Parameter Identification Process Research Articles

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.

Design and Development of Oxyclozanide Chewable Tablet Formulation Employing Quality by Design Approach

Abstract: Background: The focus of this research was to identify undiscovered knowledge associated with the production of oxyclozanide tablets utilizing Quality by Design (QbD) in order to develop an ideal formulation that would guarantee constant product quality. The modern approach to formulation design and optimization essentially entails QbD which is a systemic method of pharmaceutical development and comprises of the design and development of formulations as well as manufacturing processes to meet the target product quality. Materials and Methods: Pre-formulation studies on excipient flow properties, compactibility and tabletability profiles, identification of Critical Process Parameters (CPP) and Critical Quality Attributes (CQA) were carried out. An experimental design was adopted to investigate the effect of formulation and process variables (compression force, super-disintegrant type and concentration) on the CQAs using Modde Pro 12.1. The obtained results were used to generate a Design Space (DS) based on the study data. Results: Preformulation studies carried out on the core excipients used, gave an understanding to their tabletability and compactibility. Process parameters such a compaction force and formulation variables such as super-disintegrant concentration were studied within the framework of Quality by Design (QbD) and the new optimized formulation derived was tested and confirmed to be within the design space generated. Conclusion: At the end of the study, the aim was achieved; which was to implement pre-formulation studies and QbD to design a formulation containing Oxyclozanide (API) with a combination of excipients. Keywords: Quality by Design (QbD), Oxyclozanide, Chewable tablet, Compactibility, Tabletability.

A precision-drive hysteresis model with an equal-density weight function for GMA feedforward compensation

Giant magnetostrictive actuators (GMAs) are a widely used type of micro-nano actuator, and they are greatly significant in the field of precision engineering. The accuracy of a GMA often depends on its hysteresis model. However, existing models have some limitations, including the difficulty of identifying their parameters and the tradeoff between the quantity of modeling data required and the level of precision achieved. To solve these problems, in this paper, we propose a Preisach inverse model based on equal-density segmentation of the weight function (E-Preisach). The weight function used to calculate the displacement is first discretized. Then, to obtain a finer weight distribution, the discretized geometric units are uniformly divided by area. This can further minimize the output displacement span, and it produces a higher-precision hysteresis model. The process of parameter identification is made easier by this approach, which also resolves the difficulty of obtaining high precision using a small amount of modeling data. The Preisach and the E-Preisach inverse models were investigated and compared using experiments. At frequencies of 1 and 5 Hz, it was found that the E-Preisach inverse model decreases the maximum error of the feedforward compensation open-loop control to within 1 µm and decreases the root-mean-square error in displacement to within 0.5 µm without the need to increase the number of measured hysteresis loops. As a result, the E-Preisach inverse model streamlines the structure of the model and requires fewer parameters for modeling. This provides a high-precision modeling method using a small amount of modeling data; it will have applications in precision engineering fields such as active vibration damping and ultra-precision machining.

An Intuitive and Noniterative Design Methodology for CLLC Chargers Employing Simplified Operation Modes Model

This paper mainly focuses on the simplified operation modes (SOM) model and resonant parameter design for the CLLC charger. Based on the mathematical and detailed operation waveform assumptions, the voltage gain model expressions and the operation mode boundaries are calculated directly, providing the high efficiency and high reliability of the CLLC converter. The proposed SOM model is more accurate in depicting the voltage gain compared with the conventional fundamental harmonic approximation (FHA) model. Moreover, the SOM model is more intuitive and has less computational complexity than the complicated and unsolvable time-domain model. As for the parameter design process, the inductance ratio <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> and characteristic impedance <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Z_{0}$</tex-math></inline-formula> are selected instead of specific inductances and capacitances. Relying on the SOM model, a step-by-step parameter design methodology is studied, which avoids repetitive iterations and streamlines the procedure. The voltage gain range, efficiency, soft-switching operation, mode boundaries, and system stability are considered comprehensively and realized in this process. The simulations and experiments validate that the proposed SOM model is accurate, and the design methodology is straightforward through a 1-kW CLLC charger prototype with 97% peak efficiency.

