Manufacturing Simulation Research Articles

A Hybrid Modelling Approach Coupling Physics-based Simulation and Deep Learning for Battery Electrode Manufacturing Simulations

Lithium-ion battery (LIB) performance is significantly influenced by its manufacturing process. Manufacturing of an optimized electrode can incur high production costs such as high energy consumption, high scrap rates and emissions. This is due to the process that consists of a series of manufacturing steps presenting a complex interrelationship, thus limiting the understanding of performance as a function of manufacturing parameters. While several empirical and computational methods are employed for optimization, they are demanding in terms of resources such as materials or computational effort. By leveraging Deep Learning (DL), we can enhance our understanding of the complex manufacturing processes and accelerate its optimization. We propose a data-driven supervised DL methodology to complement physics-based LIB cathode manufacturing simulations. The trained DL-based predictive model integrates well into the manufacturing simulation framework to forecast cathode slurry microstructures. The DL model demonstrates robust predictive performance for LIB NMC-111 and LiFePO4–based slurries and slurries for a solid-state battery NMC-622/argyrodite composite electrode preparation. While the current work is focused on the cathode slurry process, the proposed methodology has potential for application to drying and calendering steps. This approach will be helpful in streamlining lab-scale electrode manufacturing, and reducing errors, waste and resource consumption.

Additive Manufacturing simulations: An approach based on space partitioning and dynamic 3D mesh adaptation

Additive Manufacturing simulations: An approach based on space partitioning and dynamic 3D mesh adaptation

Research on Integrated Optimization of Design Parameters and Process Simulation for Throughput Efficiency

This thesis presents a comprehensive approach to enhance the production throughput of aluminium bumper beams, a critical component in automotive manufacturing. Using CATIA, an innovative bumper beam design was developed, focusing on simplicity of design and manufacturability. The design parameters considered included material selection, cross-sectional area optimization, and welding techniques to ensure both performance and ease of manufacturing. Transitioning to Tecnomatix Plant Simulation, a virtual environment was constructed to simulate the bumper beam production process. The simulation incorporated the identified design parameters, process routes, layout considerations, and machine arrangements to optimize throughput without inflating costs. By leveraging advanced simulation techniques, the study aimed to identify the most efficient production strategies while maintaining design integrity and minimizing resource consumption. Through systematic scenarios experimentation and analysis, the research yielded insights into the complex relationship between design decisions and manufacturing processes. The results highlight the potential for significant throughput enhancements achievable through collaborative optimization of design and production parameters. The optimization was able to achieve an increase in throughput of about 12.64% of its initial throughput. Moreover, the study underscores the importance of holistic approaches integrating design engineering with manufacturing simulation to drive continuous improvement in industrial operations. This thesis contributes to advancing automotive manufacturing practices by offering a methodology for enhancing production efficiency while preserving product quality and cost-effectiveness. The findings provide valuable guidance for engineers and practitioners seeking to optimize manufacturing processes through the use of simulation and design parameters

QUASIM: Quantum Computing Enhanced Service Ecosystem for Simulation in Manufacturing

AbstractQuantum computing (QC) and machine learning (ML), taken individually or combined into quantum-assisted ML (QML), are ascending computing paradigms whose calculations come with huge potential for speedup, increase in precision, and resource reductions. Likely improvements for numerical simulations in engineering imply the possibility of a strong economic impact on the manufacturing industry. In this project report, we propose a framework for a quantum computing-enhanced service ecosystem for simulation in manufacturing, consisting of various layers ranging from hardware to algorithms to service and organizational layers. In addition, we give insight into the current state of the art of applications research based on QC and QML, both from a scientific and an industrial point of view. We further analyze two high-value use cases with the aim of a quantitative evaluation of these new computing paradigms for industrially relevant settings.

A Chimera method for thermal part-scale metal additive manufacturing simulation

This paper presents a Chimera approach for the thermal problems in welding and metallic Additive Manufacturing (AM). In particular, a moving mesh is attached to the moving heat source while a fixed background mesh covers the rest of the computational domain. The thermal field of the moving mesh is solved in the heat source reference frame. The chosen framework to couple the solutions on both meshes is a non-overlapping Domain Decomposition (DD) with Neumann–Dirichlet transmission conditions.Increased steadiness and accuracy within the vicinity of the Heat Affected Zone (HAZ) are the main advantages of this approach. The steadiness gain allows for the use of larger time steps, which is vital in AM applications and, in particular, Laser Powder Bed Fusion (LPBF), where the disparity of time scales represents a major hurdle. Moreover, enhanced accuracy can be observed in the resulting morphology of the melt pool. It will be shown that the method addresses classical shortcomings pointed out by Goldak without requiring the use of an asymmetrical heat source profile.

