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Laser processing of solid materials has been commonly performed in gas ambient. Having the workpiece immersed into liquid, having a liquid film on it, or soaking the material with liquid gives several advantages such as removal of the debris, lowering the heat load on the workpiece, and confining the vapour and plasma, resulting in higher shock pressure on the surface. Introduced in the 1980s, neutral liquids assisted laser processing (LALP) has proved to be advantageous in the cutting of heat-sensitive materials, shock peening of machine parts, cleaning of surfaces, fabrication of micro-o

Lasers --- Laser peening. --- Industrial applications. --- Laser shock peening --- Laser shock processing --- LSP (Laser shock peening) --- Shock peening, Laser --- Shock processing, Laser --- Metallurgy --- Peening (Metal-work) --- Laser use in

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This book introduces the fundamentals and principles of laser shock peening (LSP) for aeronautical materials. It focuses on the innovation in both theory and method related to LSP-induced gradient structures in titanium alloys and Ni-based alloys which have been commonly used in aircraft industries. The main contents of the book include: the characteristics of laser shock wave, the formation mechanism of gradient structures and the strengthening-toughing mechanism by gradient structures. The research has accumulated a large amount of experimental data, which has proven the significant effectiveness of LSP on the improvement of the fatigue performance of metal parts, and related findings have been successfully applied in aerospace field. This book could be used by the researchers who work in the field of LSP, mechanical strength, machine manufacturing and surface engineering, as well as who major in laser shock wave and materials science.

Structural materials. --- Chemical engineering. --- Lasers. --- Photonics. --- Aerospace engineering. --- Astronautics. --- Structural Materials. --- Industrial Chemistry/Chemical Engineering. --- Optics, Lasers, Photonics, Optical Devices. --- Aerospace Technology and Astronautics. --- Space sciences --- Aeronautics --- Astrodynamics --- Space flight --- Space vehicles --- Aeronautical engineering --- Astronautics --- Engineering --- New optics --- Optics --- Light amplification by stimulated emission of radiation --- Masers, Optical --- Optical masers --- Light amplifiers --- Light sources --- Optoelectronic devices --- Nonlinear optics --- Optical parametric oscillators --- Chemistry, Industrial --- Engineering, Chemical --- Industrial chemistry --- Chemistry, Technical --- Metallurgy --- Architectural materials --- Architecture --- Building --- Building supplies --- Buildings --- Construction materials --- Structural materials --- Materials --- Laser peening. --- Airplanes --- Titanium alloys. --- Materials. --- Alloys --- Laser shock peening --- Laser shock processing --- LSP (Laser shock peening) --- Shock peening, Laser --- Shock processing, Laser --- Peening (Metal-work) --- Laser use in

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Laser shock processing (LSP) is a continuously developing effective technology used to improve surface and mechanical properties for metallic alloys. LSP is in direct competition with other established technologies, such as shot peening, both in preventive manufacturing treatments and maintenance/repair operations. The level of LSP maturity has increased in recent years and several thematic international conferences have been organized (i.e., the 7th ICLPRP held in Singapore, June 17–22, 2018) to discuss different developments of a number of key aspects. These aspects include: fundamental laser interaction phenomena; material behavior at high deformation rates/under intense shock waves; laser sources and experimental process implementation; induced microstructural/surface/stress effects; mechanical and surface properties with experimental characterization and testing; numerical process simulation; development and validation of applications; comparison of LSP to competing technologies; and novel related processes. All of these aspects have been recursively treated by well-renowned specialists, providing a firm basis for the further development of the technology in its path to industrial penetration. However, the application of LSP (and related technologies) on different types of materials with different applications (such as the always demanding aeronautical/aerospatial field or the energy generation, automotive, and biomedical fields) still requires extensive effort to elucidate and master different critical aspects. Thus, LSP deserves a great research effort as a necessary step prior to its industrial readiness level. The present Special Issue of Metals in the field of “Laser Shock Processing and Related Phenomena” aims, from its initial launching date, to collect (especially for the use of LSP application developers in different target sectors) a number of high-quality and relevant papers representing state-of-the-art technology that is useful to newcomers in realizing its wide and relevant prospects as a key manufacturing technology. Consequently, in an additional and complementary way, papers were presented at the thematic ICLPRP conferences, and a call was made to authors willing to prepare high-quality and relevant papers to the journal, with the confidence that their work would become part of a fundamental reference collection regarding the present state-of-the-art LSP technology. The Special Issue includes two reviews and nine research papers. Each contribution adds to the reference knowledge of LSP technology and covers the practical totality of open issues, which will lead to present-day research at worldwide universities, research centers, and industrial companies.

