Simulation Of Manufacturing Processes Research Articles

A thermodynamically consistent phase transformation model for multiphase alloys: application to Ti$$_6$$Al$$_4$$V in laser powder bed fusion processes

A thermodynamically consistent phase transformation model for multiphase alloys: application to Ti$$_6$$Al$$_4$$V in laser powder bed fusion processes

QuadWire: An extended one dimensional model for efficient mechanical simulations of bead-based additive manufacturing processes

This paper presents the basis of a new mechanical model named QuadWire dedicated to efficient simulations of bead-based additive manufacturing processes in which elongated beads undergoing significant cooling and eigenstrain are assembled to form 3D parts. The key contribution is to use a multi-particular approach containing 4 particles per material point to develop an extended 1D model capable of capturing complex 3D mechanical states, while significantly reducing computation time with respect to conventional approaches. Indeed, 3D models usually require at least 3 to 4 elements across the bead section, which results in fine discretization along the tangential direction to avoid conditioning issues, and therefore very fine mesh of the entire 3D part. In the QuadWire model, the bead height and thickness are internal dimensions, enabling a significantly coarser mesh along the tangential direction. Thus, although the QuadWire has 12 degrees of freedom per material point instead of 3 for classical models, the total number of degrees of freedom is reduced by several orders of magnitude for large parts. The proposed model is classically developed within the framework of the principle of virtual power and standard generalized hyperelastic media (i.e., finite strain theory), which necessitates a thermodynamic analysis. Furthermore, the proposed approach includes native and manageable kinematic constraints between successive beads so that the stress state properly evolves during fabrication. Finite element analysis is used for numerical implementation under infinitesimal strain assumption for the sake of simplicity, and the QuadWire stiffness parameters are optimized so that the mechanical response fit conventional 3D approaches. To validate and demonstrate the capabilities of the proposed strategy, the evolution of displacements and stresses in fused deposition modeling of polylactide is simulated.

Computational welding simulation of a plasma wire arc additive manufacturing process for high-strength steel

Computational welding simulation of a plasma wire arc additive manufacturing process for high-strength steel

(Invited) Optimizing the Binder Distribution in Battery Electrodes with Manufacturing Process Simulations and Machine Learning

The performance of lithium-ion battery cells strongly depends on the microstructure of the electrodes, which is determined by the spatial distribution of the active material, carbon additive, binder, and pores within the electrode volume. The manufacturing process determines this spatial distribution, which, in turn, determines the interfaces between these materials and the pores, thereby impacting the practical properties of the electrodes, such as their electrical conductivity, energy density, and wettability. The binder plays a crucial role in this process, as its spatial distribution controls the degree of percolation between the particles and the adhesion of the electrode with the current collector.Since 2017, we have been developing the ARTISTIC computational platform,1 which constitutes a digital twin of the battery electrode and cell manufacturing process. Supported by a combination of physics-based models and machine learning, this platform has been demonstrated by us for lithium-ion, sodium-ion, and solid-state batteries with electrodes produced by wet processing.2 The platform simulates each step of the manufacturing process, including mixing, coating/drying, calendering, electrolyte infiltration, and resulting cell performance. It allows for predicting 3D-resolved electrode and cell sandwich microstructures with their associated electrochemical performance as a function of the manufacturing parameters.In this talk, I will discuss how the ARTISTIC computational platform can optimize the spatial distribution of active material, carbon additive, and binder within the electrode volume. Using Bayesian Optimization, the platform predicts the manufacturing parameters required to obtain optimal electrodes in terms of several properties, such as electric conductivity, tortuosity factor, electroactive surface area, and energy density. I will particularly discuss the platform's capabilities to capture the adhesion of the electrode with the current collector through the binder. Additionally, I will present an extension of this digital twin for the 3D-resolved simulation of the dry processing of active material, carbon additive, and binder based on extrusion and compare the results (in the form of 3D-resolved electrode microstructures) with experimental data. Results from the wet and dry processing simulations will be discussed for electrodes made with different active material chemistries, such as NMC and LFP, and the crucial role of the binder in the heterogeneity of lithiation/delithiation of the electrodes upon their electrochemical cycling will be explored.Finally, building on our previously reported virtual reality digital tools for battery manufacturing,3 I will present a novel virtual reality interface that allows for the interactive and immersive use of the ARTISTIC computational platform to track the influence of manufacturing parameters on the active material, carbon additive, and binder's spatial location, as well as the resulting electrode properties. I will conclude by discussing why this virtual interface is also a powerful tool to assist in the electrode manufacturing optimization.

