Modeling Of Manufacturing Processes Research Articles

Trace induction for complete manufacturing process model discovery

In industrial automation, there are numerous use cases for a model representing the equipment’s sequence of operations. Not only can such a model be used as a debugging aid, allowing users to observe the current state of the machinery at a glance, but it also can serve as a foundation for process improvements. Annotated process models can help pinpoint bottlenecks within the system, and a combination of models obtained from identical machines can be used to create a best-case baseline to which the devices can be optimised. Typically, the models used for such activities have been created during the equipment’s design phase, which means that many of the changes made during commissioning, start-up, and production are not reflected. The research domain of process mining suggests that an accurate model can be obtained from an activity log. Since most industrial processes are controlled by Programmable Logic Controllers (PLCs), which offer the capability of external access through Open Platform Communications (OPC), such a log can be created automatically. The theory is that process mining algorithms can then be used to discover the desired model from the data recorded. Unfortunately, it turns out that this whole procedure is a game of chance. The quality of the discovered models strongly depends on the logged data. Not only can the records be flawed, but also the information needed to discover a complete model might not have been observed at all. To better judge the quality of the discovered models, numerous quality metrics have been proposed, which in the authors’ opinion are a good guess at best. The industrial automation domain demands complete models, which cannot be guaranteed by any process mining algorithm unless it is known that the log contains all the data needed. This paper shows that for industrial equipment, it is possible to reason through the evaluation of a single recorded case, which case traces are required to guarantee that the discovered model will be complete. In the next step, the authors then introduced the concept of ‘trace induction’, which takes advantage of the fact that a PLC controls the observed processes. A minor change within the PLC’s logic is used to force the process to execute the desired traces on demand. The resulting, minimalistic log can then be analysed by the αLC-algorithm to create a guaranteed complete, model.

An adaptive second-order element-free Galerkin method for additive manufacturing process

Element-free Galerkin (EFG) method is applied for the first time to numerical modeling of additive manufacturing (AM) process, in which the superiorities of the EFG method in implementing adaptive computation and in constructing high order approximation are exploited. Second-order moving-least squares (MLS) approximation is constructed for both temperature and displacement. A non-linear coupled thermo-mechanical model considering moving heat sources, radiation boundaries, elastoplastic materials and temperature-dependent parameters is adopted. Adaptive coarsening based on the background integration mesh is developed to efficiently model the layered deposition process. In addition, an efficient integration scheme using background hexahedral elements is proposed to evaluate the domain integrals of the weak forms. Numerical results show that the developed method is able to effectively model the AM process with substantially reduced number of approximation nodes. The effects of laser power, scan speed and scan path on temperature and distortion are investigated. The improved accuracies due to the proposed integration scheme and the adopted second-order approximation are also demonstrated.

Risk Evaluation for a Manufacturing Process Based on a Directed Weighted Network.

The quality of a manufacturing process can be represented by the complex coupling relationship between quality characteristics, which is defined by the directed weighted network to evaluate the risk of the manufacturing process. A multistage manufacturing process model is established to extract the quality information, and the quality characteristics of each process are mapped to nodes of the network. The mixed embedded partial conditional mutual information (PMIME) is used to analyze the causal effect between quality characteristics, wherein the causal relationships are mapped as the directed edges, while the magnitudes of the causal effects are defined as the weight of edges. The node centrality is measured based on information entropy theory, and the influence of a node is divided into two parts, which are local and indirect effects. Moreover, the entropy value of the directed weighted network is determined according to the weighted average of the centrality of the nodes, and this value is defined as the risk of the manufacturing process. Finally, the method is verified through a public dataset.

A curing model for the numerical simulation within additive manufacturing of soft polymers using peridynamics

