface to an existing system.

2.1 Knowledge acquisition The overall plastic part manufacturing process includes product design, material selection, manufacturing process selection, mould design and mould manufacturing process selection. Mould design process, shown in Fig. 1, includes mould base design, core/cavity design, etc. In mould base design process engineers have to select mould type, mould dimensions, machining methods and mould material according to plastic product parameters.

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Fig. 1. Stages in mould design process

For every decision making stage there has to be developed the most reasonable and suitable technique to evaluate collected knowledge. In this paper we choose, as an example, mould base assembly related decisions making (mould type and dimension selection) to show how the proposed methodology could be used in the Unigraphics NX MOLDWIZARD environment. 3. EVALUATION OF COLLECTED KNOWLEDGE Mould base assembly design during various stages is different. Mould base design includes mould type selection, mould dimension selection, machining methods selection and mould material selection. Therefore, these different design stages cannot be represented just by one kind of knowledge representation. Depending on what kind of decisions we have to make, reasonable method or mechanism must be chosen. Each manufacturer has developed its own mould type standards. Our example based on DME mould base standard components. DME provide six different mould types to choose from the UG NX MOLDWIZARD,

shown in Fig. 2. Each mould type has its own specification and number of plates (of type TCP, AP, BP, CP, etc) which depends on different plastic product parameters. Product parameters are also input features in pattern recognition technique. The fundamental objective for pattern recognition is to provide a meaningful categorization of the input. It can be considered to be a two stage process: feature extraction and pattern classi-fication. A feature is a measurement taken on an input pattern that is to be classified. A suitable feature is one which will provide a definite characteristic of the input. The pattern classifier is then supplied with a set of features which are then mapped onto a classification state. Given the input features, the classifier must decide which type of category matched most closely. Classifiers typically rely on distance metrics and probability theory to do this [8]. As pattern classifier we choose Bayesian classifiers theory. Based on this theory, decision rule calculation uses the following equation:

gi(x) = –21

(x – µi)T Σ–-1i(x – µi) + ln P(ωi) + ci,

(1)

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Type 1 Type 2 Type 3

Type 4 Type 5 Type 6

Fig. 2. Types of mould constructions: TCP, BCP – clamping plates, AP, BP – cavity plates,

CP – risers, SPP – support plate, NP1 – intermediate plate number 1, NP2 – intermediate plate number 2.

where x is a given feature vector (parameters of product, cavity layout and number), ωi is classes (types of mould construction), µi is the mean value of the ωi class and Σi is covariance matrix defined as Σi = E[(x – µi)( x – µi)T]. Also the appended parameters or indexes are used in different Excel spreadsheet files in this paper. Design parameters can capture and represent the information which reflects the usability of certain types of elements in mould design process. Accordingly to these parameters, the mould designers can evaluate which elements are the most suitable in a specific mould design project. The meaning of the formula (1) of pattern recognition technique is to calculate the different numerical values for various mould base types. It reflects the most approximate mould base type according to input criterions of plastic product. After that numerical values are sorted increasingly.

Resorted list is reflected in mould base selection type area (Fig. 3). 4. INTEGRATION THE DESIGN KNOWLEDGE INTO CAD SYSTEM Computer-aided knowledge based engineering technique [2] has been used to represent the injection moulding process to assist the overall mould design. The researchers have started to adopt a knowledge-based approach to solving the injection moulding and mould design problems in recent years. DTMOULD-1 [1] is a knowledge based system for injection mould cost estimation. MOULDX [1] and EIMPPLAN-1 [1] incorporated mouldability considerations into part designs and addressed the conceptual design development of injection moulded parts. ICAD [1], the knowledge based system of Drexel University [1], was developed for injection mould design.

