International TEAMTech Conference, Bangalore, 4-6 October 2007

Domain Knowledge Modelling for DFM of Castings

Pavan Kumar Reddy, M.Tech Student Kamalesh Singh, Research Engineer

Shrinkhla Sagar, B.Tech, NIFFT Shweta Gupta, B.Tech, IT-BHU

Dr. B.Ravi, Professor *

Department of Mechanical Engineering Indian Institute of Technology Bombay, Mumbai – 400 076

* bravi@me.iitb.ac.in

Abstract There is an increasing demand for dimensionally-accurate, high-quality, low-cost castings developed and delivered in ever shorter time. These requirements can no longer be achieved by process improvements alone, and require early planning, starting from the product design stage itself. Unfortunately, product designers have very little knowledge of process capabilities, which is necessary to design process-friendly castings. In this paper, we present a framework for generating automatic suggestions for product design improvement, driven by casting domain knowledge base. The domain knowledge is modeled using XML, a self-describing language suitable for use over the Internet. It captures distilled experience in the form of rules regarding various casting features (mainly junctions and holes), in terms of feature geometry, material and process parameters leading to various casting defects (such as shrinkage porosity and cold shuts). For this purpose, various casting junctions and their variations have been classified in a systematic manner. Illustrated suggestions are linked to the knowledge base and are triggered when a particular rule is violated. The entire system is explained using an industrial case study. Key words: Casting, DFM, Knowledge representation, Junctions, Sections, XML 1. Introduction As the world is becoming a global village, the competition among the companies and organizations around the world is growing, which is forcing them to produce high quality products at a lower price. In order to achieve this, one of the key strategies is to reduce the wastage of time and material by integrating the design and manufacturing activities. Design for Manufacturability (DFM) is an approach which encourages a thorough interaction between product design and process design (Ventaktachalam, 1993). Metal casting is one of the direct methods of producing complex shapes by pouring the molten metal in mould within which it subsequently solidifies (Ransing, 1995). For producing a product, a number of materials, processes and techniques are involved which makes it a complex process. For employing the DFM approach to casting which is Design For Casting (DFC), the design engineer should have thorough knowledge about the process capabilities to design a quality product. Apart from this, the inflexibility in the

organizational structure hinders the interaction between the design engineer and manufacturing engineer. Metal casting being complex manufacturing process is susceptible to many defects. Producing a completely defect free casting is almost impossible. The sources of defects may be material, pouring, solidification, gating and risering or pattern equipment. The primary defects which will appear during solidification are shrinkage and blowholes. The main sources for the shrinkage defect are thick sections and junctions. By proper classification and analysis of the various junctions that are possible in casting, shrinkage defect can be easily predicted and prevented. Expert systems or Knowledge Based Systems have been employed in various fields like identifying and planning inspection schedules for critical components of an offshore structure; training technical personnel in the design and evaluation of energy cogeneration plants; configuring paper feeding mechanisms; carrying out automatic re-meshing during a finite elements analysis of forging deformation; selecting cutting tools or cutting data (Cakir, 2006). A lot of research has been done in evaluating the use of expert systems in manufacturing applications, including casting. A rule-based expert system for casting process selection was developed by Er and Dias (Er, 2000). Knowledge based expert system for the analysis of casting defects has been developed by Roshen (Roshen, 1989). Dwivedia et al proposed the development of knowledge based engineering module for diagnosis of defects in casting and interpretation of defects by non destructive testing (Dwivedia, 2003). Similarly Knowledge Based approach to Design for Manufacturability has been proposed by Venkatachalam et al (Venkatachalam, 1993). In this paper we are presenting a knowledge-based framework for DFC. Casting involves diverse domain specific knowledge which may not be available for casting product designers and the system is designed to help them to assess a given design situation and make a proper choice. The system generates suggestions to improve the product design by verifying the production rules stored. The knowledge stored in the form of production rules needs to have interoperability for sharing it over different software platforms and using it over internet. eXtensible Markup Language (XML) is one of the suitable tools for achieving such kind of interoperability, which has been employed here. 3. Casting Domain Knowledge Tree The quality of a cast component is driven by design, tooling and methoding (feeding and gating design). Design involves comparing the part features with the process capability values. Methoding involves design of size, shape and location of riser, runner and gates. Tooling includes design of moulds and cores. These led to three important checks which must be done before going for the production of any cast component viz. feedability, fillability and mouldability analysis. As shown in the figure, the casting domain knowledge tree covers three important checks to be made for producing a quality cast component. In this work we are considering the junctions and sections which lead to the defects related to solidification such as shrinkage and blowholes. For this purpose various junctions have been identified, which are explained in detail in next section.