Time-Domain Analysis and Optimal Design of LLC-DC Transformers (LLC-DCXs) Considering Discontinuous Conduction Modes

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLC</i> -DC transformers ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLC</i> -DCXs) operate under fixed-frequency and open-loop conditions to achieve high efficiency and are promising converters that can be used in the auxiliary power units (APUs) of electric locomotives. An optimal design method of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLC</i> -DCXs is proposed in this paper. First, the time-domain expressions of the symmetric half-bridge <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLC</i> resonant converter with split resonant capacitors are derived. A current increment method is proposed to determine the expressions. Second, the discontinuous conduction mode (DCM) and voltage gain limiting conditions are used to obtain the magnetizing inductance ranges. The converter losses and zero-voltage-switching (ZVS) ranges are considered to determine the final magnetizing inductance. A 10 kW prototype is used as an example, and a detailed parameter design process is implemented. Compared with conventional design methods for the magnetizing inductance, the proposed method can effectively determine the maximum magnetizing inductance for the DCM operation of the converter. The effectiveness of the proposed method is proven by the simulations and experimental results obtained for the 10 kW <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLC</i> -DCX prototype. The maximum efficiency of the designed converter is 98.12% under the full load.

Study on the Effect of Inter-Layer Cooling Time on Porosity and Melt Pool in Inconel 718 Components Processed by Laser Powder Bed Fusion.

This paper investigates the effects on the material microstructure of varying the Inter-Layer Cooling Time (ILCT) during the printing process in laser powder bed fusion (L-PBF) multi-laser machines. Despite these machines allowing higher productivity rates compared to single laser machines, they are affected by lower ILCT values, which could be critical for material printability and microstructure. The ILCT values depend both on the process parameter sets and design choices for the parts and play an important role in the Design for Additive Manufacturing approach in L-PBF process. In order to identify the critical range of ILCT for this working condition, an experimental campaign is presented on the nickel-based superalloy Inconel 718, which is widely used for the printing of turbomachinery components. The effect of ILCT on the microstructure of the material is evaluated in terms of porosity and melt pool analysis on printed cylinder specimens, considering ILCT decreasing and increasing in the range of 22 to 2 s. The experimental campaign shows that an ILCT of less than 6 s introduces criticality in the material microstructure. In particular, at an ILCT value of 2 s, widespread keyhole porosity (close to 1‱) and critical and deeper melt pool (about 200 microns depth) are measured. This variation in melt pool shape indicates a change in the powder melting regime and, consequently, modifications of the printability window promoting the expansion of the keyhole region. In addition, specimens with geometry obstructing the heat flow have been studied using the critical ILCT value (2 s) to evaluate the effect of the surface-to-volume ratio. The results show an enhancement of the porosity value (about 3‱), while this effect is limited for the depth of the melt pool.

From crack-prone to crack-free: Eliminating cracks in additively manufactured Ti-48Al-2Cr-2Nb alloy by adjusting phase composition

At present, it remains a challenge to additively manufacture Ti-48Al-2Cr-2Nb (TiAl4822) alloy by laser powder bed fusion (LPBF) technology because of the severe cracking in the building process. In this work, it was found that cracks in LPBFed TiAl4822 alloy were more likely to occur in the region near the interfaces between α2 and B2 phases, and the underlying mechanism was addressed. This leads to a strategy to reduce or eliminate these cracks via reducing the phase interfaces between α2 and B2 phases by adjusting its phase composition. Through the design of LPBF process parameters, the purpose of adjusting phase composition of LPBFed TiAl4822 alloy in a wide range (from nearly α2 single-phase, to α2 + B2 + γ, and then to nearly B2 single-phase) was realized. As expected, with the reduction of the interfaces between α2 and B2 phases, the cracking behavior has been significantly alleviated. The exciting result was that the crack-free sample could be achieved in the LPBFed TiAl4822 alloy with nearly α2 single-phase structure. Its compressive strength was measured to be 1460.7 MPa comparable to that of its casting counterpart. The present work makes it possible to additively manufacture crack-free TiAl4822 alloy by tuning phase composition.

