Manufacturing Simulation Research Articles

Validation of a Digital Simulation Model for Maintenance in a High-Volume Automated Manufacturing Facility

Digitalization in manufacturing is the conversion of information into digital format, the integration of this digital data and technologies into the manufacturing process and the use of those technologies (eg: simulation) to change a business model to provide new revenue and value-producing opportunities. Digitalization may be seen as the increased generation, analysis, and use of data to improve the efficiency of the overall manufacturing system. Simulation in manufacturing is often applied in situations where conducting experiments on a real system is impossible or very difficult due to cost or time to carry out the experiment is too long. A key input to the simulation model of automated equipment is the acquisition of valid data in relation to cycle time and reliability of various workstations on this line. As a consequence of being able to simulate equipment processes and interact with this validated simulation model, both the understanding of how the production system will perform under varying reliability and cycle time conditions is achieved. The simulation model then enables the experimentation of ‘what if scenarios’ that can be tested easily, while also providing a valuable tool to inform the maintenance personnel what station reliabilities they need to target in order to sustain a high performing manufacturing line.The author has adopted an open source simulation tool (JaamSim) to develop a digital model of an automated production line in a Johnson & Johnson Vision Care (JJVC) manufacturing facility. This research demonstrated how a digital model was validated for use. The validated digital model can then be used by the author/facility engineering teams to perform scenario testing during the design stage of the line. This simulation model can also be used as a subset of an optimization system to determine recommended optimum line parameters to maximize line performance, either during the line design or when line is in operation.

Concurrent Modeling of Porosity and Microstructure in Multilayer Three-Dimensional Simulations of Powder-Bed Fusion Additive Manufacturing of Inconel 718

Concurrent Modeling of Porosity and Microstructure in Multilayer Three-Dimensional Simulations of Powder-Bed Fusion Additive Manufacturing of Inconel 718

Hybrid Training on Modelling and Simulation for Additive Manufacturing Using Online-Learning-Nuggets

Hybrid Training on Modelling and Simulation for Additive Manufacturing Using Online-Learning-Nuggets

Integration of Annealing into the Inherent Strain Simulation of Wire Arc Additive Manufacturing

Integration of Annealing into the Inherent Strain Simulation of Wire Arc Additive Manufacturing

Numerical Simulation of Soot Trapping Process and Manufacturing of Three-Way Catalyzed Membrane Filter

Numerical Simulation of Soot Trapping Process and Manufacturing of Three-Way Catalyzed Membrane Filter

Simulation of wire arc additive manufacturing to find out the optimal path planning strategy

Wire Arc Additive Manufacturing is a metal 3-D printing technology that is a highly versatile and rapidly growing means of producing complex components by depositing metal layer-by-layer, melting them with an electric arc. Predicting the residual stress induced is of cardinal importance as components need to be analyzed during the design phase with considerations given to residual stresses to be effective. One of the critical parameters determining the residual stress of the structure in the process is the deposition pattern. In this study, finite element method in Simufact Welding 6.0 is employed to compare the different path-planning deposition strategies of the Wire Arc Additive Manufacturing process to understand how the dynamic temperature evolution can cause notable differences in distortion and residual stresses. The results show that the strategy that has the least chance of non-uniform cooling will have the least residual stresses, and the paths in which fewer longitudinal beads are exposed to the edges will tend to distort the least.

Technoeconomic Modeling and Simulation for Plant-Based Manufacturing of Recombinant Proteins.

Technoeconomic modeling and simulation is a critical step in defining a manufacturing process for evaluation of commercial viability and to focus experimental process research and development efforts. Technoeconomic analysis (TEA) is increasingly demanded alongside scientific innovation by both public and private funding agencies to maximize efficiency of resource allocation. It is particularly important for plant-based manufacturing, and other nontraditional recombinant protein production platforms, to explicitly demonstrate the manufacturing potential and to identify critical technical and economic challenges through robust technoeconomic analysis. In addition, in silico process modeling and TEA of scaled biomanufacturing facilities allows rapid evaluation of the impacts of process and economic changes on capital expenditures (CAPEX, also sometimes referred to as total capital investment), operational expenditures (OPEX, also known as total manufacturing costs or total production costs), cost of goods sold (COGS, also known as unit production costs), and profitability metrics such as net present value (NPV) and discounted cash flow rate of return (DCROR, also known as internal rate of return or IRR). These models can also be used to assess environmental, health, and safety impact of a designed biomanufacturing facility to evaluate its sustainability and environmental-friendliness. Here we describe a general method for performing technoeconomic modeling and simulation for and environmental assessment of plant-based manufacturing of recombinant proteins.

