Robust Design Process Research Articles

System model driven selection of robust tablet manufacturing processes based on drug loading and formulation physical attributes

Mechanistic process modeling presents an opportunity to reduce experimental burden, enabling relationships between process parameters and product attributes to be mapped out using in-silico experiments. A system model of a pharmaceutical tablet manufacturing process comparing dry granulation with direct compression is developed to answer key material and process design questions. The system model links API physical properties and formulation to process parameters to map out the robust operating space. To demonstrate the application of the model, several drug product formulation design questions were considered:•Which processing route is the most robust given the API material properties and dosage requirements?•How does drug loading and tablet size impact the robustness of the manufacturing process?•What process settings are required for a robust manufacturing route for the API material properties and drug loading requirements?A computational framework was developed using the system models to generate process classification and design space maps to aid robust pharmaceutical formulation and process decision making. Process classification maps were produced to assess the feasibility of roller compaction and direct compression for different material properties and formulations. Constraints on the critical quality attributes of the intermediate and final products were defined using the Manufacturing Classification System. Design space maps presented here demonstrate how system models can be used to support formulation and process design. The design space maps illustrate how the process operating space can be increased or decreased as the API mass fraction is varied.The process design and selection system model demonstrate how an understanding of the API physical properties can be used to model the impact of formulation and process design. Furthermore, these models can be instrumental in the dialogue with colleagues developing the API in order to set the requirements of the API physical properties to ensure successful and robust formulation and process designs.

SPECIAL CHARACTERISTICS AS A KEY FACTOR IN DEVELOPMENT OF A ROBUST PRODUCTION PROCESS IN THE AUTOMOTIVE INDUSTRY

A critical, key or special characteristic is any of the characteristics of the product or production process throughout the life cycle, which if not in accordance with the specifications can cause a defectiveness or failure with serious consequences to life and limb, or it may result with non-conformities related to Legal and Regulatory requirements, as well as with the product’s functionality. A special characteristic (SC) can be a feature of a material, production process or part, such as dimension, tolerance limit or finishing, the variation of which within the specified tolerance has a significant impact on safety, compliance with regulations, assembly or functionality of the product. Due to the seriousness of the consequences that the special characteristics would cause if not under controlled state, their identification, classification, and implementation is planned while the product is at an early stage of its development. Their timely identification and correct classification enables the earliest detection of potential defects and uncon-formities which will contribute to the development of robust design and robust production process. In this research, through theoretical analysis accompanied with practical example from the operation of Manufacturers in automotive industry will be presented the process of identification, classification, implementation, documentation, control and archiving of special characteristics throughout the lifecycle of the product.

Multilayer Machine Learning-Assisted Optimization-Based Robust Design and Its Applications to Antennas and Array

An efficient multilayer machine learning-assisted optimization (ML-MLAO)-based robust design method is proposed for antenna and array applications. Machine learning methods are introduced into multiple layers of the robust design process, including worst-case analysis (WCA), maximum input tolerance hypervolume (MITH) searching, and robust optimization, considerably accelerating the whole robust design process. First, based on a surrogate model mapping between the design parameters and performance, the WCA is performed using a genetic algorithm to ensure reliability. MITH searching is then carried out using an MLAO-based framework to find the MITH of the given design point. Next, based on the training set obtained using MITH searching, correlations between the design parameters and the MITH are learned. The robust design is carried out using surrogate models for both the performance and the MITH, and these models are updated online following the ML-MLAO scheme. Finally, two examples, including an array synthesis problem and an antenna design problem, are used to verify the proposed ML-MLAO method. The numerical results and computation time are discussed to demonstrate the effectiveness of the proposed method.