A highly efficient computational approach for fast scan-resolved simulations of metal additive manufacturing processes on the scale of real parts

This article proposes a novel high-performance computing approach for the prediction of the temperature field in powder bed fusion (PBF) additive manufacturing (AM) processes. In contrast to many existing approaches to part-scale simulations, the underlying computational model consistently resolves physical scan tracks without additional heat source scaling, agglomeration strategies or any other heuristic modeling assumptions. A growing, adaptively refined mesh accurately captures all details of the laser beam motion. Critically, the fine spatial resolution required for resolved scan tracks in combination with the high scan velocities underlying these processes mandates the use of comparatively small time steps to resolve the underlying physics. Explicit time integration schemes are well-suited for this setting, while unconditionally stable implicit time integration schemes are employed for the interlayer cool down phase governed by significantly larger time scales. These two schemes are combined and implemented in an efficient fast operator evaluation framework providing significant performance gains and optimization opportunities. The capabilities of the novel framework are demonstrated through realistic AM examples on the centimeter scale including the first scan-resolved simulation of the entire NIST AM Benchmark cantilever specimen, with a computation time of less than one day. Apart from physical insights gained through these simulation examples, also numerical aspects are thoroughly studied on basis of weak and strong parallel scaling tests. As potential applications, the proposed thermal PBF simulation approach can serve as a basis for microstructure and thermo-mechanical predictions on the part-scale, but also to assess the influence of scan pattern and part geometry on melt pool shape and temperature, which are important indicators for well-known process instabilities.

A compressible multiphase Mass-of-Fluid model for the simulation of laser-based manufacturing processes

A new model for compressible multiphase flows involving sharp interfaces and phase change is presented, that uses a variant of the Volume-of-Fluid model to track phases by advecting their respective mass, and is hence called Mass-of-Fluid model. The framework is aimed at predicting the coupled multi-physical phenomena involved in most processes encountered in laser material processing, with the aim of minimizing a priori assumptions on the nature of the process. Emphasis is put on the multiphase fluid flow model, especially on the treatment of compressibility and phase change. The model’s accuracy and suitability is demonstrated on problems of increasing complexity, including well-known benchmarks and problems encountered in laser material processing, where simulation results are compared to experimental observations.

Incorporating Manufacturing Process Simulations to Enhance Performance Predictions of Injection Moulded Metamaterials

Incorporating Manufacturing Process Simulations to Enhance Performance Predictions of Injection Moulded Metamaterials

The inherent strain method for simulation of additive manufacturing–A critical assessment based on a new variant of the method

AbstractTo reduce computational time of thermo‐mechanical simulation of additive manufacturing processes, the inherent strain (IS) method is quite efficient, but suffers from a lack of predictivity, preventing concurrent quantitative prediction of distortion and stress. In order to investigate the predictive ability of the IS method, a new variant of the method is proposed in which the determination of the IS tensor is based on simulation results from a standard thermo‐elastic‐viscoplastic simulation applied to a few learning layers of the studied part. More precisely, the inherent strains are determined by a direct resolution performed in each finite element, with a negligible computation cost. Because of these specific features, the predictive ability of this IS method is formulated as its ability to replicate the distortion and stress predicted by the reference standard thermo‐elastic‐viscoplastic simulation. The proposed IS method provides perfect results in a validation test where full‐field inherent strains are used in each added layer: the distortion and stress predicted by the thermo‐elastic‐viscoplastic simulation are exactly replicated by the IS method. However, when applying the IS method to the simulation of an entire part, here a turbine blade mock‐up, the results are degraded with respect to the reference solution obtained by the thermo‐elastic‐viscoplastic simulation. Results are then discussed to evaluate the limitations of the proposed method, and of the IS method in general.

Strategies for automatic constitutive model selection and recommendation

The efficient development of high-quality metal products requires realistic numerical simulations prior to manufacturing. The choice of the constitutive model significantly influences material behaviour prediction and manufacturing process simulation. Numerous models exist to describe mechanical phenomena, making model selection challenging and prone to errors. This leads to costly delays in manufacturing. To address this issue, an automated material constitutive model selection and recommendation tool is essential. This research aims to develop a systematic strategy in three steps: analysis of variance (ANOVA), identifiability analysis, and identification quality. The approach is validated through simulations using a known material, including a hole expansion test and two strain heterogeneous tests for calibration. Step one establishes a ranking for model importance, aiding decision-making and accurate parameter calibration. Step two excludes models with inadequate identifiability and sensitivity measures. Step three confirms the methodology by showing improvements in calibration.