Using a process simulation platform for reviewing automated airport baggage handling system configurations

The use of digital manufacturing platforms is becoming increasingly important since they facilitate process visualisation, optimisation, and validation, allowing engineers to make informed decisions at early product and process development phases. In this paper, the potential of using process simulation platforms is explored in order to visualise and validate baggage handling processes involving robotic handling systems and human operators. Multiple baggage handling layout configurations are digitally constructed and reviewed with the aim to investigate cost, time, and flexibility performance indicators. Furthermore, the advantages of using digital manufacturing and process simulation platforms are discussed.

Integration of Lean Manufacturing and Promodel Simulation on Repair Production Process Flow of Polysilane Bottle Printing Using VSM, WAM, VALSAT, And RCA Methods: Case Study Packaging Manufacturing Company

In the production process, waste is found, which causes a decrease in the efficiency and effectiveness of the production process. The obstacle faced is not achieving the monthly production target from the target set by the company. This study aims to minimize waste or activities that do not provide added value by visualizing the ProModel simulation. The first step of the research is to identify the production process and waste using Operation Process Chart (OPC), Value Stream Mapping (VSM), and Waste Assessment Model (WAM). Based on WAM, it is known that there are 3 critical wastes, namely Transportation 13.5%, motion 13.01%, and waiting for 10.7%. Next is waste mapping with Value Stream Analysis Tools (VALSAT) using Process Activity Mapping (PAM). Based on PAM, it is known that there are 9 Value Added (VA) operations for 210 seconds and 4 Necessary but Non-Value Added (NNVA) transportation activities for 194 seconds. Then the root cause analysis was carried out using the 5 Whys Root Cause Analysis (RCA) and analysis of the design recommendations for improvement using the RCA 5W 1H tool. Based on the 5W 1H tool, it is known that there are 2 recommended improvement activities. That is by combining the machining process and changing the layout of the machine to eliminate waste. The last is the simulation of the repair results using the Promodel software. Based on the Promodel simulation with a runtime of 1440 minutes, it is known that 2,621 pcs of polysilane finish good bottles exit.

Result quality evaluation of Directed Energy Deposition Additive Manufacturing simulations with progressive simplification of transient heat-source motion

Directed Energy Deposition (DED) additive manufacturing has recently been adopted in the industry for the build-up of structural components with weld lengths up to kilometers. As with all welding processes, DED suffers from thermal distortion, causing loss of dimensional accuracy and risk of cracking. Currently, process optimization with objective to minimize distortion requires expensive experimental trial-and-error.With numerical simulation of the DED process, this distortion compensation can be performed virtually, significantly reducing experimental trials. Although such approaches are generally available, their widespread adoption is currently being hampered by long computational times for large builds. This work presents a novel approach to reduce the calculation time by a simplification of the transient heat-source motion. This approach is assessed in terms of result accuracy for an industrial-scale component by progressively reducing the resolution of the heat-source motion. Calculation times as well as distortions in comparison to experimental trials are investigated.

ExaAM: Metal additive manufacturing simulation at the fidelity of the microstructure

Additive manufacturing (AM), or 3D printing, of metals is transforming the fabrication of components, in part by dramatically expanding the design space, allowing optimization of shape and topology. However, although the physical processes involved in AM are similar to those of welding, a field with decades of experimental, modeling, simulation, and characterization experience, qualification of AM parts remains a challenge. The availability of exascale computational systems, particularly when combined with data-driven approaches such as machine learning, enables topology and shape optimization as well as accelerated qualification by providing process-aware, locally accurate microstructure and mechanical property models. We describe the physics components comprising the Exascale Additive Manufacturing simulation environment and report progress using highly resolved melt pool simulations to inform part-scale finite element thermomechanics simulations, drive microstructure evolution, and determine constitutive mechanical property relationships based on those microstructures using polycrystal plasticity. We report on implementation of these components for exascale computing architectures, as well as the multi-stage simulation workflow that provides a unique high-fidelity model of process–structure–property relationships for AM parts. In addition, we discuss verification and validation through collaboration with efforts such as AM-Bench, a set of benchmark test problems under development by a team led by the National Institute of Standards and Technology.

Gpams: A G-Code Processor for Advanced Additive Manufacturing Simulations

Gpams: A G-Code Processor for Advanced Additive Manufacturing Simulations

Laser Remelting Process Simulation and Optimization for Additive Manufacturing of Nickel-Based Super Alloys.

