Parameter Identification Process Research Articles

Sequestration of Paracetamol from Aqueous Solution Using Zinc Oxide/Polypyrrole Nanocomposite: Cost Analysis, Scale-Up Design, and Optimization of Process Parameters

Sequestration of Paracetamol from Aqueous Solution Using Zinc Oxide/Polypyrrole Nanocomposite: Cost Analysis, Scale-Up Design, and Optimization of Process Parameters

Modeling of Eyld2000-2d Anisotropic Yield Criterion Considering Strength Differential Effect and Analysis of Optimal Calibration Strategy.

Sheet metals usually experience various loading paths such as uniaxial tension, uniaxial compression, biaxial tension, and simple shear during the forming process. However, the existing constitutive models cannot always accurately describe blanks' anisotropic yield and plastic flow behavior of blanks under all typical stress states. Given this, this paper improves the Eyld2000-2d yield criterion by introducing hydrostatic pressure to the A-Eyld2000-2d yield criterion that can describe the strength differential effect of materials. Meanwhile, to control the curvature of the yield surface more effectively, the near-plane strain yield stresses were added in the parameter identification process to calibrate the exponent m, so that the exponent is no longer considered as a constant value. Taking the widely used AA6016-T4, AA5754-O, DP980, and QP980 blanks in the automotive stamping industry as an example, the effectiveness of the new model and different parameter identification methods was verified by predicting experimental data under various simple and complex loading paths. Subsequently, the new model employing the optimal parameter identification strategy was compared with four widely used asymmetric yield criteria under associated and non-associated flow rules, including CPB06, LHY2013, S-Y2004, and Hu & Yoon2021, to further verify the accuracy of the proposed constitutive model. The results indicate that parameter identification strategy with variable exponent can significantly improve the flexibility of the yield criterion in describing the plastic anisotropy of blanks. Compared to the other yield criteria examined in this work, the new model provides the best prediction accuracy for the yield stresses and plastic flows of all blanks, especially in the near-plane strain and simple shear stress states. Modeling under the concept of anisotropic hardening can more accurately capture the evolving plastic behavior of blanks than isotropic hardening.

ASPID: An asymmetric pinching damaged hysteresis model for timber structures

This paper presents a new high-fidelity phenomenological-based hysteretic model called ASPID that includes asymmetry, pinching, and strength/stiffness degradation for timber members. To capture asymmetry, one force–displacement envelope and ten physical-based hysteretic parameters are used for each loading direction. Pinching is added with an unloading–reloading monotone smooth piecewise function composed of two quadratic Bézier polynomials and one linear segment. Strength degradation at target displacement is taken using a novel fatigue law based on the maximum displacement and hysteretic dissipated energy. Exponential stiffness degradation of pinching and reloading phases is adopted in terms of their maximum displacement, whereas irreversible damage is captured using two threshold displacements independent of each loading direction. The update force algorithm is included for their computational implementation, and a Python version can be downloaded for free. The model is validated using six experimental benchmark tests of mass and lightweight timber connections and assemblies with symmetric/asymmetric behavior, where an optimized parameter identification process is considered. Moreover, the model test responses are compared with three well-known hysteretic models: SAWS, Pinching4, and DowelType. The ASPID model’s test results show an error less than 7.6% for the capacity and 2.9% for the cumulative dissipated energy as well as fits with high precision the force and dissipated energy history, getting a Normalized Root Mean Square (NRMS) error less than 4.3% and 2.8%, a Normalized Mean Absolute (NMA) error less than 2.7% and 1.5%, and a coefficient of determination R2 over 94.88% and 99.09%, respectively. Finally, comparing the four hysteretic models, the ASPID model gives the highest R2 values in all tests and has the smallest NRMS and NMA errors regarding force history and the smallest NMA errors for the dissipated energy history.

