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This paper presents a new modeling framework for tool wear monitoring in machining processes using hidden Markov models (HMMs). Feature vectors are extracted from vibration signals measured during turning. A codebook is designed and used for vector quantization to convert the feature vectors into a symbol sequence for the hidden Markov model. A series of experiments are conducted to evaluate the effectiveness of the approach for different lengths of training data and observation sequence. Experimental results show that successful tool state detection rates as high as 97% can be achieved by using this approach.

Theoretical relationships have been drawn between acoustic emission (AE) and the metal cutting process parameters by relating the energy content of the AE signal to the plastic work of deformation which generates the emission signals. The RMS value of the emission signal is expressed in terms of the basic cutting parameters. Results are presented for 6061-T6 aluminum and SAE 1018 steel over the range of speeds 25.2 to 372 sfm (0.128 to 1.9 m/s) and rake angles 10 to 40 deg. Good correlation has been found between predicted and experimental signal energy levels. In addition, AE generation from chip contact along the tool face is studied and the AE energy level reflects the existence of chip sticking and sliding on the tool face, and indicates the feasibility of utilizing AE in tool wear sensing.

A numerical model combining the methods of enthalpy, effective-viscosity and volume-of-fluid is developed to simulate the metal transfer process in gas metal arc welding. The model describes not only the influence on droplet profile and transfer frequency of electromagnetic force, surface tension, and gravity, but it can also model the nonisothermal phenomena such as heat transfer and phase change. The model has been used to study the shape of the melting interface on the welding wire, the droplet oscillation at wire tip, the characteristics of relevant physical variables and their roles in metal transfer. We find that the taper formation in spray transfer is closely related to the heat input on the unmelted portion of the welding wire, and the taper formation affects the globular-spray transition by decelerating the transfer process. The formation of satellite drops during the metal transfer process is also considered. High-speed photography, laser-shadow imaging, and metallographic analysis validate the numerical model, and recommendations are made on the topics that require further consideration for a more accurate metal transfer model.

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A new concept is introduced to model the main effect of tool wear on system dynamics during stable cutting. Audible sound generated from the cutting process is analyzed as a source for monitoring tool wear during turning, assuming adhesive wear as the predominant wear mechanism. The analysis incorporates the dynamics of the cutting process. In modeling the interaction on the flank surface, the asperities on the surfaces are represented as a trapezoidal series function with normal distribution. The effect of changing asperity height, size, spacing, and the stiffness of the asperity interaction is investigated and compared with experimental data.

Ultrasonic welding is a solid-state bond created using ultrasonic energy. It has been used In the semiconductor industry for several decades, and more recently, in the automotive industry such as for lithium-ion battery welding. Although there existed numerical simulations for ultrasonic welding, the models were limited to two-layer and like materials stackups. In this study, finite element theories are introduced and simulation procedure is established for multiple sheets and dissimilar metal ultrasonic welding. The procedures require both ABAQUSIStandard and ABAQUSIExplicit to simulate the coupled mechanicalthermal phenomena over the entire weld duration with moderate computational cost. The procedure is verified and used to simulate selected specific cases involving multiple sheets and dissimilar materials, i.e., copper and aluminum. The simulation procedure demonstrates its capability to predict welding energy, distortion, and temperature distribution of the workpieces. Case studies of ultrasonic welding simulations for multiple layers of lithium-ion battery tabs are presented. The prediction leads to several innovative ultrasonic welding process designs for improved welding quality.

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