Within this paper, the modelling and simulation of extrusion-based Additive Manufacturing (AM) processes of curing polymers is presented. The challenge of the AM is the adjustment of processing parameters. This includes the application of laser radiation to locally accelerate the curing in order to control the final geometry of the implant. Since complex multi-physical coupling effects are hardly predictable by operator experience, numerical simulations are beneficial. When the underlying physical effects of the AM processes are captured correctly within the simulations, a realistic representation of the process is possible. To model the material behaviour during the process, a process-dependent large strain curing model is formulated, considering the stress free curing behaviour of the material. State-of-the-art models are not able to model the fluid-like behaviour of low cured polymers. This needs a formulation that takes into account finite deformations. Hence, the current model is extended to finite plasticity using a process-dependent yield function. This allows the modelling of material spreading in the fluid-like state by simultaneously reducing the accumulation of elastic stored energy, which would lead to an unintentional and non-physical bounce-off behaviour otherwise. For the numerical simulations, an enhanced version of the peridynamic correspondence formulation using fractional subfamilies with associated volume weighting factors is introduced and implemented. Besides the specific laser modelling as a volumetric heat source, a local–non-local coupling of the arising thermo-chemo-mechanical coupled equations is introduced within the peridynamic framework. Within the simulations, the applicability of the plasticity-based approach to model material spreading in the fluid-like state is presented. Finally, the software for extrusion-based printing processes is developed and the complete thermo-chemo-mechanical coupled AM process is simulated. It is shown that higher geometrical precision is obtainable in terms of a reduced material spreading by the application of a laser radiation. The model constitutes the first step of the virtual implant development regarding the optimisation possibilities during the AM process.

Generating a skin-based meta-model for compliant parts in composite material: numerical and experimental results

PurposeThe purpose of this paper is to present a skin-based approach able to generate the variability model for a component in composite material due to its manufacturing process. It generates a skin-based model of the manufactured part. The skin model discretizes the part surfaces by points to take into account the geometric deviations, those points are the nodes of finite element analysis used for tolerance analysis of compliant assemblies.Design/methodology/approachThe paper presents a general and systematic simulation model for generating a variability meta-model for a component in composite material due to its manufacturing process. The model is constituted by three steps: definition and pre-processing of the nominal model, generation of the manufacturing process model and evaluation of the part variability.FindingsThe advantage of this approach is related to the fact that it is designed as a part of a digital process that establishes a continuous and unambiguous flow of variation information from the part design to manufacturing and assembly and that takes into account the manufacturing signature. This is its uniqueness compared to other simulation approaches focused only on manufacturing.Research limitations/implicationsConsidering the variability around the nominal value of all the process parameters and parts with more complex geometries are not taken into account now, which will be modelled in practical applications.Practical implicationsTo properly manage uncertainty since conceptual design of complex product, next generation geometry assurance requires simulation models to realistically consider process signatures due to the manufacturing process. This work focusses on this next generation tool for geometry assurance.Originality/valueThe literature is focused on metal sheets joined by welding or riveting. There are other materials widely used and typically compliant: the composite materials that typically used mechanical fixing elements (bolting, riveting) and structural adhesives to joint parts. No software tools exist in the literature to deal with uncertainty from manufacturing to assembly processes in products made by composite. This is the reason of the present work.

Optimization of fused filament fabrication process parameters under uncertainty to maximize part geometry accuracy

This work presents a novel process design optimization framework for additive manufacturing (AM) by integrating physics-informed computational simulation models with experimental observations. The proposed framework is implemented to optimize the process parameters such as extrusion temperature, extrusion velocity, and layer thickness in the fused filament fabrication (FFF) AM process, in order to reduce the variability in the geometry of the manufactured part. A coupled thermo-mechanical model is first developed to simulate the FFF process. The temperature history obtained from the heat transfer analysis is then used as input for the mechanical deformation analysis to predict the dimensional inaccuracy of the additively manufactured part. The simulation model is then corrected based on experimental observations through Bayesian calibration of the model discrepancy to make it more accurately represent the actual manufacturing process. Based on the corrected prediction model, a robustness-based design optimization problem is formulated to optimize the process parameters, while accounting for multiple sources of uncertainty in the manufacturing process, process models, and measurements. Physical experiments are conducted to verify the effectiveness of the proposed optimization framework.

Validation of a smoothed particle hydrodynamics model for a highly aligned discontinuous fibre composites manufacturing process

This work presents a computational model for a discontinuous fibre composite manufacturing process. The alignment mechanism of this novel process, called the High Performance Discontinuous Fibre (HiPerDiF) method, involves highly coupled fluid-structure interactions. Fibres with a length on the order of a few millimetres are placed in a water suspension, sprayed between two parallel plates and deposited on a moving belt to make an aligned discontinuous fibre tape. This technology can be used as part of a composites recycling process to remanufacture reclaimed fibres into valuable recycled composite feedstock by ensuring a high level of alignment. In order to industrialise this process, the throughput must be increased whilst maintaining the high level of alignment. This work aims to assist this development by modelling the alignment mechanism using smoothed particle hydrodynamics (SPH). It is shown through comparison to experiments that SPH models the process well and captures the influences affecting fibre alignment.