Optimization Method for Hot Air Reflow Soldering Process Based on Robust Design

The process design of hot air reflow soldering is one of the key factors affecting the quality of PCBA (Printed Circuit Board Assembly) component products. In order to improve the product quality during the design process, this paper proposes a robust optimization-based finite element simulation analysis method including significant influencing factor screening, robustness evaluation, robust optimization, and reliability verification for the reflow soldering process. The simulation model of the reflow soldering process temperature field based on experiments is constructed and validated. Sensitivity analysis is used to select important influencing factors, such as the last five set temperature zones (T5 to T9) in the reflow oven and the thermal properties of materials such as PCBs (printed circuit boards), BGAs (ball grid arrays), and solder paste, as well as noise factors like the heating environment during the soldering process. Several surrogate models are used to construct the response surface, and the optimal fitting scheme is selected to effectively avoid poor fitting caused by inappropriate surrogate models. The 6σ robust optimization approach is introduced to evaluate and optimize the robustness of the process design parameter where the heating factor is chosen as the optimization target. The reliability analysis method is employed to validate the product quality. This paper establishes a comprehensive robustness analysis method for hot air reflow soldering, effectively reducing design costs and addressing the lack of robustness analysis in the current hot air reflow soldering process design.

Developing a New Flow Cell for Electrochemical Machining Anodic Dissolution Investigations by Simulation-Based Design and 3D Printing

Abstract Electrochemical machining (ECM) is an essential non-traditional industrial shaping technology. An in-depth understanding of ECM anodic dissolution is fundamentally important for process parameter design and optimization. However, the existing electrochemical setups face challenges in achieving efficient analysis of these processes. In this work, a new flow cell has been developed via simulation-based design and 3D printing that demonstrates comprehensive advantages in terms of improved electric and flow conditions, measurement technique versatility, and production simplicity at low cost. Simulations are performed to reveal particular characteristics of the proposed cell in terms of physical distributions and to determine its key dimensions with high efficiency. The stereo lithography technique is used to realize the complex design and fabricate the proposed flow cell, thus ensuring ease of accessibility. Furthermore, the effectiveness of the developed cell is verified experimentally by examining the anodic behavior of typical metals in common ECM electrolytes, using Fe and SS304 stainless steel as examples. Test results show that information on the polarization behavior, current efficiency, anodic interface structure, and surface finish can be obtained conveniently and the results agree with previous findings, demonstrating the potential of the developed cell to perform high throughput tests to study ECM fundamentals.

Truss bridge design with reclaimed steel elements by performing a stock‐constrained shape and topology optimisation

AbstractTo move up the circular economy hierarchy steel profiles should be reused instead of recycled. To efficiently utilise the limited availability of reclaimed steel elements in new structures, a different approach is required: form follows availability. This paper presents an algorithm for generating truss bridge designs using only reclaimed steel, which optimises material efficiency and avoids cut‐off waste. The algorithm does not require an initial design or a grid of possible positions like the commonly used ground structure method. Instead, it lets a truss geometry grow from start to end. The algorithm was developed using Python in Grasshopper, resulting in a user‐friendly parametric design process. By defining the width and span of a truss bridge and providing a stock of reclaimed elements, a solution cloud of locally optimal truss bridge designs in terms of capacity utilisation is generated, which comply to Eurocode provisions. An optimal design can be selected with given objectives, e.g. the lowest environmental impact. A case study showed that the developed growth method can lead to a truss with 63% less embodied carbon than conventionally designed new steel trusses and 17% less embodied carbon than optimised standard truss designs with retrofitted reclaimed steel elements.

Application of Quality Risk Assessment and Design of Experiment in Optimizing Chromatographic Method for Estimation of Total Sarsasapogenin from the Roots of <i>Asparagus racemosus</i> Wild