Fig.1. Casting Domain Knowledge Tree 4. Sections and Junctions in Casting A section is defined as the representation of a solid object as it would appear if cut by an intersecting plane, so that the internal structure is displayed. Sections can be viewed in a casting as a joining feature between two junctions and can be of types like straight sections, straight sections with a hole, a C section or an O section. These are very common example of sections which are easily observed when a casting is observed minutely. Various types of sections with some possible variations are shown in Fig.2. A junction is a region in which different section shapes come together within an overall casting geometry (Gwyn, 1998). Simply stated, junctions are the intersection of two or more casting sections. There are basically five types of junction represented by L, T, V, X, and Y. All other configurations at corners could be considered as modification of one or more of these five (ASM, 1962). The junction represented by K is also considered here. Some of the identified junctions with some possible variations are shown in Fig.3. There are some features like C, O which falls in simple section category, but if they intersect any other section, they make junctions which are different from any other previously defined junctions. Similarly if any L, T, Y, K or X junction meets with any other sections then they create a complete different set of junctions known as 3D junctions (Fig.4). The parameters of a junction include the number of meeting sections, their thicknesses (absolute and relative), angle between them and fillet radius. Fillets are used at junctions to eliminate sharp inside corners and the attendant problems of stress concentration.

Casting

Fillability analysis

Cooling roots

Material Tooling

Feedability

Feedability Gating design

Process

Feed path Hot spots

Normal

SectionJunction

3D junctions

Complete filling

Uniform filling

Smooth filling

Mould element

Mouldability

Mould and core

Mould complexity

Shape confirmity

Fig.2. Examples of sections and their variations

Fig.3. Examples of Normal Junctions and their variations

Fig.4. Examples of 3D Junctions and their variations For these junctions, design guidelines are developed. The guidelines are in terms of the relative sizes of neighboring sections, the fillet radius to be provided at junctions and other possible options to avoid hot spots. 5. DFM of Castings through Domain Knowledge Modelling 5.1 Representation of Casting Domain Knowledge The knowledge of casting domain is captured in the form of IF- THEN rules, which are represented in a format that supports structured representation of information. The need for a universally acceptable format for storing these rules arises because of the increasing demand for cooperation between various departments of an organization and between

different organizations which use different platforms of knowledge representation. eXtensible Mark-up Language (XML) is one of such languages which is becoming popular now-a-days. XML has the advantage of user defined tags according to the specific domain. It allows the user to store knowledge about knowledge which is called Metadata. XML is the ideal candidate format for exchange and processing of data through internet (Varela, 2002). 5.2 Knowledge Based System For implementing the DFM approach to casting through the domain knowledge available, the knowledge is represented in the form of IF-THEN rules. The user provides the necessary input like part features, process parameters etc. These are compared with the rules stored in the system. By evaluating the input the system detects the possible defects that may arise. The DFM guidelines to be followed are also provided in the form of suggestions. The rules written in XML format are called as Casting Knowledge Bit (CKB). By comparing the input provided by the user with these KnowledgeBits inferences are made by inference engine. There are two kinds of approaches followed by the inference engines viz. forward and backward chaining. Forward chaining, which is also called “data driven approach” is used when the number of input parameters to be supplied is less. Backward chaining, which is also called “goal driven approach” is used where the inference is made by moving in backward direction starting from a goal towards the cause.

Fig.5. Knowledge base system As shown in the above figure the user requests for an advice by providing the necessary information asked by the system. The inference engine communicates with the knowledge base. It draws conclusions about the possible defects using one of the above inference techniques, which will be presented to the user through user interface. These defects will be coupled with DFM guidelines related to the predicted defect and shown to the user. A sample piece of XML code is shown below. This is related to shrinkage defects in steel sand castings. The code deals with the possible factors which may lead to shrinkage cavity. To deal with the uncertainty in the rules commonly followed approaches are either using fuzzy logic or adding certainty factors to the rules.