Customized product development supported by integrated information

Information systems are key enablers for the integration and reliable management of the product development process. Fast, robust, and cost-efficient product adaptation is especially important in one-of-a-kind production. This paper presents an implementation of information supported tools of the product development (PD) design process for large power transformers. One-of-a-kind production is specific, as each product must be customized, wherefore a robust design process well supported by information technologies (IT) plays a key role in creating a digital twin and the product's final value. Goal of this research was to develop the product information model and smart supporting tools for customization and integrate them into the design process. Based on a systematic analysis of the sample company, this paper proposes a model for the complete renewal of information systems and of working methodology, where reorganization is demonstrated in an increase of overall effectiveness. The results clearly show a considerable drop in engineering changes, increased productivity and improved business competitiveness. The proposed framework is generalized, which makes it directly applicable in similar business environments and thus helpful for establishing the best-practice guidelines for promoting competitiveness in one-of-a-kind PD processes.

Application of Intelligent Modeling Method to Optimize the Multiple Quality Characteristics of the Injection Molding Process of Automobile Lock Parts.

This study focuses on applying intelligent modeling methods to different injection molding process parameters, to analyze the influence of temperature distribution and warpage on the actual development of auto locks. It explores the auto locks using computer-aided engineering (CAE) simulation performance analysis and the optimization of process parameters by combining multiple quality characteristics (warpage and average temperature). In this experimental design, combinations were explored for each single objective optimization process parameter, using the Taguchi robust design process, with the L18 (21 × 37) orthogonal table. The control factors were injection time, material temperature, mold temperature, injection pressure, packing pressure, packing time, cooling liquid, and cooling temperature. The warpage and temperature distribution were analysed as performance indices. Then, signal-to-noise ratios (S/N ratios) were calculated. Gray correlation analysis, with normalization of the S/N ratio, was used to obtain the gray correlation coefficient, which was substituted into the fuzzy theory to obtain the multiple performance characteristic index. The maximum multiple performance characteristic index was used to find multiple quality characteristic-optimized process parameters. The optimal injection molding process parameters with single objective are a warpage of 0.783 mm and an average temperature of 235.23 °C. The optimal parameters with multi-objective are a warpage of 0.753 mm and an average temperature of 238.71 °C. The optimal parameters were then used to explore the different cooling designs (original cooling, square cooling, and conformal cooling), considering the effect of the plastics temperature distribution and warpage. The results showed that, based on the design of the different cooling systems, conformal cooling obtained an optimal warpage of 0.661 mm and a temperature of 237.62 °C. Furthermore, the conformal cooling system is smaller than the original cooling system; it reduces the warpage by 12.2%, and the average temperature by 0.46%.

Digital Medical Device Companion (MyIUS) for New Users of Intrauterine Systems: App Development Study.

BackgroundWomen choosing a levonorgestrel-releasing intrauterine system may experience changes in their menstrual bleeding pattern during the first months following placement.ObjectiveAlthough health care professionals (HCPs) can provide counseling, no method of providing individualized information on the expected bleeding pattern or continued support is currently available for women experiencing postplacement bleeding changes. We aim to develop a mobile phone–based medical app (MyIUS) to meet this need and provide a digital companion to women after the placement of the intrauterine system.MethodsThe MyIUS app is classified as a medical device and uses an artificial intelligence–based bleeding pattern prediction algorithm to estimate a woman’s future bleeding pattern in terms of intensity and regularity. We developed the app with the help of a multidisciplinary team by using a robust and high-quality design process in the context of a constantly evolving regulatory landscape. The development framework consisted of a phased approach including ideation, feasibility and concept finalization, product development, and product deployment or localization stages.ResultsThe MyIUS app was considered useful by HCPs and easy to use by women who were consulted during the development process. Following the launch of the sustainable app in selected pilot countries, performance metrics will be gathered to facilitate further technical and feature updates and enhancements. A real-world performance study will also be conducted to allow us to upgrade the app in accordance with the new European Commission Medical Device legislation and to validate the bleeding pattern prediction algorithm in a real-world setting.ConclusionsBy providing a meaningful estimation of bleeding patterns and allowing an individualized approach to counseling and discussions about contraceptive method choice, the MyIUS app offers a useful tool that may benefit both women and HCPs. Further work is needed to validate the performance of the prediction algorithm and MyIUS app in a real-world setting.