Finite element modeling of the glass sealing process for solid oxide cell stacks

The sealing process for solid oxide fuel cell (SOFC) and solid oxide electrolysis cell (SOEC) stacks is a vital step in the assembly and manufacturing of these systems. The cell seal and stack seal serve to prevent leakage and the mixing of air with hydrogen and steam during operation. Good seals are critical for reporting accurate cell and stack performance and durability, as leaks can mask degradations and manifest as performance improvement. Predictive modeling of seal materials during sealing processes for SOFC and SOEC stacks has been largely unexplored and could provide design and manufacturing insights for these systems. In this work, finite element modeling was conducted to simulate the assembly and stack sealing of planar cells to capture the densification, viscous flow, and crystallization behavior of the G18 glass ceramic seal material during full scale stack fabrication. The Skorohod-Olevsky viscous sintering material model was implemented in the ANSYS finite element program and modified to account for crystallization effects to simulate the response of the G18 seal material. The effects of initial cell/stack curvature were captured via preliminary stack manufacturing simulations, and its influence on stack sealing quality was studied. Effects of compressive loading sequence and configuration were investigated.

In situ X-ray diffraction and thermal simulation of material extrusion additive manufacturing of polymer

Material extrusion additive manufacturing (AM) has gradually become a dominant technology for the fabrication of complex-designed thermoplastic polymers that require a higher level of control over the morphological and mechanical properties. The polymer internal crystal structure formed during the AM process can present significant impacts on the mechanical properties of the individual filaments, as well as the whole structure. Currently, limited details are known about the crystal structure evolution during the material extrusion AM processes of polymers. A novel in situ synchrotron X-ray diffraction (XRD) experimental configuration was developed enabling us to capture the material evolution data throughout the extrusion AM process. The in situ time-resolved data was analysed to reveal nucleation and crystallization sequences during the continuous deposition, with the aid of both complimentary numerical simulations and post-process (ex situ) characterisations. The thermal simulations supported the prediction of the filament temperature profile over time and location during the AM process, while ex situ characterisations validated the correlation between polymer crystallinity (resulting from printing parameters) and corresponding mechanical properties. The results obtained from varied process parameters suggest that the processing temperature has a dominant influence on the crystal microstructure evolution compared to the deposition velocity. A lower processing temperature just above the melting temperature permitted favourable crystallization conditions. The overall analysis demonstrated prospects for enhancing polymer AM, to engineering mechanically hierarchical structures through correlative investigations.

A flexible and efficient calibration method for discrete element simulations of additive manufacturing in construction

Extrusion-based 3D concrete printing (E3DCP), as one of the most advanced Additive Manufacturing processes, is a hotspot in the construction industry. Using the Discrete Element Method (DEM), the E3DCP can be investigated with reduced prototyping- and trial costs. However, DEM parameter calibration is challenging in the accurate simulation of construction materials because they are hardly typical granular matters. In addition, the introduction of a novel extrusion-based process Near-Nozzle-Mixing calls for a more flexible, efficient, and reliable calibration procedure due to the useable wide variety of material models.In this study, DEM simulation parameters of mortar and its components (e.g. Poraver® and paste) were thoroughly calibrated based on the central composite design method. Notably, the specific shape indices of related substrates were identified in the study to achieve the precise calibration of Poraver® and paste. Moreover, the European standard-based Haegermann flow table test was applied in the calibration by quantifying mortar consistency. This work provides a holistic and efficient calibration approach for three typical and physically different construction materials, which can be universally explored in the DEM simulation of most materials in E3DCP.

Thermo-Structural Coupled Finite Element Analysis of Repair Process for Steam Turbine Blade Using Laser-Directed Energy Deposition Method

This study presents a numerical additive manufacturing simulation aimed at simulating the shape recovery process of a steam turbine blade damaged by corrosion, using laser-directed energy deposition (LDED). The simulation integrates the finite element (FE) method with heat conduction and thermo-elastoplastic constitutive equations, incorporating phase transformation. The additive manufacturing process by LDED was modeled using the death-birth algorithm, wherein a deposition layer is defined as a virtual element. Its stiffness and thermal properties activated when the laser irradiation regions overlapped. In this study, the shape of the virtual element was determined based on the cross-sectional shape of the deposition layer manufactured under various laser conditions. To validate the numerical simulation results, additive manufacturing was conducted for one pass deposition in the width direction at the center of a cantilever-supported plate made of SUS304 steel, and the changes in displacement at the free edges with respect to the process time were compared. The obtained FE results are in good agreement with the experimental results. Finally, an FE simulation was performed for the shape recovery of a steam turbine blade thinned due to corrosion damage. The results revealed that the residual stress component becomes more compressive as the laser output decreases and scanning speed increases, which is advantageous for improving the fatigue strength of steam turbine blades.

