Cognitive Approach in Castings’ Quality Control

Every year production volume of castings grows, especially grows production volume of non-ferrous metals, thanks to aluminium. As a result, requirements to castings quality also increase. Foundry men from all over the world put all their efforts to manage the problem of casting defects. In this article the authors present an approach based on the use of cognitive models that help to visualize inner cause-and-effect relations leading to casting defects in the foundry process. The cognitive models mentioned comprise a diverse network of factors and their relations, which together thoroughly describe all the details of the foundry process and their influence on the appearance of castings’ defects and other aspects.. Moreover, the article contains an example of a simple die casting model and results of simulation. Implementation of the proposed method will help foundry men reveal the mechanism and the main reasons of casting defects formation.

Teaching Statistics to Engineers: Learning from Experiential Data

The purpose of the work is to claim that engineers can be motivated to study statistical concepts by using the applications in their experience connected with Statistical ideas. The main idea is to choose a data from the manufacturing factility (for example, output from CMM machine) and explain that even if the parts used do not meet exact specifications they are used in production. By graphing the data one can show that the error is random but follows a distribution, that is, there is regularily in the data in statistical sense. As the error distribution is continuous, we advocate that the concept of randomness be introducted starting with continuous random variables with probabilities connected with areas under the density. The discrete random variables are then introduced in terms of decision connected with size of the errors before generalizing to abstract concept of probability. Using software, they can then be motivated to study statistical analysis of the data they encounter and the use of this analysis to make engineering and management decisions.