Metrology

Wire and arc additive manufacturing is a promising technology for fabricating large and complex metallic components. Wire arc methods, like MIG and MAG, use an electric arc to melt and deposit metal wire layer-by-layer. The improvement of the surface depends on the multi-bead overlapping model. However, the high quality of multi-layer deposits is reduced by structural irregularities, such as geometric defects, poor fusion, and reduced mechanical properties of the weld bead. The analysis of a single weld bead that solidifies on a base material can be carried out to improve the geometry of the microstructure, to improve the mechanical properties, and to understand the relationship between welding parameters and the bead dimensions. In the present study, current metal welding technologies and strategies in wire-arc additive manufacturing were discussed, and different weld bead geometries using BÖHLER SG2 solid wire were realized, varying the robot’s trajectory length and welding speed. The computational models are proposed to create a dependence between the controllable welding input parameters and resulting geometrical weld bead outputs (width, height, length, and radius) for prediction and optimization. These models, using techniques such as support vector machines and artificial neural networks, can be a good tool for controlling quality by understanding these input–output relationships. However, the SVM has revealed a superior performance based on metrics for the nonlinear and intricate relationships between the geometrical weld beads and welding parameters.

Quantum metrology uses the principles of quantum mechanics to improve the accuracy of parameter estimation so that it can surpass the classical limit. However, noise and the challenge of preparing multipartite entangled states hinder practical applications. In this work, we use the Lipkin-Meshkov-Glick model as the experimental platform and the quantum parameter estimation package QuanEstimation as a tool to improve the quantum parameter estimation in many-body systems by using Hamiltonian control optimization. We apply auto-GRAPE, PSO, and DE algorithm to optimize the time-dependent control field. Our results show that the optimal control strategy can significantly enhance the quantum Fisher information and reduce the quantum Cramér-Rao bound even under environmental noise. These findings provide a way to achieve the parameter estimation limit in a noisy environment and promote the development of practical quantum metrology applications.

Measurement models that have a chemical composition as one of the arguments require special attention when used with the law of propagation of uncertainty from the Guide to the expression of uncertainty in measurement. The constraint that the amount fractions in a composition add exactly to unity not only affects the covariance matrix associated with the composition, but also impacts the differentiation of the measurement model to obtain the expressions and values of the sensitivity coefficients. Differentiating the measurement model with respect to each variable individually is not possible as it involves evaluating the model for infeasible inputs, leading to an undefined output. In this work, a numerical method for constrained partial differentiation is presented, enabling the use of the law of propagation of uncertainty for measurement models with compositions as one of their arguments. The numerical method enables treating the measurement model as a black box and using it with measurement models in the form of an algorithm. The numerical method is demonstrated by showing how the uncertainty associated with composition, temperature and pressure can be propagated through an equation of state, in this case, the GERG-2008 equation of state. It is shown that this differentiation can be completed in a few simple steps, requiring only a valid implementation of the measurement model that provides an output value for given input quantities. The numerical differentiation method applies in principle to all differentiable functions of a composition.