Manufacturing Simulation Research Articles

Development of a digital model and metamodel to improve the performance of an automated manufacturing line

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. Simulation metamodeling is an approach to line design which is of great interest to design engineers and research experts. However, its application in automated medical devices manufacturing line design has never been well explored. The author has adopted an open-source simulation tool (JaamSim) to develop a digital model of an automated medical devices manufacturing line in the Johnson & Johnson Vision Care (JJVC) manufacturing facility. This paper demonstrates with a high level of rigour, fidelity and overall system design/approach, how a digital model along with the use of a metamodel can be used for the development of an automated manufacturing line in the medical devices industry. The digital model and metamodel can be used by manufacturing engineering teams to perform scenario testing during the design and development phase of the line or as part of the continuous improvement stage when the line is in full operation. The overall average absolute error when comparing the simulation model outputs to the metamodel outputs was 0.87% was achieved with the metamodel for the actual industrial application used by the author.

Correction to: Simulation of spiral bevel gear manufacturing by face hobbing and prediction of the cutting forces using a novel CAD‑based model

Correction to: Simulation of spiral bevel gear manufacturing by face hobbing and prediction of the cutting forces using a novel CAD‑based model

Simulation of spiral bevel gear manufacturing by face hobbing and prediction of the cutting forces using a novel CAD-based model

Simulation of spiral bevel gear manufacturing by face hobbing and prediction of the cutting forces using a novel CAD-based model

Numerical simulation of selective laser melting based onSPH method

<p indent="0mm">The selective laser melting (SLM) is a common additive manufacturing technology. SLM formation involves complex multiphysical phenomena, such as heat transfer, melting, molten flow, and solidification. The development of a numerical method for modeling multitrack powder melting in SLM has important scientific significance and broad application prospects. In this paper, a powder-scale multiphysics numerical model of molten pool dynamics is established using the SPH method. For the first time, the three-dimensional SPH simulation of multilayer multitrack SLM has been realized. First, a key technique for transforming SPH surface particles into interface meshes is proposed, and the multilayer powder bed is realized. Then, the effects of energy density and laser hatch spacing on molten formation are discussed. The optimal simulation condition of a single layer is obtained and further used in multilayer simulation. This work makes up for the deficiency in SPH simulation of powder-scale metal additive manufacturing and can provide a reliable simulation tool for defect analysis, process optimization, and the correlation between process parameters and product performance.

A Review on Modelling and Simulation of Laser Additive Manufacturing: Heat Transfer, Microstructure Evolutions and Mechanical Properties

Modelling and simulation are very important for revealing the relationship between process parameters and internal variables like grain morphology in solidification, precipitate evolution, and solid-state phase transformation in laser additive manufacturing. The impact of the microstructural changes on mechanical behaviors is also a hot topic in laser additive manufacturing. Here we reviewed key developments in thermal modelling, microstructural simulations, and the predictions of mechanical properties in laser additive manufacturing. A volumetric heat source model, including the Gaussian and double ellipsoid heat sources, is introduced. The main methods used in the simulation of microstructures, including Monte Carlo method, cellular automaton, and phase field method, are mainly described. The impacts of the microstructures on mechanical properties are revealed by the physics-based models including a precipitate evolution based model and dislocation evolution based model and by the crystal plasticity model. The key issues in the modelling and simulation of laser additive manufacturing are addressed.

Remote laser welding simulation for aluminium alloy manufacturing using computational fluid dynamics model

The process of remote laser welding is simulated in this study to identify the keyhole-induced porosity generation mechanisms and keyhole. Three processes are simulated and discussed: laser power levels, laser-beam shaping configurations, and laser keyhole process. The simulation finding reveals that pore development is caused by strong melt flow behind the keyhole. As verification, the equivalent experimental test is also carried out. According to the findings, a welding speed with a high level helps to keep the keyholes released and prevents the flow of strong melt; a big advanced leaning-angle also provides inactive molten pool flow, making it difficult for bubbles to float to the backside of the molten pool. The conclusions of this study offer crucial insight into the method of porosity of aluminum (Al) alloys laser welding, as well as advice on how to avoid keyhole-induced porosity. It is also obtained that a smaller laser beam with constant power raises the velocity, welding pool depth, and liquid metal temperature.

Surgical planning and finite element analysis for the neurocraneal protection in cranioplasty with PMMA: A case study

New developments in terms of additive manufacturing, computational tools and mathematical simulation techniques have favored the development of successful methodologies for the restoration or restitution of bone structures in the human body. Likewise, achievements in Materials Science have allowed the development of biocompatible composites capable of achieving mechanical characteristics and biological similarities comparable to those of natural bone. Without considering the advantages and disadvantages of some biomaterials with respect to others, this research aims to evaluate the surgical planning, the design process, the impact resistance and the critical deflection of a customized cranial implant manufactured from polymethylmethacrylate (PMMA). With the support of finite element methods (FEM), the level of neurocranial protection offered by the implant is assessed.