Development of combined hardening model for spring-back simulation of DP600 in multi-stage sheet metal forming

High strength steel DP600 is widely used in automobile industry due to its excellent mechanical property compared to conventional steel, but spring-back issue is considered to be the major challenge of DP600 in forming process, as the spring-back is triggered by the release of complicated internal stress. In multi-stage sheet metal forming, the sheet may be subject to several cycles of loading and reverse loading, Bauschinger effect greatly influence the strain stress behaviour. A well designed material model can be used to accurately predict and compensate spring-back in forming process. A combined hardening model especially designed for multi-stage forming is proposed in this paper, and the iterated compensation method could be applied by using LS-DYNA to get an optimized result. An experimental testing facility was developed for calibration of this model with cyclic tension and compression strain stress curve. The compensation method is applied in the numerical simulation of a multi-stage manufacturing process of an A-pillar, the result demonstrates the feasibility of the combined hardening model.

Enhanced heat source modeling in particle-based laser manufacturing simulations with ray tracing

Particle-based spatial discretization methods such as smoothed particle hydrodynamics (SPH) possess suitability for the high-fidelity simulation of laser manufacturing processes due to their capability in treating complex free-surface and multiphase problems. In such simulations, laser heat source modeling remains a central concern as the mechanisms governing the material deformations and phase changes strongly depend on the input laser energy and its distribution details. Ray tracing (RT) is a realistic and accurate approach for high-fidelity heat source modeling that can capture surface heterogeneity and multiple reflections. However, RT heat source modeling for particle-based numerical methods faces additional challenges rooted in the lack of explicit surface representation, intensive computational efforts, and implementation complexities. This work addresses these issues by proposing an enhanced and efficient RT heat source model for particle-based laser manufacturing simulations with negligible computational overhead and without requiring background nodes or surface mesh reconstruction. After validating the RT heat source model versus analytical and experimental results, we assess its computational performance through two test cases, showing that the computing workload imposed by our RT model is between 0.1% and 25% of the total runtime in different SPH-based laser manufacturing simulations. With the achieved efficiency and robustness, it becomes convenient yet advantageous to simulate processes like laser drilling and laser powder bed fusion with precision far beyond the conventional approaches using volumetric heat sources.

Inducing Fiber Entanglement to Achieve Realistic Tow Fiber Volume Fractions in Textile Reinforced Composite Models

Meso-scale modeling can be an effective way to predict textile-reinforced composite performance when the geometry of the textile is known. Digital element fiber models can be used to obtain the geometry of the textile reinforcement, but these are frequently too compact because the fibers are parallel within a tow and do not provide as much resistance to compaction as tows with fiber entanglement found in textiles. A novel entanglement simulation procedure was created to obtain more realistic textile geometry. By combining artificial fiber entanglement with manufacturing process simulation, a method was developed to create fiber bundle models using entanglement to control the compaction behavior. To introduce fiber entanglement into a tow, select fibers are swapped with each other in the tow at a cross-section. This allows entanglement to be introduced after the basic textile structure is already made, and prevents individual fibers from leaving the tow and getting entangled into neighboring tows. This fiber entanglement process was controlled by three parameters, which dictate how many, how often, and how far away from each other fibers are swapped. A parametric study was conducted which showed that the entanglement within a fiber bundle could be used to control the compaction pressure versus fiber volume fraction response of the bundle. The method was then applied to a woven textile model where it was found that increasing the amount of entanglement within the tows increased the pressure required to compact the textile to the desired thickness. This method for artificial fiber entanglement and manufacturing process simulation shows the potential to be able to predict the entanglement required to create fiber bundles using a desired mold compression pressure to achieve a desired compaction behavior.