SMALL AND MEDIUM-SIZED ENTERPRISES IN REPUBLIC OF MOLDOVA

The paper examines the role of SME sector in Moldova’s economy and its likely implications in the RD processes. Several key features of the relationship between knowledge economy, RD and SMEs are emphasized, and also their effect on regional and local communities. Then, a summary of RD particular features in Moldova is presented. The role of SME sector, examined in terms of its performance by ownership and activity types, as a whole and by main firms’ categories (micro-, small, and medium-sized) is analyzed. Small enterprises have been considered as the most adequate solution for promoting RD in general case. National-level analysis is completed by regional insight into the sector as a whole. Finally, some critical aspects of SMEs’ role in RD in Moldova are discussed.

Tolerance analysis of the volumetric error of heavy-duty machine tool based on interval uncertainty

Tolerance design is one of the main stages in the robust design process of machine tool. For heavy-duty machine tools, traditional tolerance analyses based on probabilistic methods may not be suitable because it is generally difficult and costly to obtain the accurate probability distribution of error by the limited geometric error measurements in large dimension. In this paper, a new tolerance analysis method for heavy-duty machine tools is proposed based on the interval uncertainty. Considering independent geometric error components have certain bounds but accurate distributions are unknown, the interval theory is introduced to the kinematic modeling of the static volumetric error of heavy-duty machine tools based on the multi body system (MBS) and homogeneous transformation matrix (HTM). Geometric error components are described in forms of interval numbers to extend the error model to the interval number system. The interval extension of the volumetric error function is further modified so that the variation interval of the volumetric error is thus accurately evaluated by the interval arithmetic. Comparative studies on tolerance analyses of different typical probabilistic/non-probabilistic methods are conducted through numerical simulations. It is found that under the given computational condition, the tolerance analysis based on the interval uncertainty can accurately evaluate the variation range of the volumetric error.

Gasification of Biomass in Supercritical Water, Challenges for the Process Design—Lessons Learned from the Operation Experience of the First Dedicated Pilot Plant

Gasification of organic matter under the conditions of supercritical water (T > 374 °C, p > 221 bar) is an allothermal, continuous flow process suitable to convert materials with high moisture content (<20 wt.% dry matter) into a combustible gas. The gasification of organic matter with water as a solvent offers several benefits, particularly the omission of an energy-intensive drying process. The reactions are fast, and mean residence times inside the reactor are consequently low (less than 5 min). However, there are still various challenges to be met. The combination of high temperature and pressure and the low concentration of organic matter require a robust process design. Additionally, the low value of the feed and the product predestinate the process for decentralized applications, which is a challenge for the economics of an application. The present contribution summarizes the experience gained during more than 10 years of operation of the first dedicated pilot plant for supercritical water gasification of biomass. The emphasis lies on highlighting the challenges in process design. In addition to some fundamental results gained from comparable laboratory plants, selected experimental results of the pilot plant “VERENA” (acronym for the German expression “experimental facility for the energetic exploitation of agricultural matter”) are presented.

Simulation of Methoxy methyl heptane (MMH) production using unisim design R460.1

Various fuel additives are employed till now like ethanol, ethyl tertiary butyl ether (ETBE), MTBE, Di Isopropyl alcohol (DIPA) etc. after the phase-out of lead-based compounds to enhance the octane number of fuel. MMH is suggested as an alternative to methyl tertiary butyl ether (MTBE) and similar ethers since they causes groundwater contamination. The liquid phase reversible reaction of methanol (MHOH) with 2-methyl1-heptene (MH) produces MMH. But there is an undesirable reaction also to form Dimethyl ether (DME) and 2-methyl-2-heptanol (MHOH). The process of MMH production is simulated using Unisim Design software R460.1. Conventionally, MMH is produced through a reactor and three distillation column sequences for yielding high purity MMH. This reactor-column sequence requires either high MH recycle or large-sized reactor to have the desired yield. Both results in large capital and operating costs. A reactive distillation concept can tackle these constraints with reactor and distillation in a single column known as reactive distillation column (RDC).The feasibility of RDC and analysis of the process can be observed using Unisim Design software since pilot plants are costly for this type of RD process. The simulation of the process using different test bed Unisim R460.1 is not explored yet.

