New Approach to Casting Defects Classification and ...

1 New Approach to Casting Defects Classification and Analysis Supported by Simulation , Amit Sata2 and M. Y. Khire3 1 Senior Lecturer, Mechanical Engineering Department, College of Engineering, Osmanabad, E-mail: 2 Lecturer, Mechanical Engineering Department, Engineering College, Rajkot, E-Mail: 3 Principal, Padmashri Dr. Vitthalrao Vikhe Patil, College of Engineering, Ahmednagar, E-mail: Abstract Foundry industry suffers from poor quality and productivity due to the large number of process parameters, combined with lower penetration of manufacturing automation and shortage of skilled workers compared to other industries. Global buyers demand defect-free castings and strict delivery schedule, which foundries are finding it very difficult to meet.

2 Casting Defects result in increased unit cost and lower morale of shop floor personnel. The Defects need to be diagnosed correctly for appropriate remedial measures, otherwise new Defects may be introduced. Unfortunately, this is not an easy task, since Casting process involves complex interactions among various parameters and operations related to metal composition, methods design, molding, melting, pouring, shake-out, fettling and machining. For example, if shrinkage porosity is identified as gas porosity, and the pouring temperature is lowered to reduce the same, it may lead to another defect, namely cold shut.

3 So far, Casting defect analysis has been carried out using techniques like cause-effect diagrams, design of experiments, if-then rules (expert systems), and artificial neural networks. Most of the previous work is focused on finding process-related causes for individual Defects , and optimizing the parameter values to reduce the Defects . This is not sufficient for completely eliminating the Defects , since parameters related to part, tooling and methods design also affect Casting quality, and these are not considered in conventional defect analysis approaches. In this work, we present a 3-step Approach to Casting defect identification, analysis and rectification.

4 The Defects are classified in terms of their appearance, size, location, consistency, discovery stage and inspection method. This helps in correct identification of the Defects . For defect analysis, the possible causes are grouped into design, material and process parameters. The effect of suspected cause parameters on Casting quality is ascertained through simulation. Based on the results and their interpretation, the optimal values of the parameters are determined to eliminate the Defects . The proposed Approach overcomes the difficulty of controlling process parameters in foundries with manual processes and unskilled labor, by making the design more robust (less sensitive) with respect to process parameters.

5 This will especially help SME foundries to significantly improve their quality levels. 1. Introduction Metal Casting is one of the direct methods of manufacturing the desired geometry of component. The method is also called as near net shape process. It is one of the primary processes for several years and one of important process even today in the 21st century. Early applications of Casting are in making jewellery items and golden idols. Today, Casting applications include automotive components, spacecraft components and many industrial & domestic components, apart from the art and jewellery items. The principle of manufacturing a Casting involves creating a cavity inside a sand mould and then pouring the molten metal directly into the mould.

6 Casting is a very versatile process and capable of being used in mass production. The size of components is varied from very large to small, with intricate designs. Out of the several steps involved in the Casting process, moulding and melting processes are the most important stages. Improper control at these stages results in defective castings, which reduces the productivity of a foundry industry. Generally, foundry industry suffers from poor quality and productivity due to the large number of process parameters, combined with lower penetration of manufacturing automation and shortage of skilled workers compared to other industries.

7 Also, Global buyers demand defect-free castings and strict delivery schedule, which foundries are finding it very difficult to meet. Casting process is also known as process of uncertainty. Even in a completely controlled process, Defects in Casting are found out which challenges explanation about the cause of Casting Defects . The complexity of the process is due to the involvement of the various disciplines of science and engineering with Casting . The cause of Defects is often a combination of several factors rather than a single one. When these various factors are combined, the root cause of a Casting defect can actually become a mystery.

8 It is important to correctly identify the defect symptoms prior to assigning the cause to the problem. False remedies not only fail to solve the problem, they can confuse the issues and make it more difficult to cure the defect. The Defects need to be diagnosed correctly for appropriate remedial measures, otherwise new Defects may be introduced. Unfortunately, this is not an easy task, since Casting process involves complex interactions among various parameters and operations related to metal composition, methods design, molding, melting, pouring, shake-out, fettling and machining. The proper Classification and identification of a particular defect is the basic need to correct and control the quality of Casting .

9 2. Present Approaches for Analysis of Casting Defects At present, Casting defect analysis is carried out using techniques like historical data analysis, cause-effect diagrams, design of experiments, if-then rules (expert systems), and artificial neural networks (ANN). They are briefly explained in this section. ShutCrushKnock ..BlowholeHouling ..Bad MoldScabFet..ShrinkSlagBad CoreCores ..MismatchFettling ..RunoutHardSlurry ..Low ..OthersCore ..SwellSinkPercentage Historical Data Analysis To understand this concept, data for occurrence of Defects are collected from one of leading Casting manufacturer in Maharashtra for one year.

10 From this data, occurrence chart has been prepared which further helps to identify occurrence major Defects in castings. These data further help to prepare the chart for occurrence of defect. The details are shown in table 1 and fig. 1. Data of Casting Defects Figure 1: Pareto Analysis of Casting Defects Defects Rejected Quantity Job Rejection % Defects Rejected Quantity Job Rejection % Cold Shut 205 Cores Broken 16 Crush 188 Mismatch 13 Knock Crack 165 Sub. Contract Fettling Fault 10 Blowhole 123 Runout 7 Contractor s Houling Cracks 55 Hard 7 Bad Mold 42 Slurry Penetration 4 Scab 40 Low Hardness 3 Fet.