22-10-2014, 09:55 AM
Abstracts: In today’s competive environment of foundry industry, reducing manufacturing cost by improved productivity & quality through innovative ideas is becoming mandatory on each faoudryman. Casting manufacture is a value adding process and productivity is the key performance metric for any foundry’s efficiency and effectiveness. Every foundry management continuously strives to improve casting productivity. We will discuss in this presentation, how to reduce investment casting defect using statistical approaches. In this project, we are going to use data mining. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time consuming to resolve. So we are going to develop a regression approaches. Regression is the oldest and most well-known statistical technique that the data mining community utilizes. Basically, regression takes a numerical dataset and develops a mathematical formula that fits the data. Using that we can reach more closely to the specific parameters which are responsible for particular defect. In addition excessive rejection reduces yield, wastes, variable raw material and involves management time in problem solving