Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

This paper presents an efficient mesoscale simulation of a Laser Powder Bed Fusion (LPBF) process using the Smoothed Particle Hydrodynamics (SPH) method. The efficiency lies in reducing the computational effort via spatial adaptivity, for which a dynamic particle refinement pattern with an optimized neighbor-search algorithm is used. The melt pool dynamics is modeled by resolving the thermal, mechanical, and material fields in a single laser track application. After validating the solver by two benchmark tests where analytical and experimental data are available, we simulate a single-track LPBF process by adopting SPH in multi resolutions. The LPBF simulation results show that the proposed adaptive refinement with and without an optimized neighbor-search approach saves almost 50% and 35% of the SPH calculation time, respectively. This achievement enables several opportunities for parametric studies and running high-resolution models with less computational effort.

Multi-Resolution SPH Simulation of a Laser Powder Bed Fusion Additive Manufacturing Process

Machining simulation of Ti6Al4V using coupled Eulerian-Lagrangian approach and a constitutive model considering the state of stress

Meshfree simulation of metal cutting: an updated Lagrangian approach with dynamic refinement

GPU-accelerated meshfree simulations for parameter identification of a friction model in metal machining

In this work, orthogonal metal cutting simulations of Ti6Al4V are presented for two purposes: the accurate prediction of chip shapes and the automatic optimization of cutting parameters. To achieve these results, a new software tool is presented. This software tool employs meshless methods instead of the established FEM and is parallelized using GPGPU computing. These characteristics allow for a dramatic reduction in the computation time compared to established tools, enabling low-resolution simulations in the orders of minutes, and extremely high-resolution simulations in over night time frames, making the aforementioned goals possible. On the other hand, overnight simulations with very high particle numbers are conducted to study the chip shape. In the interest of reproducibility, the software tool developed is made open source and can be obtained from https://github.com/mroethli/mfree_iwf-ul_cut_gpu
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Metal cutting simulations using smoothed particle hydrodynamics on the GPU

Numerical simulation of metal cutting with rigorous experimental validation is a profitable approach that facilitates process optimization and better productivity. In this work, we apply the Smoothed Particle Hydrodynamics (SPH) and Finite Element Method (FEM) to simulate the chip formation process within a thermo-mechanically coupled framework. A series of cutting experiments on two widely-used workpiece materials, i.e., AISI 1045 steel and Ti6Al4V titanium alloy, is conducted for validation purposes. Furthermore, we present a novel technique to measure the rake face temperature without manipulating the chip flow within the experimental framework, which offers a new quality of the experimental validation of thermal loads in orthogonal metal cutting. All material parameters and friction coefficients are identified in-situ, proposing new values for temperature-dependent and velocity-dependent friction coefficients of AISI 1045 and Ti6Al4V under the cutting conditions. Simulation results show that the choice of friction coefficient has a higher impact on SPH forces than FEM. Average errors of force prediction for SPH and FEM were in the range of 33% and 23%, respectively. Except for the rake face temperature of Ti6Al4V, both SPH and FEM provide accurate predictions of thermal loads with 5–20% error.

A Numerical-Experimental Study on Orthogonal Cutting of AISI 1045 Steel and Ti6Al4V Alloy: SPH and FEM Modeling with Newly Identified Friction Coefficients

Physically sound prediction of grinding results necessarily starts with the single grain interaction with the material, which is especially important for engineered grinding tools (EGT). The deformations encountered in single grain simulations are severe, especially due to the large negative rake angles. Conventional software tools using FEM suffer from the induced extreme distortions of the grid, necessitating costly remeshing operations. Meshless methods are not limited in the amount of deformation they can reproduce and thus are a promising alternative, which also has the potential for extreme parallelisation on the graphics co-processor (GPU). With the drastically increased computational speed unprecedented simulation resolutions can be achieved at reasonable computation times allowing parameter studies on different friction and constitutive models with first order complete meshless formulations. In the interest of reproducibility, the software tool developed is made open source and can be obtained from https://github.com/mroethli/iwf_mfree_gpu_3d.

Meshless single grain cutting simulations on the GPU

This article presents a novel coupling of three state-of-the-art particle methods, capable of simulating heat transfer in multiphase flows with minimum error. The idea originates from increasing the robustness of the geometric features required to calculate physical quantities, such as the boundary conditions in the heat equation and the surface tension force in Navier-Stokes equations. The workability of this new approach for handling thermal effects in complex moving interfaces is examined with a numerical test case wherein multiple scenarios such as internal flow, bursting at free surface as well as bubble evolutionary motions occur. Performing a benchmark comparison with the reference model implemented in COMSOL Multiphysics®, a markedly improved mass conservation using the proposed particle-based solver is observed. This, in turn, results in a more realistic and accurate approximation of the temperature.

Matthias Röthlin

Papers

Metal cutting simulations using smoothed particle hydrodynamics on the GPU

GPU-accelerated meshfree simulations for parameter identification of a friction model in metal machining

A Numerical-Experimental Study on Orthogonal Cutting of AISI 1045 Steel and Ti6Al4V Alloy: SPH and FEM Modeling with Newly Identified Friction Coefficients

Meshless single grain cutting simulations on the GPU

A Robust Particle-Based Solver for Modeling Heat Transfer in Multiphase Flows