Can terminal settling velocity and drag of natural particles in water ever be predicted accurately?

Abstract. Natural particles are frequently applied in drinking water treatment processes in fixed bed reactors, fluidised bed reactors, and sedimentation processes to clarify water and to concentrate solids. When particles settle, it has been found that, in terms of hydraulics, natural particles behave differently when compared to perfectly round spheres. To estimate the terminal settling velocity of single solid particles in a liquid system, a comprehensive collection of equations is available. For perfectly round spheres, the settling velocity can be calculated quite accurately. However, for naturally polydisperse non-spherical particles, experimentally measured settling velocities of individual particles show considerable spread from the calculated average values. This work aims to analyse and explain the different causes of this spread. To this end, terminal settling experiments were conducted in a quiescent fluid with particles varying in density, size, and shape. For the settling experiments, opaque and transparent spherical polydisperse and monodisperse glass beads were selected. In this study, we also examined drinking-water-related particles, like calcite pellets and crushed calcite seeding material grains, which are both applied in drinking water softening. Polydisperse calcite pellets were sieved and separated to acquire more uniformly dispersed samples. In addition, a wide variety of grains with different densities, sizes, and shapes were investigated for their terminal settling velocity and behaviour. The derived drag coefficient was compared with well-known models such as the one of Brown and Lawler (2003). A sensitivity analysis showed that the spread is caused, to a lesser extent, by variations in fluid properties, measurement errors, and wall effects. Natural variations in specific particle density, path trajectory instabilities, and distinctive multi-particle settling behaviour caused a slightly larger degree of the spread. In contrast, a greater spread is caused by variations in particle size, shape, and orientation. In terms of robust process designs and adequate process optimisation for fluidisation and sedimentation of natural granules, it is therefore crucial to take into consideration the influence of the natural variations in the settling velocity when using predictive models of round spheres.

Sharp phase transition for the continuum Widom–Rowlinson model

The Widom–Rowlinson model (or the Area-interaction model) is a Gibbs point process in Rd with the formal Hamiltonian defined as the volume of ∪x∈ωB1(x), where ω is a locally finite configuration of points and B1(x) denotes the unit closed ball centred at x. The model is also tuned by two other parameters: the activity z>0 related to the intensity of the process and the inverse temperature β≥0 related to the strength of the interaction. In the present paper we investigate the phase transition of the model in the point of view of percolation theory and the liquid-gas transition. First, considering the graph connecting points with distance smaller than 2r>0, we show that for any β≥0, there exists 0 z˜ca(β,r)). These results are in the spirit of recent works using the theory of randomised tree algorithms (Probab. Theory Related Fields 173 (2019) 479–490, Ann. of Math. 189 (2019) 75–99, Duminil-Copin, Raoufi and Tassion (2018)). Secondly we study a standard liquid-gas phase transition related to the uniqueness/non-uniqueness of Gibbs states depending on the parameters z, β. Old results (Phys. Rev. Lett. 27 (1971) 1040–1041, J. Chem. Phys. 52 (1970) 1670–1684) claim that a non-uniqueness regime occurs for z=β large enough and it is conjectured that the uniqueness should hold outside such an half line (z=β≥βc>0). We solve partially this conjecture in any dimension by showing that for β large enough the non-uniqueness holds if and only if z=β. We show also that this critical value z=β corresponds to the percolation threshold z˜ca(β,r)=β for β large enough, providing a straight connection between these two notions of phase transition.

