Role of Sand Grains in Sorption Processes by Surface Layers of Components of Sand Moulds

The results of researches of sorption processes of surface layers of components of sand moulds covered by protective coatings are presented in the hereby paper. Investigations comprised various types of sand grains of moulding sands with furan resin: silica sand, reclaimed sand and calcined in temperature of 700°C silica sand. Two kinds of alcoholic protective coatings were used - zirconium and zirconium - graphite. Tests were performed under condition of a constant temperature within the range 30 - 35°C and high relative air humidity 75 - 80%. To analyze the role of sand grains in sorption processes quantitavie moisture sorption with use of gravimetric method and ultrasonic method were used in measurements. The tendency to moisture sorption of surface layers of sand moulds according to the different kinds of sand grains was specified. The effectiveness of protective action of coatings from moisture sorption was analyzed as well. Knowledge of the role of sand grains from the viewpoint of capacity for moisture sorption is important due to the surface casting defects occurrence. In particular, that are defects of a gaseous origin caused by too high moisture content of moulds, especially in surface layers.

Surface Casting Defects Inspection Using Vision System and Neural Network Techniques

The paper presents a vision based approach and neural network techniques in surface defects inspection and categorization. Depending on part design and processing techniques, castings may develop surface discontinuities such as cracks and pores that greatly influence the material’s properties Since the human visual inspection for the surface is slow and expensive, a computer vision system is an alternative solution for the online inspection. The authors present the developed vision system uses an advanced image processing algorithm based on modified Laplacian of Gaussian edge detection method and advanced lighting system. The defect inspection algorithm consists of several parameters that allow the user to specify the sensitivity level at which he can accept the defects in the casting. In addition to the developed image processing algorithm and vision system apparatus, an advanced learning process has been developed, based on neural network techniques. Finally, as an example three groups of defects were investigated demonstrates automatic selection and categorization of the measured defects, such as blowholes, shrinkage porosity and shrinkage cavity.