Friction stir spot welding (FSSW) is a solid-state joining process and a rapidly growing dissimilar material welding technology for joining metallic alloys in the automotive industry. Welding tool shape and process conditions must be appropriately controlled to obtain high bonding characteristics. In this study, FSSW is performed on dissimilar materials AA5052-H32 aluminum alloy sheet and SPRC440 steel sheet, and the influence of the shape of joining tool and tool insertion depth during joining is investigated. A new intermetallic compound is produced at the aluminum and steel sheets joint. When the insertion depth of the tool is insufficient, the intermetallic compound between the two sheets did not form uniformly. As the insertion depth increased, the intermetallic compound layer become uniform and continuous. The joint specimen shows higher values of tensile shear load as the diameter and insertion depth of the tool increase. This shows that the uniform formation of the intermetallic compound strengthens the bonding force between the joining specimens and increases the tensile shear load.

Carbon fiber reinforced plastics are typical examples of carbon fiber, which retain high strength and high strength at high temperatures. Also, it is applied to various fields, such as the structure of the aircraft, automobile, and the core industries. CFRP machining methods include machining tools such as cutting machining and laser machining. In this thesis, a tool dynamometer and tool microscope were used to measure the cutting characteristics of various cutting conditions and tool wear and tool wear. As a result, the cutting force of the new shape drill (lower shape drill) was measured higher than other tools, the amount of tool wear was the lowest, and it was found that a good machined surface was obtained.

On-line detection system of the abnormal states in a machining process needs to be developed to implement the IMS(Intelligent Manufacturing System). High productivity and efficient quality control can be achieved through the on-condition maintenance for normal tool condition. Generally it is difficult to determine the exact point of time for a tool change because a tool wear grows gradually on the contrary to other abnormal states such as tool fracture, chattering etc. In this article, the shape variation of cutting force signal generated by a insert during face milling was investigated along with a tool wear. The variance, skewness and kurtosis were used as the shape parameters to describe the shape variation and, consequently, utilized as the features to monitor a tool wear. Experimental results showed that the shape parameters could discriminate the tool condition reliably between a fresh tool and a worn tool. As a result, we proposed the method to diagnose a tool wear by combining these parameters with a neural network algorithm.