Fuzzy testing decision-making model for intelligent manufacturing process with Taguchi capability index

Fuzzy testing decision-making model for intelligent manufacturing process with Taguchi capability index

Economic Order Quantity Models for the Remanufacturing Industry with Imperfect Process, Two- State Markovian, and Two Types of Inventory

An economic order quantity model for an imperfect manufacturing process and imperfect items is proposed. The remanufacturing industry converts used parts to aftermarket usable parts. The remanufacturing process is imperfect; a probability is known that an item after manufacturing is not repaired, and hence, this item is sent back to the inventory of damaged items for another round of remanufacturing. A discrete Markov chains model describes the transitional probabilities of the state of the item after the remanufacture. The first inventory model is proposed to identify the optimal order size of imperfect items, assuming the imperfect items arrive at a constant and finite rate. The second proposed model assumes an infinite arrival rate; hence, in essence imperfect items arrive in one batch. For both cases of the inventory models, the optimal economic order quantity is derived based on the expected average cost functions per time to the remanufacturer.

Research on Order Tracking System for MTO

MTO is the most appropriate approach used for highly customized products, It requires the order tracking system for controlling the delivery date, manufacturing process and products quality. The author has made researches on the order, the manufacturing process and organization model, then presents a order tracking system architecture for MTO (Make-to-Order) based on the study of order tracking functions and contents, and finally proves the model and system architecture valid by the case.

An Online Virtual Metrology Model With Sample Selection for the Tracking of Dynamic Manufacturing Processes With Slow Drift

Modeling of dynamic manufacturing processes with slow drift using data-driven approaches is challenging because most data-driven models are trained by off-line data. In this paper, we propose to track slow drift of manufacturing processes using an online Bayesian Auto-Regression eXogenous (ARX) model with time-variant parameters. The model parameters are trained off-line and are updated online using Bayes’ rule. To avoid the frequent online model update, a Sample Importance (SI) test is proposed to screen online samples and only the sample that passes SI test is incremented to the VM model. The SI test decides the importance of the sample by collectively considering sample freshness, model prediction error and prediction uncertainty. Furthermore, the SI test is applied to off-line Data Base (DB) and an iteration algorithm is devised for off-line sample selection. The off-line sample selection algorithm can effectively reduce the sample redundancy in off-line DB while maintaining the model performance. To validate the effectiveness of the proposed method, Prognostics and Health Management (PHM) data challenge 2016 dataset is employed to predict material removal rate of chemical-mechanical planarization process. The validation results indicate that the proposed method outperforms existing state-of-art approaches in literature such as Just-in-Time (JIT) and deep learning models.

Physical and Numerical Modeling of the Manufacturing Process of a Complex Shape Pressed Element from a Nickel Superalloy

Physical and Numerical Modeling of the Manufacturing Process of a Complex Shape Pressed Element from a Nickel Superalloy

Establishment of an induced pluripotent cell line from Taiwan black silkie chick embryonic fibroblasts for replication-incompetent virus production

The objective of this study was to establish a versatile cell line for replication-incompetent virus production and inactivation with formaldehyde to generate a model of cell-based vaccine manufacturing process. To achieve this goal, we took advantage of the easily accessed chick embryonic fibroblasts. Nine-day old chick embryonic fibroblasts were obtained and subjected to be transduced with a set of lentivirus to develop a chick induced pluripotent stem (ciPS) cell line. Morphological features, positive periodic acid-Schiff staining as well as strong immunocytofluorescence of alkaline phosphatase, intestinal (ALPI) and POU class 5 homeobox 1 (POU5F1) proteins suggested that these chick embryonic fibroblasts have been transformed into ciPS cells. Further differentiation and immunocytofluorescence assays confirmed that this ciPS cell line possesses capacities and potentials to form embryoid bodies, differentiate into all three embryonic layers: ectoderm, mesoderm and endoderm with evidence of strongly positive and specific molecular markers. Immunoblot analysis next demonstrated that through recombinant DNA technology and the 2nd generation lentiviral transfer system, the goose hemagglutinin gene (H5) gene was packaged into the replication-incompetent virus and highly expressed in a bladder cancer-derived cell line, T24, after transduction. The titer of ciPS-generated replication-incompetent virus is comparable to that from the Phoenix-AMPHO cell line, which is a commercial and high productive retrovirus producer. Our study successfully established a ciPS cell line which is able to produce replication-incompetent virus, providing a new strategy for cell-based vaccine production after virus inactivation.