On the possibility of doing reduced order, thermo-fluid modelling of laser powder bed fusion (L-PBF) – Assessment of the importance of recoil pressure and surface tension

Meso-scale, multi-physics simulations of metal additive manufacturing (MAM) processes have so far proven their capability as a reliable tool for predicting potential defect formations. Nevertheless, there is a large number of uncertainty contributions involved with the input process parameters as well as the implemented material properties in these models. As expected, both the process-related and material-related uncertainties affect the outcome of these multi-physics simulations to a large degree. The present work is therefore intended to quantify the impacts of some of the important material/process-related uncertainties involved with meso-scale multi-physics models, on the heat transfer conditions within melt pool. In this respect, a meso-scale multi-physics model of the laser powder bed fusion process of stainless steel 316-L is developed in the commercial Finite Volume Method (FVM) based software Flow-3D and then validated against in-house experiments prior to the main investigation. In the first part of the study, the impact of recoil pressure at different laser linear energy densities (LED) and different laser beam sizes on the melt pool morphology are investigated. It is found that there is a specific threshold of LED below which the melt pool shape is not affected by the recoil pressure and the melt pool fluid dynamics is mostly governed by the Marangoni effect. This threshold increases from 80 J/mm to 280 J/mm when the beam size is increased from 20 μm to 120 μm. Moreover, a parametric study using dimensionless numbers is carried out to understand the impact of different capillary forces on the melt pool shape and size. It is observed that for inverse Bond numbers below 4.105, the depth-to-width ratio of the melt pool is above 1 where the recoil pressure dominates the melt pool dynamics and a keyhole forms. In summary, this study specifies in essence the process window over which specific physics are unimportant so that a lower-fidelity meso-scale model could replace the higher-fidelity multi-physics models.

Use of production process simulation as a verification tool

This article deals with the use of manufacturing process simulation as a verification tool. Simulation proves to be a valuable tool for testing and evaluating production processes and their effectiveness in a controlled virtual environment. The authors discuss the advantages that simulation brings, such as the identification of defects, the optimization of production parameters, and the prevention of problems even before the start of the real production process. The use of simulation of the production process enables the acquisition of valuable knowledge about the influence of various factors on the final product quality, the optimization of production procedures, and the minimization of the risk of errors. The authors emphasize that the simulation provides accurate information that helps in the decision-making process and allows the prediction of possible problems and their solutions before they arise.

Finite element simulation of additive manufacturing process of carbon allotropes

Finite element simulation of additive manufacturing process of carbon allotropes

FE Simulation of Truck Tire Manufacturing Process

Pneumatic Tires are one of the most important components in automobiles whether in aviation, 2/3/4 wheels drive or industrial applications. The tire is a complex assembly of numerous products which are assembled on a drum and then cured in a press under heat and pressure. Michelin being one of the premium tire manufacturing company, emphasize on use of latest technology at every stage of Tire manufacturing. The complex tire manufacturing process, from design conception till final assembly is currently simulated at Michelin using inhouse CAD/FEA simulation software. Use of FEA tools in manufacturing process simulation allows engineer to iterate over different mixes and evolving designs. In current scope, pre-defined FE template is developed for Michelin to simulate physical truck tire manufacturing process to allows user to perform automated FE iterations on iterative designs, materials etc improving the result output and increasing the productivity of user. Truck tire manufacturing process from green tire to cured tire is simulated as large displacement, 2d axisymmetric FE analysis with viscous or viscoelastic materials. Results of FE simulation are compared with that of Tire architect post manufacturing to marks effectiveness of FE analysis.

Modeling and simulation of the furniture manufacturing and assembly process in the arena simulation software

The article describes issues related to creating discrete simulation models for the implementation of the furniture manufacturing and assembly process in a furniture company. The methodology of the manufacturing system analysis was presented, which is aimed to support the appropriate approach to the construction of simulation models. The scope of the work includes the technological identification of the furniture manufacturing and assembly process in real production conditions, on the basis of which the appropriate simulation model was built. The course of the process was analyzed in the Arena software on the basis of a computer simulation based on reports. As a result of the simulation of the manufacturing process with the use of information from report of usage, an area for improvement was located. The re-analysis of the material flow made it possible to propose a change in the input parameters for the simulation model in the indicated area. The results of the second simulation show significant changes in the effective use of workstations and increase in the efficiency of the production line. In practice, it can be the basis for introducing simulated changes in the production system.

Desarrollo de formulaciones de PUR más sostenibles para aplicaciones estructurales

The industry decarbonization has become in a fundamental challenge for the industries such as automotive or wind power. In this way, the structural components lightening is one of the principal issues for material manufacturers. For this objective, the composites are a competitive alternative, due to their unique relation between their strength and weight. Among the different structural composite part fabrication processes, liquid composite moulding (LCM) is one of the most interesting, due to its efficiency in terms of costs, time and energy consumption. In this sense, resins with fast cure and low viscosity are gaining attention, among them polyurethanes are of particular interest. This family of polymers is characterized by its great versatility, among them high-performance thermosetting polyurethanes (PUR). The advantages offered by PUR-based technology are great fatigue resistance, low viscosity and fast cure. However, the commonly used PUR’s for structural applications have some environmentally drawbacks, such as their components petrochemical origin and their low recyclability. For this reason, the aim of this work was the development of a more sustainable PUR formulation with the required mechanical properties for structural applications and suitable processability for LCM processes. Because of that, this work has focused on the analysis, synthesis and characterization of new PUR formulations and also the manufacturing process simulation and optimization.