Nickel-based super alloys are popular for applications in the energy and aerospace industries due to their excellent corrosion and high-temperature resistance. Direct metal deposition (DMD) of nickel alloys has reached technology readiness for several applications, especially for the repair of turbomachinery components. However, issues related to part quality and defect formation during the DMD process still persist. Laser remelting can effectively prevent and repair defects during metal additive manufacturing (AM); however, very few studies have focused on numerical modeling and experimental process parameter optimization in this context. Therefore, the aim of this study is to investigate the effect of determining the remelting process parameters via numerical simulation and experimental analyses in order to optimize an industrial process chain for part repair by DMD. A heat conduction model analyzed 360 different process conditions, and the predicted melt geometry was compared with observations from a fluid flow model and experimental single tracks for selected reference conditions. Subsequently, the remelting process was applied to a demonstrator repair case. The results show that the models can well predict the melt pool shape and that the optimized remelting process increases the bonding quality between base and DMD materials. Therefore, DMD part fabrication and repair processes can benefit from the remelting step developed here.

SIMULASI NUMERIK KENDALI KAPAL CEPAT TAK BERAWAK DUA PROPELLER TANPA RUDDER

This paper discusses the simulation of automation control and hull manufacturing on a fast boat. This high-speed boat that will be carried out for fish mapping. This study uses a numerical simulation to predict its motion. The experiment was carried out 3 times in forward motion, and 3 times in turn motion. The ship’s time and movement in ideal conditions will be recorded for each experiment. With the PID value for forward overshoot with values such as: 8.94%, 30.86%, and 40.60%, while turning overshoot is obtained, among others: 25.77%, 49.82%, and 61.15% Then the advanced settling time obtained includes: 0.91 seconds, 3.84 seconds, and 5.31 seconds. The settling time for turns is: 1,022 seconds, 0.85 seconds, and 0.84 seconds These values are obtained from the simulation graph of the three-dof formulas for surge, sway, yaw.

Implementation of a viscoplastic substrate creep model in the thermomechanical simulation of the WAAM process

This research work focusses on the implementation of a viscoplastic creep model in the thermomechanical simulation of the wire arc additive manufacturing (WAAM) process for Ti-6Al-4 V structures. Due to the characteristic layer by layer manufacturing within the WAAM process, viscoplastic material effects occur, which can be covered by implementing a creep model in the thermomechanical simulation. Experimental creep tests with a wide temperature, load and time range were carried out to examine short-term creep behaviour in particular. A Norton-Bailey creep law is used to accurately fit the experimental data and describe the base material’s creep behaviour. Subsequently, the fitted Norton-Bailey creep law was implemented in the thermomechanical simulation of the WAAM process. Finally, to determine the effect of creep on global distortion and local residual stress state in the substrate, simulations of a simplified linear, three-layer WAAM structure, with and without applying the implemented creep law, were carried out and compared to experimental data. The thermomechanical simulation with implemented creep model reveals a significant improvement in the numerical estimation of distortion and residual stress state in the substrate. The maximum distortion is reduced by about 13% and respectively the mean absolute percentage error between simulation and experiment decreases by about 34%. Additionally, the estimation accuracy with respect to the local residual stress state in the substrate improved by about 10%.

Multiscale simulation of rapid solidification of an aluminium–silicon alloy under additive manufacturing conditions

At present, most multiscale simulation approaches to model the temperature evolution of the molten pool, and the resulting microstructure evolution for selective laser melting, assume an equilibrium freezing range and steady state solidification conditions. This is despite the solidification conditions being observed to be highly unsteady and non-equilibrium. These two assumptions lead to inaccurate predictions of the temperature evolution of the molten pool and thus microstructure predictions. To demonstrate this, an approach to scale-bridging computational models of the laser additive manufacturing process is presented, in which the temperature history is passed from a macroscale molten pool simulation to a microscale phase-field simulation. This linkage is achieved by volume mapping of the temperature field from the grid of the molten pool simulation to the grid of the microstructure simulation. To describe the system evolution at the scale of the molten pool, a computational fluid dynamics (CFD) method that captures the laser–metal interaction, vapour production, gas recoil pressure, fluid flow, surface tension, Marangoni flow, and heat conduction, convection, and radiation is applied. To capture the chemical kinetics of the phase-transition, a non-equilibrium CALPHAD-integrated phase-field (PF) model is applied. The discrepancy between the predictions of the solid front isotherm is quantified as ⩾ 100 K for an Al-10Si alloy under the large observed cooling rate. This leads to a spatial discrepancy in the solidification front between the CFD model, which assumes equilibrium freezing behaviour, and the PF model, which does not, of approximately 10 µm over 50 µs in the present case. Under these conditions, present formulations of multiphase CFD cannot accurately predict the solidification behaviour because of the assumption of equilibrium at the solid–liquid interface. Strategies for reconciling this discrepancy for materials that exhibit rapid solidification under large thermal undercooling will need to be developed for multiscale simulation of additive manufacturing to advance.