The roots of the plant Asparagus racemosus (Wild) (family: Liliaceae) are used alone as a lactogenic and general tonic or incorporated as a major ingredient in many herbal and Ayurveda formulations. Shatavarin glycosides containing sarsasapogenin as aglycone were found bioactive, and present in predominantly higher amount. The experiments were planned to develop an analytical method for estimation of total sarsasapogenin from the extract using HPTLC, through a systematic approach after identifying the potential failure modes, termed as critical process parameters to achieve the analytical target profile. The associated risks were mitigated by studying the combined effect of the critical process parameters on area, Rf, and tailing factor corresponding to the sarsasapogenin peak, assigned as critical method attributes. The optimization studies were carried out through Design of Experiment approach and process parameters were finalized to achieve analytical target profile. The developed method could resolve the peak of sarsasapogenin from the hydrolyzed methanolic extract prepared from the root powder using silica gel-backed pre-coated aluminum plates. The optimized mobile phase was Hexane:Ethyl acetate:Formic acid (7.0:1.9:0.35, v/v/v); the plates were subjected to post-chromatographic derivatization using 5% v/v hydrochloric acid solution, and heating of the plats for 3 min at 110˚C. The chromatogram was recorded by scanning the plates in TLC scanner using fluorescence mode at 366 nm. The developed analytical method was found to yield linear response in the range of 40-280 ng/spot. The limit of quantification was determined to be 35.88 ng/spot. The method was found selective, accurate, and robust. The amount of sarsasapogenin was found to be determined using the developed method and found to be 2.31% w/w on dried weight basis from the dried root powder. This is the first report of a systematic approach in estimating sarsasapogenin from Asparagus sp.

Recommendation of SLM Process Parameters Based on Analytic Hierarchy Process and Weighted Particle Swarm Optimization for High-Temperature Alloys.

Selective laser melting (SLM) of high-temperature alloys involves intricate interdependencies among key process parameters, such as laser power and scanning speed, affecting properties such as density and tensile strength. However, relying solely on experiential knowledge for process parameter design often hampers the precise attainment of target requirements. To address this challenge, we propose an innovative approach that integrates the analytic hierarchy process (AHP) and weighted particle swarm optimization (WPSO) to recommend SLM process parameters for high-temperature alloy fabrication. Our proposed AHP-WPSO model consists of three main steps. First, a comprehensive historical database is established, capturing the process parameters and performance metrics of high-temperature alloy SLM parts. Utilizing an AHP framework, we compute the performance similarity between target and historical cases, applying rational thresholds to identify analogous cases. When suitable analogs are elusive, the model seamlessly transitions to the second step. Here, the WPSO model optimizes and recommends process parameters according to target specifications. Lastly, our experimental validation of the GH4169 high-temperature alloy through SLM experiments corroborates the effectiveness of our AHP-WPSO model in making process parameter recommendations. The outcomes underscore the model's high accuracy, attaining a recommendation precision of 99.81% and 96.32% when historical analogs are present and absent, respectively. This innovative approach offers a robust and reliable solution to the challenges posed in SLM process parameter optimization for high-temperature alloy applications.

Lyocell fibre production using NMMO – A simulation-based techno-economic analysis

The demand for man-made cellulosic fibres is expected to grow in the future. One commercially-available concept to supply fibres is Lyocell manufacturing from dissolving wood pulps using N-Methylmorpholine N-oxide (NMMO) as the solvent. The literature qualitatively indicates that NMMO recycling efficiency is a key factor for profitable operation. Process design information and parameter data are however poorly available publicly to illustrate the cost factors. Therefore, systematic techno-economic analysis of a 50 kt/year Lyocell plant was conducted using steady-state process simulation and cost modeling. With the simulation models, the underlying technical process design and modelling decisions, and economic assumptions were studied. NMMO makeup need is an important cost item. The simulated makeup need is very dependent on the design of the solvent recovery system and the vapor-liquid equilibrium thermodynamic model selection. On the other hand, water use, fibre washing process design, and washing model parameterization have relatively lower impact on the cost of production. Raw material cost and capital expenses are most critical cost items when the NMMO recycling efficiency is high.