Strip-Type Embeddable Shape Sensor Based on Fiber Optics for In Situ Composite Consolidation Monitoring.

Carbon fibers and resin used in manufacturing carbon fiber-reinforced plastic composite structures flow before the resin solidifies, resulting in disrupted fiber orientation and non-uniform thickness. This process, known as consolidation, is critical for the quality of the composite structure, but no technology exists to measure the deformation in situ. This study proposes a strip-type embeddable shape sensor based on fiber optics for in situ monitoring of consolidation deformation. The sensor consists of a thin, flexible sheet with optical fibers embedded in the upper and lower surfaces of the sheet, and it can monitor out-of-plane bending deformation in composite materials during consolidation. Finite element analysis and experiments are used to evaluate the basic performance of the shape sensor before it is applied to composite gap/lap monitoring. For the first time, the relaxation of consolidation deformation due to the flow of fiber-resin suspension is measured. The proposed sensor will be a powerful tool for elucidating consolidation mechanisms and for validating composite manufacturing simulations.

Simulation and Genetic Algorithms to Improve the Performance of an Automated Manufacturing Line

Simulation in manufacturing is often applied in situations where conducting experiments on a real system is very difficult often because of cost or the time to carry out the experiment is too long. Optimization is the organized search for such designs and operating modes to find the best available solution from a set of feasible solutions. It determines the set of actions or elements that must be implemented to achieve an optimized manufacturing line. As a result of being able to concurrently simulate and optimize equipment processes, the understanding of how the actual production system will perform under varying conditions is achieved. The author has adopted an open-source simulation tool (JaamSim) to develop a digital model of an automated tray loader manufacturing system in the Johnson &amp; Johnson Vision Care (JJVC) manufacturing facility. This paper demonstrates how a digital model developed using JaamSim was integrated with an author developed genetic algorithm optimization system and how both tools can be used for the optimization and development of an automated manufacturing line in the medical devices industry.

Multicellular Cell Seeding on a Chip: New Design and Optimization towards Commercialization.

This paper shows both experimental and in-depth theoretical studies (including simulations and analytical solutions) on a microfluidic platform to optimize its design and use for 3D multicellular co-culture applications, e.g., creating a tissue-on-chip model for investigating diseases such as pulmonary arterial hypertension (PAH). A tissue microfluidic chip usually has more than two channels to seed cells and supply media. These channels are often separated by barriers made of micro-posts. The optimization for the structures of these micro-posts and their spacing distances is not considered previously, especially for the aspects of rapid and cost-efficient fabrication toward scaling up and commercialization. Our experimental and theoretical (COMSOL simulations and analytical solutions) results showed the followings: (i) The cell seeding was performed successfully for this platform when the pressure drops across the two posts were significantly larger than those across the channel width. The circular posts can be used in the position of hexagonal or other shapes. (ii) In this work, circular posts are fabricated and used for the first time. They offer an excellent barrier effect, i.e., prevent the liquid and gel from migrating from one channel to another. (iii) As for rapid and cost-efficient production, our computer-aided manufacturing (CAM) simulation confirms that circular-post fabrication is much easier and more rapid than hexagonal posts when utilizing micro-machining techniques, e.g., micro-milling for creating the master mold, i.e., the shim for polymer injection molding. The findings open up a possibility for rapid, cost-efficient, large-scale fabrication of the tissue chips using micro-milling instead of expensive clean-room (soft) lithography techniques, hence enhancing the production of biochips via thermoplastic polymer injection molding and realizing commercialization.

Discontinuous Galerkin FEM with Hot Element Addition for the Thermal Simulation of Additive Manufacturing

Despite its promising advantages, the application of directed energy deposition (DED) to produce large metal parts is hindered by challenges inherent to the process. Undesired residual stresses, distortions and heterogeneous material properties mainly originate from a part’s thermal history. Fast part-scale thermal models therefore facilitate improved applicability of DED by enabling the prediction and mitigation of these unwanted effects. In this work, the efficiency of a discontinuous Galerkin-based thermal model with heat input by hot element addition, is evaluated and improved to allow such fast simulations. It is found that the model permits the use of a coarse discretization around the heat source, which significantly reduces simulation time while maintaining accurate solutions. It is also shown that the model naturally facilitates the use of local time stepping, which can considerably improve the efficiency of thermal additive manufacturing simulations.

