Photopolymerization processes are exploited in light exposure-based 3D printing technologies, where either a focused laser beam or a patterned light sheet allows layers of a UV curable, liquid pre-polymer to be solidified. Here we focus on the crucial, though often neglected, role of the layer thickness on photopolymerization. The temporal evolution of polymerization reactions occurring in droplets of acrylate-based oligomers and in photoresist films with varied thickness is investigated by means of an optical system, which is specifically designed for in -situ and real-time monitoring. The time needed for complete curing is found to increase as the polymerization volume is decreased below a characteristic threshold that depends on the specific reaction pathway. This behavior is rationalized by modeling the process through a size-dependent polymerization rate. Our study highlights that the formation of photopolymerized networks might be affected by the involved volumes regardless of the specific curing mechanisms, which could play a crucial role in optimizing photocuring-based additive manufacturing.

This paper presents an experimental study where a vision camera integrates coaxially into a laser beam welding tool to monitor beam deviations (beam offset) in laser stake welding of T-joints. The aim is to obtain an early detection of deviations from the joint centreline in this type of welding where the joint is not visible from the top side. A polynomial surface fitting method is applied to extract features that can describe the behaviour of the melt pool. A nonlinear autoregressive with exogenous inputs neural network model is trained to relate eight image features to the laser beam offset. The performance of the presented model is evaluated offline by different welding samples. The results show that the proposed method can be used to guide post weld inspection and has the potential for on-line adaptive control.

Vibrations onset represents a paramount issue in all grinding processes. The related surface defects appear in the form of micrometric waviness that decreases the finishing quality and in some cases the functionality of the ground workpieces: sometimes, these defects can be also marked on the grinding wheel surface. This paper presents an online model-based approach to identify and quantify the level of waviness starting from multiple acceleration measurements, allowing a continuous monitoring of wheel and/or workpiece defects. The identification algorithm, that exploits a linear model of machine and process dynamics, is based on the application of Least Squares method in the frequency domain. Experiments confirm the good performance of the algorithm that, hence, can be exploited for developing advanced control schemes of the grinding process.

In-process monitoring and feedback control are fundamental actions for stable and good quality laser welding process. In particular, penetration depth is one of the most critical features to be monitored. In this research, overlap welding of stainless steel is investigated to stably reproduce a fixed penetration depth using both CO 2 and Nd:YAG lasers. Plasma electron temperatures of Fe(I) and Cr(I) are evaluated as in process monitoring using the measurement of intensities of emission lines with fast spectrometers. The sensor system is calibrated using a quantitative relationship between electron temperature and penetration depth in different welding conditions. Finally closed loop control of the weld penetration depth is implemented by acquiring the electron temperature value and by adjusting the laser power to maintain a pre-set penetration depth. A PI controller is successfully used to stabilize the electron temperature around the set point corresponding to the right penetration depth starting from a wrong value of any initial laser power different than the set point. Optical inspection of the weld surface and macroscopic analyses of cross sections verify the results obtained with the proposed closed-loop system based on a spectroscopic controller and confirms the reliability of our system.

The in-process monitoring and real-time control of the penetration depth during laser welding is evaluated. An optical collimator collects the optical emission for measurement with a fast spectrometer. The sensor data are used to calculate the electron temperature and subsequently to determine the weld quality of overlap welds in AISI 304 stainless steel sheets performed both with CW Nd:YAG and CO2 lasers. A PI-controller adjusts the laser power aiming at a constant penetration depth and has been tested for Nd:YAG laser welding. Optical inspection of the weld verifies the results obtained with the proposed closed-loop system of spectroscopic sensor and controller.