Machine vision system for automatic inspection of surface defects in aluminum die casting

A camera based machine vision system for the automatic inspection of surface defects in aluminum die casting is presented. The system uses a hybrid image processing algorithm based on mathematic morphology to detect defects with different sizes and shapes. The defect inspection algorithm consists of two parts. One is a parameter learning algorithm, in which a genetic algorithm is used to extract optimal structuring element parameters, and segmentation and noise removal thresholds. The second part is a defect detection algorithm, in which the parameters obtained by a genetic algorithm are used for morphological operations. The machine vision system has been applied in an industrial setting to detect two types of casting defects: parts mix-up and any defects on the surface of castings. The system performs with a 99% or higher accuracy for both part mix-up and defect detection and is currently used in industry as part of normal production.

Seeding of single crystal superalloys--Role of seed melt-back on casting defects

Single crystal seeds of the nickel-base superalloy CMSX-4 have been partially melted in a temperature gradient and then quenched. Small islands of random orientation are observed throughout the melted-back semi-solid. These random orientations appear to be pinched-off secondary dendrite arms, but there is no evidence that they are transported ahead of the dendrite tips to nucleate stray grains during directional solidification.

Thermal control of high pressure die casting using computational fluid dynamics

The quality of high pressure die castings is a function of many interdependent parameters. It has been observed that many defects detected in the HPDC castings can be tracked back to poor die temperature distribution in the critical areas. It has therefore been recommended that the development of a technique to directly control the critical features - making them less sensitive to thermal related parameters - be very beneficial to the HPDC industry. From the information obtained from thermal image (processing), computational fluid dynamics has been applied to design the layout of internal cooling system and assign the flow conditions such as flow rate and pressure of the cooling water. it is observed that CFD prediction provides an excellent insight into the thermal balance of the high pressure die casting.

Modelling of porosity defects in high pressure die casting with a neural network

High Pressure Die Casting (HPDC) is a complex process that results in casting defects if configured improperly. However, finding out the optimal configuration is a non-trivial task as eliminating one of the casting defects (for example, porosity) can result in occurrence of other casting defects. The industry generally tries to eliminate the defects by trial and error which is an expensive and error -prone process. This paper aims to improve current modelling and understanding of defects formation in HPDC machines. We have conducted conventional die casting tests with a neural network model of HPDC machine and compared the obtained results with the current understanding of formation of porosity. While most of our findings correspond well to established knowledge in the field, some of our findings are in conflict with the previous studies of die casting.

Machine vision system for automatic inspection of surface defects in aluminium die casting

A machine vision system is presented for the automatic inspection of surface defects in aluminium die casting. The system uses a hybrid image processing algorithm based on mathematic morphology to detect defects with different sizes and shapes. The defect inspection algorithm consists of two parts. One is a parameter learning algorithm, in which a genetic algorithm is used to extract optimal structuring element parameters, and segmentation and noise removal thresholds. The second part is a defect detection algorithm, in which the parameters obtained by a genetic algorithm are used for morphological operations. The machine vision system has been applied in an industrial setting to detect two types of casting defects: parts mix-up and any defects on the surface of castings. The system performs with a 99% or higher accuracy for both part mix-up and defect detection and is currently used in industry as part of normal production.

Cracking and blowout in an aluminium high-pressure die casting die

Die cracking and metal blowout have been identified as problems in production of the structural sump, a high pressure die cast aluminium part, at Ford's Geelong manufacturing plant. Visual inspection, thermography and strain measurements have been performed and results are consistent with the view that cracking and blowout are caused by excessive stresses and deflections, respectively, generated by bending of the sliding cores. Models are being developed for finite element simulation of the stresses and deflections in the die during production, with a view to eliminating the aforementioned problems.

Squeeze pin implementation in a high pressure die casting

This paper discusses the implementation of hydraulically operated squeeze pins to reduce porosity formation in cast aluminium bearing caps. Two complete sets are cast in an eight-cavity die with a 2000t cold chamber high pressure die casting machine. The initial die configuration used a sliding core assembly with stationary pins to core a through hole in a thick section of the front cam caps. This configuration resulted in high post machining scrap rates, primarily due to porosity associated with solidification shrinkage. Replacement of the sliding core assembly with a squeeze pin unit substantially reduced shrinkage porosity in the critical region, with consequent reductions in the scrap rate. The squeeze pins are actuated 1.5s after the piston reaches the high shot changeover position, but can be successfully engaged between I and 3.5 seconds after high shot changeover. Density measurements and visual inspection confirmed the substantial improvement in porosity levels in the critical region of the castings.