Numerical verification of an Octree mesh coarsening strategy for simulating additive manufacturing processes

Thermo-mechanical finite element modeling of additive manufacturing processes, such as Directed Energy Deposition and Laser Powder Bed Fusion, has been widely applied for the prediction and mitigation of part distortion. However, as the size of modeled geometries gets larger, the number of nodes and elements required in the finite element mesh increases significantly. Because runtime will increase as more nodes are added, it is not practical to conduct full simulations of large builds using standard static meshes. Advanced meshing strategy is required to reduce the run time and to retain the accuracy of the prediction. In this work, a mesh coarsening strategy is evaluated for predicting temperature, distortion, and residual stress in additive manufacturing, aiming to achieve feasible run times with reasonable accuracy on large builds. Directed Energy Deposition of thin wall geometries built from Inconel® 625 and Ti6Al4V is used as a reference and models with 2 levels of Octree mesh coarsening are investigated. The thermal history, in situ distortion, residual stress, and run times are compared with previously experimentally validated static mesh results. Two levels of mesh coarsening is found to be the most effective case for both materials reducing the computational time by 75% while reporting less than 2.5% error for the peak distortion and negligible error for the thermal history difference as compared to the static mesh. Keeping two fine layers of elements underneath the heat source is found to be the most efficient in terms of prediction accuracy and run time.

On Rough Set-Based Modeling and with Application to Process Decision for Forming Plate by Line Heating

In shipbuilding, forming plate by line heating is atypical complex manufacturing process involving many uncertain factors, so it is difficult to establish an accurate mathematical model. How to establish a knowledge model that can reflect technological laws is the key to the development of an intelligent decision system for forming plate by line heating. In this article, rough set (RS) theory is applied to the modeling of the line heating process. By defining variable-inclusion RSs, an algorithm of knowledge reduction is proposed, which enhances the noise immunity and fault tolerance of the model, and improves the efficiency of knowledge acquisition. Through introducing fuzzy logic, a method of modeling the line heating process based on RSs and fuzzy logic is proposed, which effectively extracts the technological rules of plate formation. Finally, rapid decision-making for process parameters is implemented by fuzzy inference technology. 1. Introduction For some complex manufacturing technologies in modern shipbuilding, such as line heating and welding processes, it is difficult to establish an exact mathematical model because of high nonlinearity, multivariable coupling, and uncertainties of the system. With the development of computer technology and artificial intelligence, soft computing methods such as artificial neural networks, genetic algorithms, fuzzy logic, and rough set (RS) theory have been applied successively in ship manufacturing process modeling, which shows good prospects for intelligent technology in shipbuilding. Shin et al. (1999) at Seoul National University used a single-curvature plate model to simulate the formation of saddle-type shells and deduce the technological parameters of line heating by an artificial neural network. They also proposed a comprehensive algorithm for automatically curving plate by line heating, and further developed an application system that can simulate the deformation of double-curved plates (Shin et al. 2004a, 2004b). Liu et al. (2006) applied a hierarchical genetic algorithm to optimize the technological parameters of an automatic line heating process. In the field of ship welding, fuzzy logic technology has been used to establish a fuzzy model of the relationship between welding variables and weld forming-parameters (Su 2009). Feng (2012) set up a knowledge base of a ship-welding process by a RS method and then implemented ship-welding production design through uncertainty reasoning. Based on RS theory, Chen and Lv (2013) developed a data-driven knowledge base for quality control of ship hull welding.