DIE SHEET HYDROFORMING OF A COMPLEX-SHAPED AA2024-W AIRCRAFT SKIN PANEL — FROM CONCEPT TO FINAL COMPONENT

Product design in the early stages requires a more quantitative process to ensure that the simulation study's outcome will meet expectations. The purpose of this study was to determine whether a manufacturing simulation software package could effectively evaluate and validate the conceptualised blank and tooling to manufacture a sizeable complex- shaped aluminium alloy aircraft panel using die sheet hydroforming. Blank and tool concepts were conceptualised, evaluated, and validated digitally using the forming process conceptualisation cycle. Without the need to trial several blank and tool concepts physically, a significant saving was achieved using a resource-efficient development process. Even without modelling the hyperelastic rubber elements, a successful die sheet hydroformed simulation result was conducted. However, the study showed that a rubber-to-metal friction coefficient must be incorporated appropriately. The study further showed the lack of standard metrology methods in commercial simulation packages to evaluate a simulation result accurately. Simulation metrology is a new area of interest that requires further development. This research contributes to improving the sheet metal forming simulation process and, specifically, die sheet hydroforming process simulation. The study will benefit the aerospace and automotive industry and promote the adoption of digital manufacturing processes.

A System Modelling Approach Optimization Process and Machine Utilization In Casting Plant Using Lean Manufacturing Simulation Model

Perbaikan berkelanjutan, reduksi dan eliminasi waste dalam proses bisnis menjadi salah satu aspek yang dilakukan agar dapat terus memiliki daya saing yang sustainable. PT. X saat ini sedang dalam proses melakukan transformasi, reduksi dan eliminasi NVA serta perbaikan berkelanjutan di sisi proses guna meningkatkan daya saing yang sustainable. penelitian ini bertujuan dalam menganalisis dan mengajukan formulasi perbaikan proses menggunakan metode kerangka kerja pemodelan sistem dan simulasi. Temuan dari penelitian diketahui bahwa peningkatan Utilisasi Dies rata-rata 82,82 % relative meningkat 36% dibanding simulasi sebelumnya (52,9%); dengan rata-rata output 21,04 pcs/jam relative meningkat 42% dibanding simulasi sebelumnya (12,9 pcs/jam), hal ini dengan melakukan improvement proses semi auto dalam proses eject produk yang sekaligus berdampak dalam pengurangan manpower, serta mereduksi loss time akibat lama proses pendinginan dengan sistem heat transfer conveyor system

CycleStyleGAN-Based Knowledge Transfer for a Machining Digital Twin.

Digitalisation of manufacturing is a crucial component of the Industry 4.0 transformation. The digital twin is an important tool for enabling real-time digital access to precise information about physical systems and for supporting process optimisation via the translation of the associated big data into actionable insights. Although a variety of frameworks and conceptual models addressing the requirements and advantages of digital twins has been suggested in the academic literature, their implementation has received less attention. The work presented in this paper aims to make a proposition that considers the novel challenges introduced for data analysis in the presence of heterogeneous and dynamic cyber-physical systems in Industry 4.0. The proposed approach defines a digital twin simulation tool that captures the dynamics of a machining vibration signal from a source model and adapts them to a given target environment. This constitutes a flexible approach to knowledge extraction from the existing manufacturing simulation models, as information from both physics-based and data-driven solutions can be elicited this way. Therefore, an opportunity to reuse the costly established systems is made available to the manufacturing businesses, and the paper presents a process optimisation framework for such use case. The proposed approach is implemented as a domain adaptation algorithm based on the generative adversarial network model. The novel CycleStyleGAN architecture extends the CycleGAN model with a style-based signal encoding. The implemented model is validated in an experimental scenario that aims to replicate a real-world manufacturing knowledge transfer problem. The experiment shows that the transferred information enables the reduction of the required target domain data by one order of magnitude.

Machine Learning Boosted Data-driven Modeling and Simulation of Additive Manufacturing: Process, Structure and Property

Machine Learning Boosted Data-driven Modeling and Simulation of Additive Manufacturing: Process, Structure and Property