A systematic approach to resolve high impedance of multifunctional energy storage composites

Previous work has proposed and characterized the structural and electrical performance of Multifunctional Energy Storage Composite (MESC) structures: structural elements with embedded lithium-ion batteries which were developed by the Structures and Composite Laboratory (SACL) at Stanford University. This work conducts a comprehensive parametric study over the design variables in the manufacturing process to determine the root cause of increased DC impedance compared to commercial pouch cell batteries. Several MESC samples were assembled under various controlled environmental temperatures and pressures. The samples underwent Hybrid Peak Pulse Characterization (HPPC) to approximate the DC impedance as a function of state of charge (SOC). Failure limits of the battery with respect to assembly temperature and pressure were tested, and an MESC manufacturing simulation model was constructed in Abaqus to validate the manufacturing limits. Samples assembled without enough temperature or pressure failed due to a lack of structural integrity or hermetic seal. Conversely, samples which underwent elevated temperatures and pressures resulted in high DC impedance due to nonuniform electrolyte wetting. This work resolves the primary concern of MESC by achieving comparable DC impedance to commercial pouch cells of the same chemistry and testing procedure.

A multi-criteria h-adaptive finite-element framework for industrial part-scale thermal analysis in additive manufacturing processes

This work presents an h-adaptive finite-element (FE) strategy to address the numerical simulation of additive manufacturing (AM) of large-scale parts. The wire-arc additive manufacturing is chosen as the demonstrative technology for its manufacturing capabilities suitable for industrial purposes. The scanning path and processing parameters of the simulation are provided via a RS-274 (GCode) file, being the same as the one delivered to the AM machine. The approach is suitable for industrial applications and can be applied to other AM processes. To identify the location in the FE mesh of the heat affected zone (HAZ), a collision detection algorithm based on the separating axis theorem is used. The mesh is continuously adapted to guarantee the necessary mesh resolution to capture the phenomena inside and outside the HAZ. To do so, a multi-criteria adaptive mesh refinement and coarsening (AMR) strategy is used. The AMR includes a geometrical criterion to guarantee the FE size within the HAZ, and a Zienkiewicz–Zhu-based a-posteriori error estimator to guarantee the solution accuracy elsewhere. Thus, the number of active FEs is controlled and mesh manipulation by the end-user is avoided. Numerical simulations comparing the h-adaptive strategy with the (reference) fixed fine meshes are performed to prove the computational cost efficiency and the solution accuracy.

Investigation of flow field, die swelling, and residual stress in 3D printing of surimi paste using the finite element method

In this research, the finite element method (FEM) was used to analyze the flow behavior in 3D printing (3DP) of surimi pastes. Additive layer manufacturing (ALM) simulation was used to analyze residual stress during and after 3DP. Rheological studies showed that the surimi paste with 82% moisture was suitable for 3DP. The FEM model revealed that a decrease in nozzle diameter (1.2–0.6 mm) increased die swell (9.8–14.1%). The rise in the swell ratio was due to an increase of pressure gradient at the nozzle exit (1.15 × 107Pa to 7.80 × 107Pa). FEM simulation indicated that the nozzle diameter influenced fluid properties (pressure, velocity, and shear rate) in the flow field as well as residual stress and deformation of the printed sample. Industrial relevanceThe development and production of food with 3-dimensional printing (3DP) technology has the potential to create and produce customized food in a more advanced format that will be a new paradigm shift in the food industry. To reach the full potential of this promising technology, methods for evaluating and predicting production outcomes must be identified. This study provides an insight into the use of the finite element method (FEM) for the prediction of printability and the deposition process of surimi paste. This method can be applied to other foods during 3DP.

Decision-making in smart manufacturing: A framework for performance measurement

ABSTRACT The deployment of smart manufacturing technologies like communication, computing, sensors, cyber-physical systems, simulation, and data sciences has enabled the operational transformations of the factory ecosystem. Therefore, it is essential to monitor the alterations commenced by the business due to smart manufacturing implementation. Smart Manufacturing Performance Measures (SMPMs) are used to evaluate operational transformations realized through digitalization of systems. However, there is no reported work focusing on the quantification of SMPMs. This study addresses the research gap by defining potential indicators to quantify SMPMs referred to as smart manufacturing performance indicators (SMPIs) identified through literature review methodology. The SMPIs are discussed in detail besides their mathematical expression advocated through in-depth literature study. Further, a conceptual framework for decision-making in smart manufacturing environment based on SMPIs is proposed. The conceptual framework provides guidelines to plan and select the preferred focused manufacturing output and the relevant set of SMPIs contributing to the outputs for expediting effective smart manufacturing implementation. The research findings are beneficial for the managers and consultants to gauge the potential structure of measurement that can be used to evaluate smart manufacturing systems’ performance.

