No abstract

The three-dimensional sand mold printing technology (3DSP) for casting sand molds via a binding jet is a breakthrough in the casting mold-making process. It is a favorable combination of digital forming technology and sand casting, which is a significantly interesting research area in the foundry industry. This study has proposed an edge extraction approach for the forming region in the sand bed image. With the edge information, the study measures the forming accuracy of the printed molds, which offers a basis for assessing the forming quality after 3DSP. The extracted edges essentially match the original image through the validation of cube printing. The error between the measured and actual size is below 0.6 mm, and the standard deviation of the straight line edge is below 0.170 mm, which fulfills the accuracy requirements for 3D sand mold printing.

Within the framework of casting process design, the efficient retrieval of three-dimensional (3D) computer-aided design (CAD) models could result in significant time and cost savings. However, this technique still suffers from inefficiency and inaccuracy because of the wide variety of casting models' poses. In this study, a method for normalizing the poses of casting models is proposed. This method constructs the transformation matrix through the eigendecomposition of the second-order central moment matrix calculated from the voxel casting model. Then the transformation matrix is applied to the casting model to get a normalized pose. An assessment approach for pose normalization is also suggested in the study, which measures the distance between poses normalized based on multiple poses of the same model. The study demonstrates that the pose-normalization approach reliably transforms distinct poses of the same model into a unified pose. The mean distance between normalized poses is 0.016 and the minimum distance is 0.010. The method's effects improve when the voxel size reduces.

G. Cortes Robles’ TRIZ (Russian acronym for “Theory of Inventive Problem Solving”)–CBR (Case-based reasoning) model has the capacity to accelerate conceptual design and to find inventive solutions. This paper describes how the TRIZ–CBR model can be enhanced by introducing the weight sensitivity. Of course, weight used to measure the relative importance of multiple attributes is a very important component of the similarity function, which has a great influence on the CBR results. By means of weight sensitivity analysis, the similar cases which possess high reference value among retrieved cases can be selected. A laundry drum product example was given to verify the validity of the model.