A multi-material-oriented modeling framework to characterize and predict mechanical self-healing

In the family of smart materials termed as “self-healing materials”, there is a prominent number of them sharing many similarities in the way that healing is affecting their post-damaged mechanical response. In this work, a generic and multi-material phenomenological-based healing formulation is proposed to investigate and characterize the self-healing effect in the mechanical response of materials exhibiting strong nonlinearities like rate-dependent plasticity, visco-damage initiation and evolution. The proposed healing formulation uses objective experimental measures such as resting time, loading rate and damage level. This formulation is integrated in a combined computational–experimental procedure, the so-called Generic Healing-Oriented Multi-Material Modeling Framework (GHOM3) that facilitates (i) the understanding of a strongly nonlinear response, (ii) a robust material characterization and (iii) the predictive simulation via the finite element analysis. As study case, the intrinsic self-healable mechanical response of a highly nonlinear asphalt-based composite matrix is characterized at room temperature using the proposed framework. As additional novelty in the framework, an ultra-fast optimization-based material parameter identification process is developed using a master–slave parallelization paradigm that leads to save up to 90% of data processing time. The framework is put into practice to virtually predict the mechanical response influenced by the healing process of the validated material in a dog-bone specimen under Load–Unload–Resting–Reload at multiple loading rates and damage levels. The implemented approach gives direct access to the entire damage and healing histories, which are experimentally inaccessible, as well as providing an alternative definition of the healing indices commonly used in experimentation.

Current challenges and recent advances on the path towards continuous biomanufacturing.

Continuous biopharmaceutical manufacturing is currently a field of intense research due to its potential to make the entire production process more optimal for the modern, ever-evolving biopharmaceutical market. Compared to traditional batch manufacturing, continuous bioprocessing is more efficient, adjustable, and sustainable and has reduced capital costs. However, despite its clear advantages, continuous bioprocessing is yet to be widely adopted in commercial manufacturing. This article provides an overview of the technological roadblocks for extensive adoptions and points out the recent advances that could help overcome them. In total, three key areas for improvement are identified: Quality by Design (QbD) implementation, integration of upstream and downstream technologies, and data and knowledge management. First, the challenges to QbD implementation are explored. Specifically, process control, process analytical technology (PAT), critical process parameter (CPP) identification, and mathematical models for bioprocess control and design are recognized as crucial for successful QbD realizations. Next, the difficulties of end-to-end process integration are examined, with a particular emphasis on downstream processing. Finally, the problem of data and knowledge management and its potential solutions are outlined where ontologies and data standards are pointed out as key drivers of progress.

Hybrid modeling in bioprocess dynamics: Structural variabilities, implementation strategies, and practical challenges.

Hybrid modeling, with an appropriate blend of the mechanistic and data-driven framework, is increasingly being adopted in bioprocess modeling, model-based experimental design (digital-twin), identification of critical process parameters, and optimization. However, the development of a hybrid model from experimental data is an inherently complex workflow, involving designed experiments, selection of the data-driven process, identification of model parameters, assessment fitness, and generalization capability. Depending on the complexity of the process system and purpose, each piece of these modules can flexibly be incorporated into the puzzle. However, this extra flexibility can be a cause of concern to trace an "optimal" model structure. In this paper, the development of hybrid models in a common bioprocess system, selection of data-driven components and their mapping to states, choice of parameter identification techniques, and model quality assurance are revisited. The challenges associated with hybrid-model development, and corrective actions have also been reviewed. The review also suggests the lack of data, and code sharing in communal repositories can be a hurdle in the exploration, and expansion of those tools in a bioprocess system.

Process-structure-property modeling for postbuild heat treatment of powder bed fusion Ti-6Al-4V

Postbuild heat treatment is an important component in optimized manufacturing processing for laser beam powder bed fusion (PBF-LB) Ti-6Al-4V. The development of predictive modeling, based on the understanding of the relationships between process parameters, microstructure evolution, and mechanical properties, is a potentially key ingredient in this optimization process. In this paper, a process-structure-property (PSP) model is developed to predict the effect of postbuild heat treatment on yield strength, which is a key tensile property for PBF-LB Ti-6Al-4V. The process-structure part is developed with a focus on the prediction of solid-state phase transformation, especially dissolution of martensite during the heating phase. Subsequent tensile properties are quantified by a microstructure-sensitive yield strength model based on the predicted microstructure variables. The integrated PSP model is validated via experimentally measured phase fraction, α lath width and monotonic tensile tests on PBF-LB Ti-6Al-4V with different heat treatment temperatures, for identification of optimal process parameters.