History of engineering & technology --- laser peening --- fatigue --- residual stress --- laser shock waves --- laser peening history --- laser shock peening --- residual stresses --- shot pattern --- energy density --- overlap --- hole drilling --- AA 2024 --- cladded aluminum --- notch --- aluminium alloys --- finite element method --- polymers --- solid confinement --- VISAR measurement --- laser shock processing --- plasma diagnosis --- electron density --- dry laser peening --- femtosecond laser --- shock wave --- laser welding --- 2024 aluminum alloy --- high-velocity impact welding --- laser impact welding --- finite element simulation --- experimental analysis --- microhardness --- resonant fatigue resistance --- roughness --- aluminum alloys --- handheld laser --- nuclear power reactor --- stress corrosion cracking --- anisotropy --- FEM analysis --- Mg AZ31B alloy --- n/a

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Third millennium engineering address new challenges in materials sciences and engineering. In particular, the advances in materials engineering combined with the advances in data acquisition, processing and mining as well as artificial intelligence allow for new ways of thinking in designing new materials and products. Additionally, this gives rise to new paradigms in bridging raw material data and processing to the induced properties and performance. This present topical issue is a compilation of contributions on novel ideas and concepts, addressing several key challenges using data and artificial intelligence, such as:- proposing new techniques for data generation and data mining;- proposing new techniques for visualizing, classifying, modeling, extracting knowledge, explaining and certifying data and data-driven models;- processing data to create data-driven models from scratch when other models are absent, too complex or too poor for making valuable predictions;- processing data to enhance existing physic-based models to improve the quality of the prediction capabilities and, at the same time, to enable data to be smarter; and- processing data to create data-driven enrichment of existing models when physics-based models exhibit limits within a hybrid paradigm.

plasticity --- machine learning --- constitutive modeling --- manifold learning --- topological data analysis --- GENERIC --- soft living tissues --- hyperelasticity --- computational modeling --- data-driven mechanics --- TDA --- Code2Vect --- nonlinear regression --- effective properties --- microstructures --- model calibration --- sensitivity analysis --- elasto-visco-plasticity --- Gaussian process --- high-throughput experimentation --- additive manufacturing --- Ti–Mn alloys --- spherical indentation --- statistical analysis --- Gaussian process regression --- nanoporous metals --- open-pore foams --- FE-beam model --- data mining --- mechanical properties --- hardness --- principal component analysis --- structure–property relationship --- microcompression --- nanoindentation --- analytical model --- finite element model --- artificial neural networks --- model correction --- feature engineering --- physics based --- data driven --- laser shock peening --- residual stresses --- data-driven --- multiscale --- nonlinear --- stochastics --- neural networks --- n/a --- Ti-Mn alloys --- structure-property relationship

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Third millennium engineering address new challenges in materials sciences and engineering. In particular, the advances in materials engineering combined with the advances in data acquisition, processing and mining as well as artificial intelligence allow for new ways of thinking in designing new materials and products. Additionally, this gives rise to new paradigms in bridging raw material data and processing to the induced properties and performance. This present topical issue is a compilation of contributions on novel ideas and concepts, addressing several key challenges using data and artificial intelligence, such as:- proposing new techniques for data generation and data mining;- proposing new techniques for visualizing, classifying, modeling, extracting knowledge, explaining and certifying data and data-driven models;- processing data to create data-driven models from scratch when other models are absent, too complex or too poor for making valuable predictions;- processing data to enhance existing physic-based models to improve the quality of the prediction capabilities and, at the same time, to enable data to be smarter; and- processing data to create data-driven enrichment of existing models when physics-based models exhibit limits within a hybrid paradigm.

Technology: general issues --- plasticity --- machine learning --- constitutive modeling --- manifold learning --- topological data analysis --- GENERIC --- soft living tissues --- hyperelasticity --- computational modeling --- data-driven mechanics --- TDA --- Code2Vect --- nonlinear regression --- effective properties --- microstructures --- model calibration --- sensitivity analysis --- elasto-visco-plasticity --- Gaussian process --- high-throughput experimentation --- additive manufacturing --- Ti-Mn alloys --- spherical indentation --- statistical analysis --- Gaussian process regression --- nanoporous metals --- open-pore foams --- FE-beam model --- data mining --- mechanical properties --- hardness --- principal component analysis --- structure-property relationship --- microcompression --- nanoindentation --- analytical model --- finite element model --- artificial neural networks --- model correction --- feature engineering --- physics based --- data driven --- laser shock peening --- residual stresses --- data-driven --- multiscale --- nonlinear --- stochastics --- neural networks --- plasticity --- machine learning --- constitutive modeling --- manifold learning --- topological data analysis --- GENERIC --- soft living tissues --- hyperelasticity --- computational modeling --- data-driven mechanics --- TDA --- Code2Vect --- nonlinear regression --- effective properties --- microstructures --- model calibration --- sensitivity analysis --- elasto-visco-plasticity --- Gaussian process --- high-throughput experimentation --- additive manufacturing --- Ti-Mn alloys --- spherical indentation --- statistical analysis --- Gaussian process regression --- nanoporous metals --- open-pore foams --- FE-beam model --- data mining --- mechanical properties --- hardness --- principal component analysis --- structure-property relationship --- microcompression --- nanoindentation --- analytical model --- finite element model --- artificial neural networks --- model correction --- feature engineering --- physics based --- data driven --- laser shock peening --- residual stresses --- data-driven --- multiscale --- nonlinear --- stochastics --- neural networks