Optimization of precision molding process parameters of viscoelastic materials based on BP neural network improved by genetic algorithm

Micro-structured optical components (MOC) are popular due to their unique optical and mechanical properties. Glass molding processing (GMP) is currently the main method of MOC manufacturing. In GMP, the process parameters determine the efficiency and quality of the glass processing. However, the optimization of their multi-objective molding process parameters has not been studied comprehensively enough. In this paper, a thermocompression model of a viscoelastic material and a mold is developed using coupled thermal-structural analysis in Abaqus. In addition, the data was analyzed to investigate the influence of process parameters on GMP performance. Then, a BP neural network model was built, and the model was trained and tested. A prediction model that can reasonably reveal the relationship between the process parameters and the molding effect was obtained. A multi-objective optimization algorithm GA was used to optimize the design of the hot-pressing process parameters. At last, a set of process parameters with the best forming effect was obtained. This study provides valuable insights into optimizing glass molding process parameters for practical production.

Influence of Different Material Properties on Cold Working of AgSnO2 Electrical Contact Material

Based on the finite element software, a calculation model of cold working forming (pier thickness) of rivet type electrical contact materials was established, and the effects of different material properties (elongation, elastic modulus and hardness) on the cold working of AgSnO2 electrical contact materials were analyzed. The proposed simulation technology fills the gap in the force analysis method of silver alloy rivets, and can provide certain technical theory and process parameter design basis for rivet deformation control.

A study on the parametric optimization of drawing metal stamping process for aluminum alloy tailgate parts using response surface methodology

AbstractThe tailgate‐extension in a vehicle is a part that connects the upper and lower parts of the tailgate. Since this part does not require high strength, aluminum must be used in order to reduce the weight of the vehicle. However, when choosing aluminum, it is difficult to satisfy the shape quality with the existing manufacturing process. Therefore, this study was conducted for the purpose of optimizing the draw metal stamping process for the development of tailgate extensions for vehicles using aluminum. The response surface methodology was utilized to optimize the draw metal stamping process. The process design parameters were established as blank holding force, coefficient of friction and die speed. Finally, the reliability of the optimization process and finite element analysis was secured by conducting field experiments to review the derived optimal process conditions.

Numerical and experimental investigations of mechanical properties of AW 6005-T6 Aluminium alloy butt weld joint using GMAW process

This study aimed to investigate the effect of the welding heat input on the heat affected zone (HAZ) of AW 6005-T6 aluminium alloy for a butt-welded joint using gas metal arc welding. The determination of the thermal cycles, metallography, and the resulting mechanical properties in the zone makes its possible. The study involved using a welding experiment, numerical simulation, physical simulation, and mechanical tests. The welding was carried out using the pulsed gas metal arc welding (GMAW) transfer and type J thermocouples were used to develop the thermal cycles in the HAZ. Simufact® Welding was utilized for the numerical simulation. Optical microscope was used to evaluate the microstructures and Vickers microhardness test was done along the weld cross-section. The HAZ was located on the weld cross-section with a mean hardness of 63.7 HV0.1, which is considerably lower when compared with the base metal (BM) which has a hardness of 100 HV0.1. This indicates thermal softening occurred due to the heat input to the material. There is a match in the hardness values of the Gleeble samples and the locations on the weld cross section suggested by the model showing validity of the simulation. It is important to note the fact that there is an influence of heat input into aluminum AW 6005-T6 weld joints and its mechanical properties in the design of welding process parameters for automotive parts. The welding parameters can be optimized to decrease the heat input into the weld, as this can directly affects the mechanical properties in the HAZ.

Optimal shape design using machine learning for wind energy and pressure

As energy consumption presents exponential growth, topics on sustainable design and energy generation are brought to the forefront. This paper presents procedures and methods suggest the initial designs for a shell pavilion that generates energy. Thus, optimal strategies incorporating a wind turbine that maximizes wind speed and structural integrity were proposed. Computational Fluid Dynamics (CFD) was used to find wind pressure around the thin shell pavilion. Also, the paper adopted the parametric design process to find an optimal form for the design. The main obstacle to running CFD simulation for parametric design is the heavy computational load and time. To achieve Multiple Objective Optimization (MOO) with less computational effort, we created a data-driven framework that engages Artificial Neural Network (ANN) and Genetic Algorithm (GA), which predicted both wind speed around the turbine and structural behaviors. These performance values were fed into optimization algorithm to find the form that satisfied the goal of this study. The results demonstrate that the proposed methods find various shapes optimized for different materials. Moreover, several optimal designs were identified based on various preferences. Especially by considering energy generation and structural integrity equally, the optimal pavilion can generate a wind speed of 14.73 m/s with a shell thickness of 1.27 cm when constructed using steel. The main contribution of this paper is integrating the Machine Learning method and advanced wind pressure calculation with CFD to find optimal designs that are more comprehensive than typical.