Equitable design of material processing parameters by utilizing probability‐based method for multi‐objective optimization

AbstractIn this paper, the equitable design is regulated, two steps are involved in the main processes. Preliminarily, the newly proposed probability‐based method for multi‐objective optimization is used to transfer the multi‐objective problem into a single objective one in the viewpoint of probability theory; subsequently the “uniform design method” and “good lattice point” are employed to complete the discretization in the sequential optimization. Moreover, the detailed parameters design of heat treatment for steel 30CrMnSiA and the proper cutting of titanium alloy are given as examples. The experimental results show the rationality of the proposed approach reasonably.

Hydrodynamics of expanded bed adsorption studied through CFD-DEM

The hydrodynamics of the Expanded Bed Adsorption process is studied through simulations combining Computational Fluid Dynamics and the Discrete Element Method. A representative base case is defined, based on process design parameters commonly encountered in literature. Then, 19 other cases are defined, each representing a singular adjustment to the column design, material properties, or operating conditions. The parameters that are varied are the expansion factor, liquid viscosity, bed aspect ratio, mean particle density, width of the particle density distribution, width of the particle size distribution, column taper angle, and column alignment angle. The impact of each adjustment on the bed behaviour is discussed, using the local particle size distribution and solids dispersion coefficient as main indicators of bed stability. Optimal performance was found for an expansion factor of two to three, and the combination of particle size distribution and particle density distribution was found to greatly improve bed stability. The mixing process of the liquid and solid phases is concluded to be of highly complex nature, and cannot simply be predicted from the liquid flow velocity.

Stability studies for the identification of critical process parameters for a pharmaceutical production of the Orf virus

A promising new vaccine platform is based on the Orf virus, a viral vector of the genus Parapoxvirus, which is currently being tested in phase I clinical trials. The application as a vaccine platform mandates a well-characterised, robust, and efficient production process. To identify critical process parameters in the production process affecting the virus’ infectivity, the Orf virus was subjected to forced degradation studies, including thermal, pH, chemical, and mechanical stress conditions. The tests indicated a robust virus infectivity within a pH range of 5–7.4 and in the presence of the tested buffering substances (TRIS, HEPES, PBS). The ionic strength up to 0.5 M had no influence on the Orf virus’ infectivity stability for NaCl and MgCl2, while NH4Cl destabilized significantly. Furthermore, short-term thermal stress of 2d up to 37 °C and repeated freeze-thaw cycles (20cycles) did not affect the virus’ infectivity. The addition of recombinant human serum albumin was found to reduce virus inactivation. Last, the Orf virus showed a low shear sensitivity induced by peristaltic pumps and mixing, but was sensitive to ultrasonication. The isoelectric point of the applied Orf virus genotype D1707-V was determined at pH3.5. The broad picture of the Orf virus’ infectivity stability against environmental parameters is an important contribution for the identification of critical process parameters for the production process, and supports the development of a stable pharmaceutical formulation. The work is specifically relevant for enveloped (large DNA) viruses, like the Orf virus and like most vectored vaccine approaches.

NEW OPPORTUNITIES AND BENEFITS IN THE PRODUCT DEVELOPMENT PROCESS USING THE MACHINE LEARNING BASED DIRECT INVERSE METHOD FOR MATERIAL PARAMETER IDENTIFICATION

AbstractFinite element (FE) simulations can be used both in the early product development phase to evaluate the performance of developed components as well as in later stages to verify the reliability of functions and components that would otherwise require a large number of physical prototype tests. This requires calibrated material cards that are capable of realistically representing the specific material behavior. The necessary material parameter identification process is usually time-consuming and resource-intensive, which is why the direct inverse method based on machine learning has recently become increasingly popular. Within the neural network (NN) the generated domain knowledge can be stored and retrieved within milliseconds, which is why this method is time and resource-efficient. This research paper describes advantages and potentials of the direct inverse method in the context of the product development process (PDP). Additionally, arising transformation opportunities of the PDP are discussed and an application scenario of the method is presented followed by possible linkage potentials with existing development methods such as shape optimization.