Operating parameters causing defects

Interface

U S E R

Interface

E N G I N E

Casting defects Knowledge base

Request for advice

Product and process information

Defects and suggestionsUser

<KnowledgeBit> <KnowledgeField> <Filed_Title value="SHRINKAGE CAVITY"/> <author VALUE="B.RAVI"/> <place value="IIT BOMBAY"/> <author_email value="BRAVI@ME.IITB.AC.IN"/> <keywords value="DEFECTS, SHRINKAGE CAVITIES"/> <!--<field_description="The defects which are caused by not providing sufficient metal to compensate for the shrinkage of molten metal are represented along with the reasons for this to occur."/>--> </KnowledgeField> <IfThen> <if> <ifPar1 value="T2/T1" /> <ifRel1 value="&gt;" /> <ifVal1 value="2" /> <ifOpr1 value="OR" /> <ifPar2 value="POURING.HEAD" /> <ifRel2 value="=" /> <ifVal2 value="LOW" /> <ifOpr2 value="OR" /> <ifPar3 value="RISER.SIZE" /> <ifRel3 value="=" /> <ifVal3 value="INSUFFICIENT" /> <ifRel3 value="OR"/> <ifPar4 value="PHOSPHOROUS.CONTENT" /> <ifRel4 value="&gt;" /> <ifVal4 value="0.03%" /> <ifOpr4 value="OR" /> <ifPar5 value="CARBIDE_STABILIZERS" /> <ifRel5 value="=" /> <ifVal5 value="HIGH" /> <ifOpr5 value="OR" /> <ifPar6 value="COMBINED_CARBON" /> <ifRel6 value="=" /> <ifVal6 value="HIGH" /> </if> <then> <thenPar1 value="DEFECTS.NAME" /> <thenRel1 value="=" /> <thenVal1 value="SHRINKAGE_CAVITY" /> </then> </IfThen> </KnowledgeBit>

Fig.6. Sample XML code for shrinkage defects related to steel castings

6. Case Study The case study involved a grey iron casting known as Cast Iron Track of overall size 8000mm X 1115mm, weighing 6500 Kg, manufactured by a private sector unit. The casting consists of various junctions and sectional changes and was produced with feeders on the top face and with bottom gating. The part information in the form of user inputs was checked against the rules in the XML code. The result was the prediction of shrinkage defect at the junctions. This is validated by checking the casting through AutoCAST (Fig.7), confirming the possible location of defect.

Fig.7. DFM Guideline on a real casting design

7. Conclusions and Future Work There is a need for developing an expert system which helps the design engineer for implementing DFM for castings at the design phase itself. The system will have interoperability among different platforms of knowledge representation and can be accessed through internet. The system will be able to predict the possible defect based on the information provided by the user and will come up with a set of DFM guidelines for the predicted defect so that a design engineer can change the part design if possible to have manufacturability of the casting. In the present system, the inputs related to product and process information are provided by asking the user a series of questions. There is a scope to develop an advance expert system shell in which the input will be only an STL file of the part model and the system will extract the information based on feature recognition techniques for predicting the possible defects. REFERENCES 1. Cakir, M. C., Cavdar, K., “Development of a knowledge-based expert system for

solving metal cutting problems,” Journal of materials and design, Vol. 27, Issue 10, pp. 1027-1034, 2006.

2. Casting design defect handbook, ASM, pp. 51, 1962. 3. Dwivedia, S.N, Sharan, A., “Development of knowledge based engineering module

for diagnosis of defects in casting and interpretation of defects by non destructive testing,” Journal of Materials Processing Technology, Vol.141, pp.155–162, 2003.

4. Er, A., Dias, R., “A rule-based expert system approach to process selection for cast components,” Knowledge based systems, Vol. 13, issue 4, pp. 225-234,10 June 2000.

5. Gwyn, M.A., “Cost-Effective Casting Design: What Every Foundryman and Designer Should Know,” Modern Casting, Vol. 88, No. 5, pp. 32-36, 1998.

6. Ko, K.H., Pochiraju, K., Manoochehri, S., “An embedded system for knowledge-based cost evaluation of molded parts,” Knowledge-Based Systems Vol.20, pp.291–299, 2007.

7. Ransing, R.S., Srinivasan, M.N., Lewis, R.W., "ICADA – intelligent computer aided defect analysis for castings", Journal of Intelligent Manufacturing, No.6, pp.29-40, 1995.

8. Roshen, H. M., “Knowledge based expert system for analysis of casting defects,” AFS Transactions, Vol.96, pp.145-150, 1988.

9. Varela, L. R., Aparício, J. N., Silv, S. C., “An XML Knowledge Base System for Scheduling Problems,” Proceedings of the Second International Workshop on Innovative Internet Computing Systems, pp. 63-74, 2002.

10. Venkatachalam, A.R., Mellichamp, J.M., Miller, D.M., “A knowledge-based approach to design for manufacturability,” Journal of Intelligent Manufacturing, Vol. 4, pp. 355–366, 1993.