Machining processes, including turning, are a critical capability for discrete part production. One limitation to high material removal rates and reduced cost in these processes is chatter, or unstable spindle speed-chip width combinations that exhibit a self-excited vibration. In this paper, an artificial neural network (ANN)—a data learning model—is applied to model turning stability. The novel approach is to use a physics-based process model—the analytical stability limit—to generate a (synthetic) data set that trains the ANN. This enables the process physics to be combined with data learning in a hybrid approach. As anticipated, it is observed that the number and distribution of training points influences the ability of the ANN model to capture the smaller, more closely spaced lobes that occur at lower spindle speeds. Overall, the ANN is successful (>90% accuracy) at predicting the stability behavior after appropriate training.

In selective laser melting (SLM) and selective laser sintering (SLS) techniques for manufacturing, the powder spreading process plays a key role in the quality of the manufactured parts. Some of the important powder processing parameters that influence the quality of the powder bed are the powder particle size distribution, spreader-type (roller or blade), spreader speed,

The material removal rates during milling operations are affected by the selection of the cutting depth and spindle speed. Poor selection of these parameters can result in chatter or suboptimal material removal rates. Stability Lobe Diagrams (SLDs) are the well-known approach to selecting appropriate chatter-free values for these parameters. The Physics-based stability lobe diagram is usually generated using the structural dynamics and the cutting parameters. However, since the machine dynamics are measured in the static state of the machine (zero speed), the generated SLD is not reliable as the machine behavior may vary during the cutting operations. Besides, measuring structural dynamics parameters under cutting conditions is difficult and needs new equipment. This study proposes a new approach to determining in-process structural dynamics parameters based on a multivariate Newton-Raphson method. The physics-based model is combined with empirical records to extract reliable structural dynamics parameters inversely. Some examples based on synthetic data are presented to illustrate this inverse approach. Also, the performance of the algorithm is evaluated on an empirical data set and its ability to improve the stability boundary is verified. Furthermore, the sensitivity analysis is performed to quantify the exposure of the SLD to the changes in each structural dynamics parameter.

Smoothed Particle Hydrodynamics (SPH), a particle-based, meshless method originally developed in the 1970s for modeling astrophysical problems, is being increasingly used for modeling fluid mechanics and solid mechanics problems. The method has several advantages over grid-based methods including better handling of large deformations and the availability of an intrinsic material

In selective laser melting (SLM) and selective laser sintering (SLS) techniques for manufacturing, the powder spreading process plays a key role in the quality of the manufactured parts. Some of the important powder processing parameters that influence the quality of the powder bed are the powder particle size distribution, spreader-type (roller or blade), spreader speed,