An efficient and high-fidelity local multi-mesh finite volume method for heat transfer and fluid flow problems in metal additive manufacturing

The multi-scale and nonlinear feature of additive manufacturing problems poses great challenges to numerical algorithms regarding computational efficiency and fidelity. In this work, we propose a novel local multi-mesh finite volume method for heat transfer and fluid flow problems in the additive manufacturing process. In this method, two sets of meshes (coarse base mesh and finer overlay mesh) are used to resolve the macroscopic temperature field of the whole domain and the mesoscopic heat transfer and fluid flow problem within a relatively small region covering the melt pool, respectively. The interaction between the base mesh and the overlay mesh is handled by the Dirichlet boundary conditions on the interface following the refinement-by-superposition idea, such that the powder-scale simulation is available with high efficiency. The finer mesh with cells of sub-powder size uses the arbitrary Lagrangian–Eulerian description and moves with the heat source to keep coverage of the region with high solution gradients. According to the moving feature of the finer mesh, a particle-volume fraction conversion scheme is introduced to update the material distribution. The local multi-mesh finite volume method is further combined with the discrete element method as well as a surface reconstruction method to alternatively solve the powder layering process and the laser melting process. A set of numerical cases is presented, and the numerical results are compared with analytical and experimental data from the literature, with good agreement in cases where such data is available. Moreover, the computational efficiency of the proposed method is proved to be dozens of times (16∼50 for the given size ratios of the melt pool to the whole domain) of the finite volume method using a uniform structured mesh. It is expected to be a powerful tool for efficiently achieving powder-scale simulation of the additive manufacturing process and other similar multi-scale problems.

(Digital Presentation) 3D Electrochemical Model Revealing Electrode Manufacturing Parameters’ Effects on Battery Performance

Electrode manufacturing is in the core of Li-ion battery manufacturing process. The electrode mesostructure and the associated electrochemical performance is determined by the adopted manufacturing parameters. However, in view of the strong interdependencies between these parameters, evaluating their influence on the performance is not a trivial task, and our ERC-funded ARTISTIC project1,2 is providing a theoretical framework to rationalize and optimize the manufacturing process of Li-ion battery cells. Over the years, the discussion of importance of electrode mesostructure in cell behavior has become increasingly lively. Many modeling researchers have been conducted studies to explain the influences of electrode mesostructure on its electrochemical performance3–6. However, many studies did not include manufacturing parameters as inputs, which hinders universality in regards of these electrochemical models’ application. With the aim of going into the scope of real electrode manufacturing parameters, we present here a calibrated 3D-resolved electrochemical model for NMC111-electrodes which reveals how slurry formulation and compression rate affect the electrode performance. A series of electrodes with different formulations and compression rates were manufactured. Corresponding models were built based on the basis of these manufacturing parameters, using the manufacturing process simulation workflow previously developed in ARTISTIC7. The results of simulations and experiments were compared individually. We found that one of the major factors affecting the electrode performance is the carbon-binder domain (CBD) distribution, which highly changes the conductive network within the electrode. A well-connected conductive network throughout the electrode is vital for ensuring full utilization of active material. Other features such as porosity, tortuosity factor, active material particle packing, and etc. also play important roles in different ways. This work provides systematic insights on the essence of how electrode manufacturing process takes effect on electrode performance, through a digital twin experimentally validated under multiple experimental conditions.References Artistic http://www.erc-artistic.eu/. Artistic http://www.erc-artistic.eu/scientific-production/publications.X. Lu et al., Energy Environ. Sci., 14, 5929–5946 (2021).M. E. Ferraro, B. L. Trembacki, V. E. Brunini, D. R. Noble, and S. A. Roberts, J. Electrochem. Soc., 167, 013543 (2020).X. Lu et al., Nat. Commun., 11, 2079 (2020).L. S. Kremer et al., Energy Technol., 8, 1900167 (2020).T. Lombardo et al., Batter. Supercaps, 5, e202100324 (2022).