Ensuring Stability and Automatic Process Control with Deburring Process in Cast Z-Rot Parts

The undesirable protrusions and roughness that occur on the surfaces of the parts during or after the production process are called burrs, and the process applied to remove these burrs is called “burring”. Burrs are mostly formed around the cut edges. A little more material than the measure is transferred to the mold so that the metal is not missing in a piece to be poured into the mold. This excess, when the molds are compressed, overflows from the joints and creates casting burrs. Post-production burr removal is an important issue in the manufacturing industry. It is necessary to clean the burrs formed after the production in the z-rod parts produced by the casting method. Burrs formed in the holes cause material problems and connection problems. Burrs cause stress at the hole corners due to high stress, reducing cracking resistance and fatigue life. In mating parts, burrs will enter the connector seat and damage the connector or assembly. Burrs in the holes will also affect the coating thickness on the rough surfaces, thus increasing the risk of corrosion. Burrs on moving parts increase unwanted friction and heating. Currently, this process is generally cleaned manually by the operator using a deburring tool (blaster) or it can be cleaned using high cost and special features robot arm integrated systems. Manual cleaning causes a significant loss of productivity in the labor factor. On the other hand, deburring operation and post-operation product control vary according to the operator’s competence and initiative. Therefore, the cleaning operation is not always at the same standards. With this study, a system was designed for deburring, apparatus suitable for the dimensions of the z-rod parts. And the deburring process was carried out in a standard way for mass production. At the same time, the process accuracy was instantly checked with the highest accuracy by using the image processing algorithms with the Python programming language, and the burr types were determined by detecting the burr form and size with the deep learning method. System cleaning efficiency was measured as 92% higher than manual cleaning.