Crystallization Kinetics of Polyamide 2200 in the Modelling of Additive Manufacturing Processes by FE Analyses

The thermoplastic polymers present amorphous or semi-crystalline structures which are very important factors in describing volumetric shrinkage. The thermoplastic materials are commonly used for production of daily life products, industrial or as the prototypes. Different techniques of manufacturing polymer structures are considered like: injection molding, extrusion, milling, additive manufacturing (AM). AM is a very fast developing field in the manufacturing and research. Unfortunately, components or prototypes made using the thermoplastic semi-crystalline materials in 3D techniques have quite low mechanical strength compared to the parts made by injection molding processes. It is caused by porosity obtained during the processing, as well as by fraction of crystallinity in the volume of the components. Additionally, the volumetric shrinkage is hard to predict without knowledge of its origin. Therefore, it is necessary to consider crystallization kinetics and the melting of the analysed materials. The investigations presented in this work concern the crystallization and melting model to be implemented in the finite element (FE) analyses. With use of the model, one can predict development of the structure during the real processes and, in the future, to control the warpage of the manufactured components.

Solid State Additive Manufacturing of Acrylonitrile Butadiene Styrene with Silica Augmentative: Application of Friction Stir Processing

The additive manufacturing processes are increasing their importance in fabrication of composite like structures. Friction stir processing is classified as a solid state additive manufacturing technique that is used for lamination or powder deposition in a matrix substrate. In the present work an attempt was made to add the silica as secondary phase into the acrylonitrile butadiene styrene (ABS) surface for enhancing the mechanical and surface properties of fabricated composite. The parameters such as amount of silica fraction, pass number, tool rotational speed and linear speed were considered to be involved in the study. The responses surface method (RSM) was used here to assess relationship between aforementioned inputs and outputs vs bending strength and deflection before fracture. Optimal setting of process parameters was determined by maximizing bending strength and deflection. The obtained optimal results showed that maximum strength and deflection can be achieved by selecting of 100% silica volume fraction, 1 pass number, 1400 rpm tool rotation and 80 mm/min travel speed. The optimum results were further verified through confirmatory experiment and the result showed that the values of prediction error for bending strength and deflection are 6.1% and 8.5%, respectively. This proves the validity of the proposed approach for empirical modeling and optimization of friction stir additive manufacturing process.

Mathematical modeling of the synthesis process of vinyl acetate

A complete mathematical modeling of the technological process of manufacturing vinyl acetate monomer by vapor-phase method has been implemented. The model is partial differential equations of the material and energy balance and catalyst aging. An algorithm for an integrated equation based on the second-order Gregg-Bulirsh-Shter rectangle symbol has been developed. The error in modeling the process is equal (CH3COOCH=CH2) ± 5.4% rel. for the target matter, and (CO2) ± 6.4% rel. for the secondary substance The kinetic coefficients of differential equations, namely the pre-exponential factors and the activation energy have been refined. The obtained data of the modeling of the vapor-phase manufacturing process of vinyl acetate are shown in the form of tables and graphic three-dimensional dependencies of quality indicators on technological parameters.

Integrated modeling of a continuous direct compression tablet manufacturing process: A production scale case study

A flowsheet model for a continuous direct compression tableting process is developed and applied to process data generated at production scale. The model contains powder feeding, blending and tablet compaction unit operations. The dataset was provided by Merck & Co., Inc., Kenilworth, NJ USA using a GEA ConsiGma™ continuous direct compression process designed for approximately 50 kg/h throughput using a six ingredient, potentially commercial, formulation. A tanks-in-series methodology is used to model the transport of powders through the blending units and a number of tablet compaction equations are selectable which can illustrate how upstream changes in composition are propagated to tablet properties. The flowsheet model performed well based on the available dataset with accurate predictions of API concentration and tablet hardness. Monte Carlo simulations were used along with the flowsheet model to investigate the contribution of feeder flowrate variability to measurement noise and the sensitivity of tablet hardness to process parameters and material properties.

Data-driven modeling and simulation of complex multistation manufacturing process for dimensional variation analysis

Dimensional variation analysis in multistation manufacturing processes (MMPs) is a challenging research topic with great practical significance. Researchers have been focused on constructing various mathematical models to identify the correlations among the huge amounts of collected production data. However, current models have achieved insufficient insights into the variation correlation laws due to the complexity of the data’s mutual relations. In this study, a data-driven modeling method is developed for deep data-mining and dimensional variation analysis. The proposed initial mathematical expression originates from practical engineering knowledge. Through a mathematical treatment, the mathematical expression is transformed into a first-order AR(1) model format, which contains multiple dimensional variations’ interstation and temporal correlating information. To obtain this information, the estimation of the proposed model is discussed in detail. A simulation case involving two key product characteristics of a grinding process is used to demonstrate the effectiveness and accuracy of the proposed method for dimensional variation analysis in MMPs.