Applying and developing mass customization in construction industries – A multi case study

Mass customization as a strategy has been utilised successfully in the manufacturing industry meeting customers’ idiosyncratic needs in a cost-efficient way. The productivity of the Danish manufacturing industry has increased six times over the last 50 years, whereas the Danish con-struction industry has only doubled. Mass customization has not been extensively explored in the construction industry; therefore, the theoretical background is currently limited. Nevertheless, utilization of mass customization as a strategy might have potentials in the construction industry. This research is a multi-case study of 11 companies in the construction industry focusing on how the cases apply the three fundamental capabilities of mass customization 1) solution space development, 2) choice navigation and 3) robust process design, and how their im-provement initiatives effect performance, and how the cases plan to develop the three funda-mental capabilities of mass customization. The outset is an analysis of project phases and criti-cal success factors characterizing the cases followed by an analysis of how the cases apply and plan to develop the nine tools and approaches supporting the development of the three fundamental capabilities of mass customization. The data are collected through interview as a two-way communicative approach providing holistic and in-depth explanations of the 18 variables of the analysis. The results are that all 11 cases aim at increasing all nine tools and approaches, which strengthen the utilization of the three fundamental capabilities of mass customization in the sense of improving the productivity. This research put forward a definition of mass customization intending to make the concept more visible and accessible for the construction industry.

A Platform Programming Paradigm for Heterogeneous Systems Integration

To cope with growing computing performance requirements, cyber-physical systems architectures are moving toward heterogeneous high-performance computer architectures and networks. Such architectures, however, incur intricate side effects that challenge traditional software design and integration. The programming paradigm can take a key role in mastering software design, as experience in automotive design demonstrates. To cope with the integration challenge, this industry has started introducing a programming paradigm that efficiently preserves application data flow under platform integration and changes with minimum performance loss. This article will revisit this paradigm that is currently used for lock-free multicore programming and explain its extension to the system level. It will then explore its application to two important developments in industrial design. This article will conclude with an evaluation of its properties, its overhead, and its application toward a robust design process.

Robust Design Process to Restrain Irreversible Demagnetization of Interior Permanent Magnet Synchronous Motor Applied in Railway Vehicles Using Dysprosium-Free Permanent Magnet

Trends of traction motor of railway vehicles have been shifted from induction motors to permanent magnet motors. Recently, studies have been conducted on dysprosium-free permanent magnets. Dysprosium is a heavy rare earth element that has a great impact on performance of motors. However, dysprosium affects the coercive force of a permanent magnet and can produce irreversible demagnetization. Conventional studies have focused on improving the performance of motors using permanent magnets without heavy rare earth elements. In this study, a robust rotor design process includes the way to restrain irreversible demagnetization using a dysprosium-free permanent magnet. First, the irreversible magnetization of bar-type and V-type magnets, which are basic models of interior permanent magnet synchronous motors, is analyzed. Next, expected demagnetized parts of the main magnets are used as a submagnets, which make magnetic field bypass both sides of the submagnets, and magnets are added in submagnets regions to compensate gap of performance between target model and double type models. Finally, parameter analysis is performed on the design variables of the main magnet. The proposed the robust design process to restrict irreversible demagnetization proves its validity through finite element analysis.

SMALL AND MEDIUM-SIZED ENTERPRISES IN REPUBLIC OF MOLDOVA

The paper examines the role of SME sector in Moldova’s economy and its likely implications in the RD processes. Several key features of the relationship between knowledge economy, RD and SMEs are emphasized, and also their effect on regional and local communities. Then, a summary of RD particular features in Moldova is presented. The role of SME sector, examined in terms of its performance by ownership and activity types, as a whole and by main firms’ categories (micro-, small, and medium-sized) is analyzed. Small enterprises have been considered as the most adequate solution for promoting RD in general case. National-level analysis is completed by regional insight into the sector as a whole. Finally, some critical aspects of SMEs’ role in RD in Moldova are discussed.