PROSIM in the Cloud: Remote Automation Training Platform with Virtualized Infrastructure

The need for high-availability, efficient, training solutions for industrial automation technology is driving the adoption of new tools, software and services supporting the Industry 4.0 paradigm. Specific requirements include vendor agnostic and standards-based programming languages and availability of both physical input–output and industrial communication, while providing a rich and immersive experience through suitable human–machine interface components. The PROSIM platform represents an integrated embedded hardware-software system for programmable logic controller automation training which provides examples in increasing order of complexity, ranging from basic digital logic up to complex manufacturing simulations and process control. A fully software-based solution has been developed in order to complement the laboratory-based system as a training digital twin. We describe the design, implementation and evaluation of the platform as ported onto the HUBCAP collaborative sandboxing environment for virtualized operation. Integration of dedicated communication libraries for Open Platform Communications, Unified Architecture, with simulated programmable logic controllers such as PLCSIM and ARSim is also discussed for fully leveraging new features emerging from increased information and operational technology convergence witnessed in realistic, plant-level, environments. Qualitative and quantitative evaluation in comparison to the physical system while leveraging the resources provided by the cloud environment is carried out.

Hierarchical multi-response Gaussian processes for uncertainty analysis with multi-scale composite manufacturing simulation

Variations of constituent fiber and matrix properties and process conditions can cause significant variability in composite parts and affect their performance. The focus of this paper is to establish a new computational framework that can efficiently quantify the uncertainty propagation of parts manufactured through the resin transfer molding (RTM) process. RTM involves a sequence of inter-related processes that span multiple spatial and temporal scales. This calls for a multi-scale analysis for the nominal process, which is computationally complex and intensive. A direct Monte Carlo simulation of uncertainty quantification leads to prohibitive cost. In this research we leverage a sequentially architected multi-response Gaussian process (MRGP) meta-modelling approach to facilitate a hierarchical procedure. This can dramatically reduce the computational cost, and allow us to characterize the process outputs of interest at different scales and at the same time capture the intrinsic correlation amongst these outputs. Moreover, integrating a global sensitivity analysis with the hierarchical MRGP meta-models yields the importance ranking of uncertainty propagation paths. This computational framework provides a quantitative assessment tool of the uncertainties in composite manufacturing. Case study on curing-induced dimensional variability of a curved composite part is conducted for demonstration and validation.

Multi-task Gaussian process upper confidence bound for hyperparameter tuning and its application for simulation studies of additive manufacturing

In many scientific and engineering applications, Bayesian Optimization (BO) is a powerful tool for hyperparameter tuning of a machine learning model, materials design and discovery, etc. Multi-task BO is a general method to efficiently optimize multiple different, but correlated, “black-box” functions. The objective of this work is to develop an algorithm for multi-task BO with automatic task selection so that only one task evaluation is needed per query round. Specifically, a new algorithm, namely, Multi-Task Gaussian Process Upper Confidence Bound (MT-GPUCB), is proposed to achieve this objective. The MT-GPUCB is a two-step algorithm, where the first step chooses which query point to evaluate, and the second step automatically selects the most informative task to evaluate. Under the bandit setting, a theoretical analysis is provided to show that our proposed MT-GPUCB is no-regret under some mild conditions. Our proposed algorithm is verified experimentally on a range of synthetic functions. In addition, our algorithm is applied to Additive Manufacturing simulation software, namely, Flow-3D Weld, to determine material property values, ensuring the quality of simulation output. The results clearly show the advantages of our query strategy for both design point and task.

Development of the Platform for Three-Dimensional Simulation of Additive Layer Manufacturing Processes Characterized by Changes in State of Matter: Melting-Solidification.

A new platform for three-dimensional simulation of Additive Layer Manufacturing (ALM) processes is presented in the paper. The platform is based on homogeneous methods—the Lattice Boltzmann Method (LBM) with elements of Cellular Automata (CA). The platform represents a new computer-based engineering technique primarily focused on Selective Laser Melting (SLM) technology. Innovative computational strategies and numerical algorithms for simulation and analysis of entire powder bed-based technology with changes in state of matter (melting-solidification) are presented in the paper. The models deal mainly with heat transfer, melting and solidification, and free-surface flow. Linking LBM and CA into a complex holistic model allows for complete full-scale simulations avoiding complicated interfaces. The approach is generic and can be applied to different multi-material powder bed-based SLM processes. A methodology for the adaptation of the model to the real material (Ti-6Al-4V alloy) and processing parameters is presented. The paper presents the first quantitative results obtained on the platform and shows the ability of the model to simulate and analyze a very complex technology, entirely without a complicated interface between the sub-models. It solves the large-scale problem connected with computer-aided design and analysis of new multi-passes and multi-materials processes.

Thermal simulation of wire arc additive manufacturing: a new material deposition and heat input modelling

Thermal simulation of wire arc additive manufacturing: a new material deposition and heat input modelling