Formation of the burrs in the industrial parts after manufacturing processes such as blanking, punching, machining, cutting and etc. is inevitable. The burring phenomenon decreases the geometrical and dimensional tolerance and makes assembling parts together difficult. Radiusing the corners of workpieces is a common technique to reduce the stress concentrations at corners and also facilitate their assembly. By means of barrel finishing, a number of workpieces undergo deburring and radiusing coincidently in one process. In barrel finishing which is one of the mass finishing techniques, many workpieces with abrasive media are loaded in a rotary barrel. By rotation of the barrel, the parts and abrasive media are collided to each other and rub themselves, resulting in deburring and polishing the workpieces. The working time of this process is normally long. The aim of this study is to decrease the working time by using proper abrasive media. Here, a number of experiments is designed on combination of three abrasive media: steel balls, ceramics and aluminum oxide particles, in different working periods, to achieve appropriate radius and reducing burrs height of CK45 steel alloy samples in reduced working time. پرونده مقاله

The Barrel finishing process is a finishing method applied for cleaning, polishing, improving surface quality, Deburring, and rounding corners of both metallic and non-metallic parts. There are Several factors affect the final surface integrity of the barrel finished samples such as initial surface roughness, piece length, operation time, and different abrasive materials (i.e. aluminum oxide, steel balls, and ceramic). On the other hand, each factor has different levels, and handling this amount of data to reach desired results is approximately impossible due to the “curse of dimensionality”. Machine learning is a promising method to pave this avenue for computing huge amounts of data and predicting the future state of the system. Accordingly, in this study, it is attempted to apply a supervised machine learning algorithm, an artificial neural network- to improve surface quality in the barrel finishing process. Python is used to code the program and extract several simulations and related graphs. Results show that time has the greatest effect on surface roughness, moreover, among the different abrasive media, steel balls have the best performance to improve surface roughness and the combination of 75% steel balls and 25% aluminum oxide has the effective effect. The simulation results have an acceptable compatibility with experimental ones. پرونده مقاله

Airliner’s parts are very critical and should be manufactured properly without any defects, because every fault may lead to irreparable happening. Therefore, the manufacturing of the parts of airliners is very significant. One of the production methods for manufacturing these parts is die press metal forming (stamping). This process which is a kind of sheet metal forming process, in recent years has become one of the most widely used methods in the field of manufacturing industrial parts with complex geometry. Therefore, studying the effective factors in the proper execution of the process helps to achieve high-quality products and optimal geometry. To analyze the behavior of materials during the die press forming process, the FEM simulation procedure should be applied. In this study, manufacturing (stamping) of a part related to the tail of the 8-person capacity airliner, is investigated through FEM simulation. The FEM simulation is performed via ABAQUS software. Due to the importance of the weight of airliners, their parts are usually made of aluminum alloys. In this project, Al6061 is designated for the mentioned part. The results of the simulation show that the designed dies are suitable and the forming process is completely performed. پرونده مقاله