Process-informed simulation of Big-Area Additive Manufacturing (BAAM) of polymers

Process-informed simulation of Big-Area Additive Manufacturing (BAAM) of polymers

Outcomes and Conclusions from the 2022 AM Bench Measurements, Challenge Problems, Modeling Submissions, and Conference

The Additive Manufacturing Benchmark Test Series (AM Bench) provides rigorous measurement data for validating additive manufacturing (AM) simulations for a broad range of AM technologies and material systems. AM Bench includes extensive in situ and ex situ measurements, simulation challenges for the AM modeling community, and a corresponding conference series. In 2022, the second round of AM Bench measurements, challenge problems, and conference were completed, focusing primarily upon laser powder bed fusion (LPBF) processing of metals, and both material extrusion processing and vat photopolymerization of polymers. In all, more than 100 people from 10 National Institute of Standards and Technology (NIST) divisions and 21 additional organizations were directly involved in the AM Bench 2022 measurements, data management, and conference organization. The international AM community submitted 138 sets of blind modeling simulations for comparison with the in situ and ex situ measurements, up from 46 submissions for the first round of AM Bench in 2018. Analysis of these submissions provides valuable insight into current AM modeling capabilities. The AM Bench data are permanently archived and freely accessible online. The AM Bench conference also hosted an embedded workshop on qualification and certification of AM materials and components.

A semi-analytical approach to wire arc additive manufacturing simulation for deposition sequence optimisation

ABSTRACT Categorised as a directed energy deposition process, wire arc additive manufacturing (WAAM) is a promising technology for fabricating large-scale structures and components across various industries. However, the quality of WAAM parts depends on the tool trajectory, as it affects the temperature distribution. Ideally, temperature-related issues, such as overheating, are minimised by choosing an optimal deposition sequence. Selecting the deposition sequence solely on intuition is an unreliable approach, particularly when dealing with complex and irregular parts. This paper introduces a simplified WAAM simulation (SWS) model, which was embedded into an optimisation procedure for determining the optimal deposition sequence of thin-walled WAAM parts. The proposed method is based on a semi-analytical function, which was calibrated and validated using thermal histories obtained from finite element simulations. The findings showed that the proposed SWS model can reproduce the average thermal history at predefined positions within a sample part with minimal computational effort. Moreover, the optimisation successfully identified optimal deposition sequences for producing the sample part regarding a given optimisation criterion. The outcomes of this research contribute to the tool trajectory planning process for WAAM parts. An optimal deposition sequence improves WAAM part quality, increasing the usability of WAAM technology in the production industry.

A highly efficient computational approach for fast scan-resolved microstructure predictions in metal additive manufacturing on the scale of real parts

In metal additive manufacturing (AM), fast and efficient simulation approaches are essential to explore the full potential of these promising processes, particularly in generating components with tailored microstructures via laser powder bed fusion (LPBF). Due to the inherent multiscale nature of LPBF, existing approaches often need to resort to strong simplifications, such as layer-wise heating models, to make part-scale simulations feasible. In contrast, the present article proposes a scan-resolved approach, which consistently resolves the laser scan path in a coupled thermo-microstructural model of LPBF. Building on a high-performance computing model for the thermal problem, we propose a highly efficient implementation of a recently developed microstructure model for Ti-6Al-4V with three main constituents: stable αs-phase, martensitic αm-phase and β-phase. The implementation is tailored to modern hardware features using vectorization and fast approximations of transcendental functions. A performance model and selected numerical examples of LPBF manufacturing of parts on the centimeter scale are studied to verify the high degree of optimization. Depending on the specific example, results were obtained with moderate computational resources in a few hours to days. We demonstrate how the proposed scan-resolved model allows us to predict the correlation between scan strategy and resulting microstructure composition, an aspect that layer-wise heating models cannot capture. The numerical examples include scan-resolved thermo-microstructure simulations of the full NIST AM Benchmark cantilever specimen. It is shown that varying the build plate temperature by only 100K can significantly change the microstructure composition from αm- to αs-dominated.