Impact of the isoelectric point of model parvoviruses on viral retention in anion-exchange chromatography.

Anion-exchange chromatography (AEX) is used in the downstream purification of monoclonal antibodies to remove impurities and potential viral contamination based on electrostatic interactions. Although the isoelectric point (pI) of viruses is considered a key factor predicting the virus adsorption to the resin, the precise molecular mechanisms involved remain unclear. To address this question, we compared structurally homologous parvoviruses that only differ in their surface charge distribution. A single charged amino acid substitution on the capsid surface of minute virus of mice (MVM) provoked an increased apparent pI (pIapp ) 6.2compared to wild-type MVM (pIapp = 4.5), as determined by chromatofocusing. Despite their radically different pIapp , both viruses displayed the same interaction profile in Mono Q AEX at different pH conditions. In contrast, the closely related canine parvovirus (pIapp = 5.3) displayed a significantly different interaction at pH 5. The detailed structural analysis of the intricate three-dimensional structure of the capsids suggests that the charge distribution is critical, and more relevant than the pI, in controlling the interaction of a virus with the chromatographic resin. This study contributes to a better understanding of the molecular mechanisms governing virus clearance by AEX, which is crucial to enable robust process design and maximize safety.

Hybrid reliability analysis and robust optimum process design as applied to hot stamping of steel sheets

In the process of hot stamping of steel sheet, there may appear crack and wrinkling defects. Therefore, in the early stage of die design, it is necessary to evaluate the reliability of its quality and optimize the original design. Considering that there are both random and fuzzy factors in the stamping process, the entropy invariance principle is used to transform the fuzzy factors to the random ones, and the equivalent hybrid reliability model of anti-defect is established. Then, the reliability index is taken as constraints in robust optimization. Finally, the stamping process of bulge formed joint is taken as an example, the maximum thinning rate is taken as objective, and the reliability of anti-crack and anti-wrinkle is taken as constraints in reliability-based robust optimization (RRDO). The results show that the standard deviation obtained by robust optimization is 77.5% less than the standard deviation obtained by deterministic optimization and the reliability obtained by RRDO is higher. Compared with the experiment result, the result obtained by RRDO is more in line with engineering practice.

Robust Design Optimization of SPMSM for Robotic Actuator Considering Assembly Imperfection of Segmented Stator Core

To be used as a robotic actuator, a surface-mounted permanent magnet synchronous motor (SPMSM) requires a low cogging torque characteristic. The aim of this study is to analyze and reduce the cogging torque's variation due to the assembly imperfection of the segmented stator that is widely used in the SPMSM. Considering the interconnection of the stator segments, an equivalent modeling method of the segmented stator with assembly imperfection is introduced. In addition, the variation in the cogging torque caused by the assembly imperfection is mathematically evaluated. Then, a robust design process that incorporates a sensitivity analysis and a robust design optimization is proposed. By using the proposed design, a robust optimum design model was built and its validity was verified through Monte Carlo simulation and tests.

Progressive intrinsic ultracontractivity and heat kernel estimates for non-local Schrödinger operators

We study the long-time asymptotic behaviour of semigroups generated by non-local Schrödinger operators of the form H=−L+V; the free operator L is the generator of a symmetric Lévy process in Rd, d>1 (with non-degenerate jump measure) and V is a sufficiently regular confining potential. We establish sharp two-sided estimates of the corresponding heat kernels for large times and identify a new general regularity property, which we call progressive intrinsic ultracontractivity, to describe the large-time evolution of the corresponding Schrödinger semigroup. We discuss various examples and applications of these estimates, for instance we characterize the heat trace and heat content. Our examples cover a wide range of processes and we have to assume only mild restrictions on the growth, resp. decay, of the potential and the jump intensity of the free process. Our approach is based on a combination of probabilistic and analytic methods; our examples include fractional and quasi-relativistic Schrödinger operators.