Andrey V. Filippov

Bio: Andrey V. Filippov is an academic researcher from Institute of Strength Physics and Materials Science SB RAS. The author has contributed to research in topics: Acoustic emission & Materials science. The author has an hindex of 13, co-authored 73 publications receiving 497 citations. Previous affiliations of Andrey V. Filippov include Tomsk Polytechnic University.

Effect of heat input on phase content, crystalline lattice parameter, and residual strain in wire-feed electron beam additive manufactured 304 stainless steel

Abstract: Structural and mechanical characterization of electron beam additive manufactured stainless steel samples has been carried out The XRD measured austenite and ferrite lattice parameters showed their sensitivity to the heat input value, which was related to the chromium atom redistribution The ferrite content depended on the heat input too Optimal heat input level has been detected, which allowed obtaining the tensile strength higher than that of the base stainless steel Residual strain levels in the as-deposited metal and fusion line zone have been measured using the X-ray sin2ψ method The highest tensile residual strain was determined in a fusion line zone between the first as-deposited layer and a substrate The microstructure of the first fusion line zone contained deformation twins and entangled dislocations generated by plastic flow under thermal expansion-contraction cycles

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Metallurgy, mechanistic models and machine learning in metal printing

Abstract: Additive manufacturing enables the printing of metallic parts, such as customized implants for patients, durable single-crystal parts for use in harsh environments, and the printing of parts with site-specific chemical compositions and properties from 3D designs. However, the selection of alloys, printing processes and process variables results in an exceptional diversity of microstructures, properties and defects that affect the serviceability of the printed parts. Control of these attributes using the rich knowledge base of metallurgy remains a challenge because of the complexity of the printing process. Transforming 3D designs created in the virtual world into high-quality products in the physical world needs a new methodology not commonly used in traditional manufacturing. Rapidly developing powerful digital tools such as mechanistic models and machine learning, when combined with the knowledge base of metallurgy, have the potential to shape the future of metal printing. Starting from product design to process planning and process monitoring and control, these tools can help improve microstructure and properties, mitigate defects, automate part inspection and accelerate part qualification. Here, we examine advances in metal printing focusing on metallurgy, as well as the use of mechanistic models and machine learning and the role they play in the expansion of the additive manufacturing of metals. Several key industries routinely use metal printing to make complex parts that are difficult to produce by conventional manufacturing. Here, we show that a synergistic combination of metallurgy, mechanistic models and machine learning is driving the continued growth of metal printing.

Directed Energy Deposition (DED) Process: State of the Art

Abstract: Metal additive manufacturing technologies, such as powder bed fusion process, directed energy deposition (DED) process, sheet lamination process, etc., are one of promising flexible manufacturing technologies due to direct fabrication characteristics of a metallic freeform with a three-dimensional shape from computer aided design data. DED processes can create an arbitrary shape on even and uneven substrates through line-by-line deposition of a metallic material. Theses DED processes can easily fabricate a heterogeneous material with desired properties and characteristics via successive and simultaneous depositions of different materials. In addition, a hybrid process combining DED with different manufacturing processes can be conveniently developed. Hence, researches on the DED processes have been steadily increased in recent years. This paper reviewed recent research trends of DED processes and their applications. Principles, key technologies and the state-of-the art related to the development of process and system, the optimization of deposition conditions and the application of DED process were discussed. Finally, future research issues and opportunities of the DED process were identified.

The effect of vibration and cutting zone temperature on surface roughness and tool wear in eco-friendly MQL turning of AISI D2

Abstract: Today, developments in technology have gained momentum more than ever, and the need for efficiency in production as well as in the ecological domain has increased significantly. Studies examining dry machining and coolant removal have been superseded by those presenting new cooling and lubrication techniques. The effects on surface roughness directly related to final product quality are being investigated in terms of tool life and employee health. This has resulted in more frequent use of the eco-friendly minimum quantity lubrication (MQL) technique, which has now become a major competitor to dry and coolant machining. In this study, AISI D2 cold work tool steel, a material widely used in the mold industry, was used as the workpiece. Tests were carried out under dry and MQL conditions and the temperature, cutting tool vibration amplitude, tool wear, surface roughness and tool life were evaluated. The experiments were carried out using two different cutting tool coating types (CVD-chemical vapor deposition and PVD-physical vapor deposition) and three different cutting speeds (60, 90 and 120 m/min) at a constant cutting depth (1 mm) and feed rate (0.09 mm/rev). Results revealed that tool wear, cutting temperature and cutting tool vibration amplitude were lower by 23, 25, and 45%, respectively, compared to dry cutting. Because of these improvements, the surface roughness of the workpiece was improved by 89% and tool life was increased by up to 267%.