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The commercially available package Unigraphics NX MOLDWIZARD is a convenient program to customize the different mould design scenarios. The single standard elements or subassemblies could be described in Excel spreadsheet format. These available spreadsheets could be customized according to the specific enterprise needs. To analyze decision based models and make design decisions based on best praxis of companies, the pattern recognition technique described afore was applied to the Excel spreadsheet. The goal was to extend existing possibilities in the Unigraphics NX MOLDWIZARD. It helps to support the mould design process in tool making companies. For example, if Mouldbase Management dialog window in NX MOLDWIZARD was selected (Fig. 3), it is clearly noticeable that two Excel spreadsheet file are used: 1. Mould base register file and 2. Corresponding mould base database

file. In mould base register file “mouldbase_-reg_mm.xls”, pattern

recognition technique was used, to find what type of mould base components are most suitable for different specific criteria’s (for instance DME metrical mould base components). The goal is to sort different mould base types so what the most reasonable mould base type is the first selectable in the “Mold base Management” dialog window (Fig. 3). It helps the mould designer, to select the most reasonable mould base type to his specific mould design project. Also, as more specific data which correspond to input classifier are entered to the mould base register file, the overall mould base type selection becomes more accurate. Additionally, new parameter called “Index of usability” was entered. The meaning of “Index of usability” is to record every single selection of a mould base variant with specific dimensions in “Moldbase Management” dialog window and copy that information back to the mould base database file. It gives to mould designer the possibility to evaluate which mould base variants are mostly used in same kind of projects.

Type of mould base catalogue.

The resorted mould types according to pattern recognition technique in mould base register

file.

The indexes which reflect the different

dimensions of selected mould

type.

Fig. 3. Mold base Management dialog window

The shortcuts to two Excel spreadsheet file (mould base register file and

specific database file.

Additional variable “Index of

usability”.

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Additional variable “Index of usability” is visible in MOLDWIZARD “Moldbase Management” dialog window, in variable sector (Fig. 3). The suitability of this methodology was tested on practical examples based on developed questionnaires and pattern recognition technique. The results indicates that pattern recognition technique as additive element in NX MOLDWIZARD’s mould base register file and additional variables is a convenient base for further research. 5. CONCLUSION This paper has presented an approach and methodology of supportive technique of CAD-based mould design procedure based on existing CAD resources (Unigraphics NX MOLDWIZARD and Unigraphics NX Knowledge Based Engineering approach) with collected experimental data. The proposed methodology does not aim to replace existing systems in companies but rather to be a supportive tool for communicating and sharing specific heuristical and empirical data. In the implementation of the proposed methodology in CAD could facilitate the utilization of specific knowledge in tool making companies. The future direction of this research is to integrate the presented approach with Unigraphics Knowledge Fusion language. Due to Knowledge Fusion , Unigraphics CAE system takes advantage of engineering knowledge bases in conjunction with mould making design rules and. Therefore the possibilities to deliver powerful solutions while permitting knowledge based extension of Unigraphics by a mould designer could be available. 6. ACKNOWLEDGEMENTS The research was supported by the Estonian Science Foundation (Grant 5620).

7. REFERENCES 1. Mok C. K., Chin K. S. and John K.

L. Ho. An Interactive Knowledge-Based CAD System for Mould Design in Injection Moulding Processes. The International Journal of Advanced Manufac-turing Technology, Vol. 17, 2001, 27 – 38.

2. Knowledge Fusion for Designers. Student Guide February 2004 MT15130 Unigraphics NX 2.

3. Huang T., S. IMD – an aided design software for the design of injection moulds. Pacific Con-ference on manufacturing, 1994, 377 – 385.

4. Xiang Q., Bell D., McGinnity M. Multiknowledge in decision making. Knowledge and Informa-tion Systems, 2004

5. Rodriguez K., Al-Ashaab A., “Knowledge web-based system architecture for collaborative product development”, Computers in Industry, Vol. 56, 2005, 125-140.

6. Lardeur E., Longueville B., “Mutual enhancement of systems engineering and decision-making through process modeling: toward an integrated framework”, Vol. 55, 2005, 269 - 282

7. Russell S., Norvig P., “Artificial Intelligence: A modern Approach”, 2003, 1081.

8. Theodoridis S., Koutroumbas K., “Pattern Recognition. Second Edition”, 2003, pp. 689.

Corresponding author: M.Sc. Aigar Hermaste Department of Machinery Faculty of Mechanical Engineering Tallinn University of Technology, TUT Ehitajate tee 5, 19086 Tallinn, Estonia Tel: Int +372 620 3269 Fax: Int +372 620 3250 E-mail: aigar@staff.ttu.ee

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