Universal Virtual Synchronous Generator Based on Extended Virtual Inertia to Enhance Power and Frequency Response

Virtual synchronous generators (VSG) are considered a new generation of grid-friendly inverters with the same inertial support characteristics as synchronous generators (SG). However, the inertia support comes with the side effects of power and frequency response deterioration. The existing VSG power oscillation suppression methods have limitations such as complex controller parameter tuning, altering the frequency support characteristics, and power quality degradation. To address these issues, this paper proposes a universal VSG control strategy based on extended virtual inertia (VSG-EVI). Herein, the virtual inertia is no longer a constant or varying number; it is endowed with frequency domain characteristics to improve the VSG transient responses. Moreover, a detailed parameter design process is given in detail. Compared with the conventional VSG (CVSG), VSG-EVI significantly suppress the power and frequency oscillations. Compared with the existing VSG oscillation suppression methods, VSG-EVI addresses the issues of the reduction of frequency support capability, whereas the controller parametric design process is simplified with the proposed intuitive extended virtual inertia. Finally, the proposed VSG-EVI method is thoroughly validated by experiments under both grid-connected and stand-alone modes.

An active equalization method for series-parallel battery pack based on an inductor

The equalization topologies based on inductive energy storage have high equalization accuracy and perfect functionality, but often have more complex structure and control method. To overcome this problem, an active equalization method based on an inductor is proposed for the series-parallel battery pack. The energy storage device responsible for energy transfer requires only one inductor and the topology is simple and low cost. Combining diodes and MOSFETs to form a switching array reduces the cost of the equalization topology while increasing the fault tolerance of the control signal and making equalization control simpler. First, the working principle and parameter design process of the topology are described, and the control strategy adapted to it is proposed on this basis. Second, based on the simulation models, the variation law of the efficiency and speed of the proposed equalization method influenced by inductance and switching frequency is analyzed, and by comparing the new equalization method with similar methods, it is verified that the new equalization method is simple in structure, low in cost, and convenient in control. Final, by building an experimental platform for “four-series and two-parallel” battery pack, the effectiveness of the new equalization method is verified.

Multi-Objective Parametric Optimization Design for Mirrors Combined with Non-Dominated Sorting Genetic Algorithm

The process of intelligent multi-objective parametric optimization design for mirrors is discussed in detail in this paper, with the error of the mirror surface shape and the total mass being examined as the optimization objectives. The establishment of complex objective functions for solving the optimization problem of the mirror surface shape error was realized, and manual modification of the model was avoided. Moreover, combining this with a non-dominated sorting genetic algorithm (NSGA) helped the Pareto front move towards an ideal optimal set of solutions. To verify the effectiveness of the proposed method, an aluminum alloy mirror with an aperture of 140 mm was taken as an example. The Pareto optimal solution set of the mass and surface shape error under 1 g gravity was obtained for finding the required solution and satisfying the optimization goal. In addition, this method is applicable to other complex structural design problems.

An Active Equalization Method Based on an Inductor and a Capacitor for Series Battery Pack

Combining the characteristics of the high precision of inductive energy storage equalization and the fast speed of capacitive energy storage equalization, an active equalization method is proposed for a series battery pack based on an inductor and capacitor. The energy storage devices responsible for energy transfer have only one inductor and one capacitor. First, we propose the topology, analyze the working principle, and elaborate on its parameter design process. Second, an adaptive equalization control strategy matching the topology is designed. Third, based on the simulation models, the speed and efficiency characteristics of the new equalization method are verified, and the significant cost and volume advantages and the simple control characteristics are analyzed in detail. Finally, an experimental platform was built for a four-cell series battery pack to verify the effectiveness of the new equalization method for the battery pack charging and discharging process. The voltage difference is less than 80 mV.