Statistical modelling and optimization of FS-welded 6061-T651 Aluminum alloy

An RSM-based experimental design, mathematical modelling and statistical optimization of friction stir welding process parameters was studied. A quadratic fitting model developed from a five-leveled four-factor parametric setting predicted the Ultimate Tensile Strength (UTS) of the welded AA6061- T651 joints. Statistical analysis at 95% Confidence Interval using ANOVA validated the conformity of the developed model with experimental data and also verified the adequacy of the model for UTS prediction and optimization. Results showed that the model was statistically significant (p<0.0001) with no notable lack of fit with the four parameters and their squared terms also significant statistically. The numerical optimization resulted to an optimum UTS of 166.32MPa from rotational speed, traversing speed, tool tilt angle and axial load values of 1293.641rpm, 48.467mm/min, 1.888° and 4.720kN, respectively with a desirability of 0.944. Also, 2D contour and 3D surface plots showed that the four parameters made decreasing effects on the UTS after reaching their optimized UTS. Driving forces for high UTS were: sufficient heat generation for plastic deformation, effective material coalescence, appropriate extrusion of molten material towards the trailing edge, adequate heat and mass transfer to control grain coarsening, void and flash formations. With an SN-ratio of 45.963 and low coefficient-of-variation of 1.11%, the conformity of the predicted and adjusted regression coefficients (R²) of 0.9619 and 0.9868 respectively supported by the confirmatory test and diagnostic plots showed a strong correlation between the experimental and predicted results. These demonstrated that the developed model was sufficient for predicting and optimizing the UTS of Friction Stir Weld (FSW) AA6061T651 plates.

Efficiency optimization method and accurate power loss evaluation method of 48 V/12 V resonant DC–DC converter

With the rapid development of artificial intelligence, the rack load power is constantly increasing. The 48 V/12 V converter which is suitable for the two-stage step-down structure of 48 V distribution system has received much attention. In this paper, the efficiency optimization scheme for 48 V/12 V three-phase resonant switched-capacitor-converter is proposed. The component selection method is considered under light load and heavy load conditions. An accurate loss model is also proposed for the converter, and the model is accurate. Parameter design process is given and three experiments are designed to verify the correctness of the optimization method. The highest power convert efficiency (PCE) achieved by the 48 V/12 V experimental prototype with the maximum load power of 144 W is 98.97%.

Multi-fidelity data-driven optimization design framework for self-piercing riveting process parameters

Due to its excellent performance, the self-piercing riveting (SPR) process has been widely used in auto body manufacturing in recent years. The need for efficient and reliable design of SPR process parameters is growing in industry. Traditional trial-and-error design methods rely on historical experience and can be limited by physical experimental materials and equipment. Data-driven methods rely on data accuracy and data volume, and data from a single source is often inadequate to meet design needs. To address these issues, a multi-fidelity data-driven optimization design framework is proposed in this paper. The most significant feature of this framework is the fusion of physical experiment data and simulation data to build surrogate models. In this framework, a modified optimal Latin hypercube sampling method and a multi-fidelity surrogate model based on transfer learning and neural networks are proposed for SPR process. This multi-fidelity surrogate model can use a very small amount of experiment data to modify the surrogate model built on simulation data based on transfer learning, so that the model predictions can more closely match the physical experiment results at a lower modeling cost. Benefit from this method, the proposed framework can balance the contradiction between design accuracy and development cost compared to a single-fidelity data-driven framework. The application cases show that the prediction errors of the multi-fidelity models are <0.1 mm for the key geometric parameters of the SPR joint. As verified by physical experiments, the rivets and dies selected by the framework are the optimal solutions within the optional range. To the authors' knowledge, this is the first time that a multi-fidelity modeling method has been introduced to the field of SPR process, to solve the process parameter optimization design problem. Further, the method is not limited to the use of SPR process but can be applied as a paradigm in other engineering optimization design problems, especially in joining process parameter design problems.