Fundamental Frequency Layer-Wise Optimization of Tow-Steered Composites Considering Gaps and Overlaps

AbstractThe advent of Automated Fiber Placement (AFP) in aerospace composites lay-up and manufacturing has allowed orientations to vary along pre-defined curved directions rather than being forced to remain constant within the lamina. These composites are called Variable Angle Tow (VAT) or Variable Stiffness Composites (VSC). Despite the enhancements in mechanical performance offered by VAT, constraints from the manufacturing process hinder their full potential. This paper explores the effect of primary defects, i.e., gaps and overlaps, on optimal design and fundamental frequency optimization. For doing so, the Carrera Unified Formulation (CUF) and the Defect Layer Method (DLM) are integrated directly into the optimization process to provide an efficient and cost-effective framework for modeling the structural behavior and manufacturing process of VSCs. Particular attention is given to manufacturing and tow-steering simulation to quantify and map defects for each laminate layer. This research serves a dual purpose: (i) examining the impact of process-induced defects on achieving an optimal design and (ii) exploring how the choice of structural theory may affect the optimal solution.

A life cycle assessment of early-stage enzyme manufacturing simulations from sustainable feedstocks

Enzyme-catalyzed reactions have relatively small environmental footprints. However, enzyme manufacturing significantly impacts the environment through dependence on traditional feedstocks. With the objective of determining the environmental impacts of enzyme production, the sustainability potential of six cradle-to-gate enzyme manufacturing systems focusing on glucose, sea lettuce, acetate, straw, and phototrophic growth, was thoroughly evaluated. Human and ecosystem toxicity categories dominated the overall impacts. Sea lettuce, straw, or phototrophic growth reduces fermentation-based emissions by 51.0, 63.7, and 79.7%, respectively. Substituting glucose-rich media demonstrated great potential to reduce marine eutrophication, land use, and ozone depletion. Replacing organic nitrogen sources with inorganic ones could further lower these impacts. Location-specific differences in electricity result in a 14% and a 27% reduction in the carbon footprint for operation in Denmark compared to the US and China. Low-impact feedstocks can be competitive if they manage to achieve substrate utilization rates and productivity levels of conventional enzyme production processes.

Enabling cross-morphological performance comparison: A case study in heat management design

Cross-morphological performance comparisons in mechanical engineering design may well resemble comparisons of apples and oranges. They are meaningful only if pertinent properties shared across compared morphologies (so-calledtertia comparationis) are identified, and equity with regard to these properties is established. This article demonstrates such an approach with the use of parametric geometry modeling, optimization, and additive manufacturing, enabling a thermal performance comparison of different heat sink morphologies, using both steady-state numerical simulation and experimental evaluation, by establishing equity across the compared heat sinks in terms of (thermal) mass. Limitations in additive manufacturing precision, as well as inconsistencies between numerical simulation and experimental evaluation results, were encountered in this case study. Likely resulting from the heat sinks’ thermal properties on the additive manufacturing process and the simulation environment’s disproportionate reliance on convection surface area, these limitations and inconsistencies will likely diminish as additive manufacturing and numerical simulation technologies improve. The cross-morphological comparison approach presented here is shown to be viable in principle and may inform decision-making in applied mechanical engineering design research and practice.

Simulating multi-material specimen manufacturing from VZh159 and CuCr1Zr alloys via SLM method: Computational modeling and experimental findings

Manufacturing of multi-material products through layer-by-layer synthesis poses various challenges encompassing process parameter optimization, equipment calibration, and the mitigation of warping and internal stresses within the manufactured parts. The article investigates the feasibility of simulating the selective laser melting (SLM) process for manufacturing multi-material components, exemplified through specimens composed of the VZh159 nickel alloy and CuCr1Zr copper alloy. The study entails numerical simulations of the printing process, which were then validated against real specimens produced through SLM. Each test specimen was vertically divided into three parts: the top and bottom sections consisted of the VZh159 alloy, while the central part was composed of the CuCr1Zr alloy. Simulations involved using identical process parameters as employed in the printing process. Thermal and mechanical analyses for each part of the multi-material specimen were sequentially addressed, transferring the outcomes of the preceding analysis as initial conditions for subsequent calculations. The study concludes that while the obtained simulation results are indicative, they do not precisely capture the deformation observed in the specimens manufactured via the SLM method. The numerical values of deformations derived from simulation results slightly underestimate the actual deformations, attributed to limitations in the chosen calculation algorithms. For future utilization of numerical computer simulation in the SLM manufacturing of multi-material specimens, the study suggests the necessity of implementing a seamless, continuous simulation process without transitions between different parts of the specimen. This entails considering the entire manufacturing process without segregating sections, ensuring a comprehensive account of continuous deformation and stress accumulation throughout fabrication.

