No abstract

No abstract

Machine learning tools represent key enablers for empowering material scientists and engineers to accelerate the development of novel materials, processes and techniques. One of the aims of using such approaches in the field of materials science is to achieve high-throughput identification and quantification of essential features along the process-structure-property-performance chain. In this contribution, machine learning and statistical learning approaches are reviewed in terms of their successful application to specific problems in the field of continuum materials mechanics. They are categorized with respect to their type of task designated to be either descriptive, predictive or prescriptive; thus to ultimately achieve identification, prediction or even optimization of essential characteristics. The respective choice of the most appropriate machine learning approach highly depends on the specific use-case, type of material, kind of data involved, spatial and temporal scales, formats, and desired knowledge gain as well as affordable computational costs. Different examples are reviewed involving case-by-case dependent application of different types of artificial neural networks and other data-driven approaches such as support vector machines, decision trees and random forests as well as Bayesian learning, and model order reduction procedures such as principal component analysis, among others. These techniques are applied to accelerate the identification of material parameters or salient features for materials characterization, to support rapid design and optimization of novel materials or manufacturing methods, to improve and correct complex measurement devices, or to better understand and predict fatigue behavior, among other examples. Besides experimentally obtained datasets, numerous studies draw required information from simulation-based data mining. Altogether, it is shown that experiment-and simulation-based data mining in combination with machine leaning tools provide exceptional opportunities to enable highly reliant Bock et al. Machine Learning in Materials Mechanics identification of fundamental interrelations within materials for characterization and optimization in a scale-bridging manner. Potentials of further utilizing applied machine learning in materials science and empowering significant acceleration of knowledge output are pointed out.

In the present study, the feasibility of the Friction Spot Joining technique on magnesium AZ31-O / glass fiber and carbon fiber reinforced poly(phenylene sulfide) joints is addressed. The thermo-mechanical phenomena associated with the Friction Spot Joining process promoted metallurgical and polymer physical-chemical transformations. These effects resulted in grain refinement by dynamic recrystallization and changes in local (microhardness) and global strength (lap shear). Friction spot lap joints with elevated mechanical performance (20-28 MPa) were produced without surface pre-treatment. This preliminary investigation has successfully shown that Friction Spot Joining is an alternative technology for producing hybrid polymer-metal structures.