INCONEL® Alloy Machining and Tool Wear Finite Element Analysis Assessment: An Extended Review

Machining INCONEL® presents significant challenges in predicting its behaviour, and a comprehensive experimental assessment of its machinability is costly and unsustainable. Design of Experiments (DOE) can be conducted non-destructively through Finite Element Analysis (FEA). However, it is crucial to ascertain whether numerical and constitutive models can accurately predict INCONEL® machining. Therefore, a comprehensive review of FEA machining strategies is presented to systematically summarise and analyse the advancements in INCONEL® milling, turning, and drilling simulations through FEA from 2013 to 2023. Additionally, non-conventional manufacturing simulations are addressed. This review highlights the most recent modelling digital solutions, prospects, and limitations that researchers have proposed when tackling INCONEL® FEA machining. The genesis of this paper is owed to articles and books from diverse sources. Conducting simulations of INCONEL® machining through FEA can significantly enhance experimental analyses with the proper choice of damage and failure criteria. This approach not only enables a more precise calibration of parameters but also improves temperature (T) prediction during the machining process, accurate Tool Wear (TW) quantity and typology forecasts, and accurate surface quality assessment by evaluating Surface Roughness (SR) and the surface stress state. Additionally, it aids in making informed choices regarding the potential use of tool coatings.

WITHDRAWN: Modeling and simulation in wooden furniture manufacturing: technologies, scenarios, changes and challenges

In the context of “Made in China 2025” and “Industry 4.0”, the traditional furniture manufacturing industry is undergoing a transformation towards digitization, informatization, and intelligence. In this regard, modeling and simulation (M&S) have been comprehensively applied in wooden furniture manufacturing, effectively enhancing product quality, manufacturing efficiency, and reducing production costs. This paper referenced the steps of a scoping review method, initially presenting guiding questions regarding the application technologies, scenarios, changes, and challenges of M&S in wooden furniture manufacturing. From a preliminary search, 307 out of 4232 papers were selected for analysis, and a further refined selection of 123 papers was chosen for the report. To provide more insightful answers to the questions, a small number of relevant review papers from external sources on other aspects of wooden furniture manufacturing, “Made in China 2025”, and Industry 4.0, were also incorporated. After summarizing and defining the themes of the literature included in the analysis, our analysis concluded that M&S technologies in wooden furniture manufacturing can be categorized into digital modeling technologies, numerical analysis, mathematics and algorithms, and intelligent M&S technologies. The application of M&S in wooden furniture manufacturing scenarios primarily focuses on wooden furniture design M&S and wooden furniture production-oriented M&S. Specifically, M&S in wooden furniture design encompasses wooden furniture digital and intelligent design, wooden furniture three-dimensional modeling and virtual exhibition, wooden furniture structure M&S, as well as assessment and design for the life cycle of wooden furniture. Wooden furniture production-oriented M&S includes wood processing with tools M&S, wooden furniture component parts M&S, wooden furniture manufacturing management M&S, and other production-oriented M&S. Consequently, we propose that the transformation brought about by M&S in wooden furniture manufacturing is not only evident in the application and depth of these technologies but is of greater significance in the transformation of the wooden furniture knowledge system, the workflow in various stages product lifecycle, and the value chain of wooden furniture enterprises. This transformation faces challenges posed by the dynamic furniture market, changing design environments, and variable production environments, which introduce new attributes such as big data, high dynamics, uncertainty, strong coupling, and multi-objectives. In the future, M&S in wooden furniture manufacturing will advance towards increasing technological applications in various wooden furniture manufacturing scenarios, which includes further applications of M&S technologies, consisting of intelligent M&S technologies in the field of wooden furniture manufacturing. This reviewing paper aims to provide methods and insights for the more reasonable and efficient utilization of M&S techniques in wooden furniture and timber product manufacturing. This serves as a reference and guide for the furniture industry’s transition from digital manufacturing towards intelligent manufacturing for higher-quality development.

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