Robust Design

Today's manufacturers face increasingly intense global competition. To remain pro table, they are challenged to design, develop, test, and manufacture high reliability products in ever-shorter product-cycle times and, at the same time, remain within stringent c o s t constraints. Design, manufacturing, and reliability engineers have d e v eloped an impressive array of tools for producing reliable products. These tools will continue to be important. However, due to changes in way that new product-concepts are being developed and brought to market, there is need for changes in methods used for design-for-reliability and reliability testing, assessment, and improvement programs. This paper uses a conceptual degradation-based reliability model to describe the role and need for integration of reliability data sources. These sources include accelerated degradation testing, accelerated life testing (for materials and components), accelerated multifactor robust-design experiments and over-stress prototype testing (for subsystems and systems), and the use of eld data (especially early-production) to produce a robust, high-reliability product and to provide a process for continuing improvement of reliability of existing and future products. Manufacturers need develop economical and timely methods of obtaining, at each step of the product design and development process, the information needed to meet overall reliability goals. We emphasize the need for intensive and e ective upstream testing of product materials, components, and design concepts.

A new stiffness matrix for nonlinear dynamic analysis of triangular TLPs is formulated utilising both the equilibrium of forces and energy balance approach for arbitrary surge, sway and yaw. Static numerical analysis is performed for a triangular TLP, ordinarily used as a benchmark in the relevant literature, by imposing surge force and yaw moment. In order to validate the new stiffness formulation, the obtained results are compared with those determined by an FEM analysis. Some shortcomings of the traditionally used stiffness matrix are pointed out.

Plastic injection molding comprises plastication, injection, packing, cooling, and ejection and process/part quality control applications. These steps are followed for the parts, which are designed by CAD & to be produced by plastic injection method. A competent mold designer must have a thorough knowledge of the principles of mold making as the design of the various parts of the mould depends on the technique adopted for its manufacture. This paper deals with the literature review on study of application of taguchi method. The Taguchi robust parameter design has been widely used over the past decade to solve many single response process parameter designs. Taguchi optimization method was used by exploiting mold analyses based on two level factorial design. Orthogonal arrays of Taguchi, the signal-to-noise (S/N) ratio are utilized to find the optimal levels and the effect of process parameters are determined by many researchers on shrinkage & warpage.

As a system becomes more complex, the uncertainty in the operating conditions increases. In such a system, implementing a precise failure analysis in early design stage is vital. However, there is a lack of applicable methodology that shows how to implement failure analysis in the early design phase to achieve a robust design. The main purpose of this paper is to present a framework to design a complex engineered system resistant against various factors that may cause failures, when design process is in the conceptual phase and information about detailed system and component is unavailable. Within this framework, we generate a population of feasible designs from a seed functional model, and simulate and classified failure scenarios. We also develop a design selection function to compare robust score for candidate designs, and produce a preference ranking. We implement the proposed method on the design of an aerospace monopropellant propulsion system.

Our demo aims at proving the concept of a recent proposed interference management scheme that reduces the inter-cell interference in downlink without complex coordination, known as non-classic interference alignment (IA) scheme. We assume a case where one main Base Station (BS) needs to serve three users equipments (UE) while another BS is causing interference. The primary goal is to construct the alignment scheme ; i.e. each UE estimates the main and interfered channel coefficients, calculates the optimal interference free directions dropped by the interfering BS and feeds them back to the main BS which in turn applies a scheduling to select the best free inter-cell interference directions. Once the scheme is build, we are able to measure the total capacity of the downlink interference channel. We run the scheme in CorteXlab ; a controlled hardware facility located in Lyon, France with remotely programmable radios and multi-node processing capabilities, and we illustrate the achiev...

We discuss some first steps towards experimental design for neural network regression which, at present, is too complex to treat fully in general. We encounter two difficulties: the nonlinearity of the models together with the high parameter dimension on one hand, and the common misspecification of the models on the other hand. Regarding the first problem, we restrict our consideration to neural networks with only one and two neurons in the hidden layer and a univariate input variable. We prove some results regarding locally D-optimal designs, and present a numerical study using the concept of maximin optimal designs. In respect of the second problem, we have a look at the effects of misspecification on optimal experimental designs. iii All glory and honor goes to God Almighty because without his abundant grace and love nothing would have been possible. I would like to express my deep and sincere gratitude to my supervisor, Professor Dr. Jürgen Franke, Chair for Applied Mathematical Statistics, University of Kaiserslautern, for the opportunity and continuous support for my PhD study and research, for his patience, motivation, enthusiasm, and immense knowledge. His outstanding guidance helped me during the time of research and writing of this thesis. I wish to also thank him for providing extra funding through the University of Kaiserslautern for my study when it was needed. My profound gratitude goes to Professor Dr. Jens-Peter Kreiß, University of Braunschweig, for accepting to be the second referee for my thesis. I owe a lot of gratitude to my second supervisor, Dr. Alex Sarishvili, Department of Systems, Analysis and Prognosis (SYS), Fraunhofer Institute for Industrial and Financial Mathematics (ITWM), Kaiserslautern, for his kind support, guidance, constructive and valuable comments and suggestions throughout my work.

Das Paper wurde doppelt als Technical report mit der Zählung 2009,7 und 2009,9 eingereicht. Das aktuellere Datum mit der Zählung 2009,9 ist gültig. Die Zählung 2009,7 ist nicht mehr existent.Toxicologists have been increasingly using a class of models to describe a continuous response in the last few years. This class consists of nested nonlinear models and is used for estimating various parameters in the models or some meaningful function of the model parameters. Our work here is the first to address design issues for this popular class of models among toxicologists. Specifically we construct a variety of optimal designs under model uncertainty and study their properties for estimating the critical effective dose (CED), which is model dependent. Two types of optimal designs are proposed: one type maximizes the minimum of efficiencies for estimating the CED regardless which member in the class of models is the appropriate model, and (ii) dual-objectives optimal design that simultane...

We construct optimal designs for estimating fetal malformation rate, prenatal death rate and an overall toxicity index in a toxicology study under a broad range of model assumptions. We use Weibull distributions to model these rates and assume that the number of implants depend on the dose level. We study properties of the optimal designs when the intra-litter correlation coefficient depends on the dose levels in different ways. Locally optimal designs are found, along with robustified versions of the designs that are less sensitive to misspeciffication in the nominal values of the model parameters. We also report efficiencies of commonly used designs in toxicological experiments and e±ciencies of the proposed optimal designs when the true rates have non-Weibull distributions. Optimal design strategies for finding multiple-objective designs in toxicology studies are outlined as well

The Michaelis-Menten model has and continues to be one of the most widely used models in many diverse fields. In the biomedical sciences, the model continues to be ubiquitous in biochemistry, enzyme kinetics studies, nutrition science and in the pharmaceutical sciences. Despite its wide ranging applications across disciplines, design issues for this model are given short shrift. This paper focuses on design issues and provides a variety of optimal designs of this model. In addition, we evaluate robustness properties of the optimal designs under a variation in optimality criteria. To facilitate use of optimal design ideas in practice, we design a web site for generating and comparing different types of tailor-made optimal designs and user-supplied designs for the Michaelis-Menten and related models

Prediction of transient natural convection heat transfer in vented enclosures has multiple applications such as understanding of cooking environment in ovens and heat sink performance in electronic packaging industry. The thermal field within an oven has significant impact on quality of cooked food and reliable predictions are important for robust design and performance evaluation of an oven. The CFD modeling of electric oven involves three-dimensional, unsteady, natural convective flow-thermal field coupled with radiative heat transfer. However, numerical solution of natural convection in enclosures with openings at top and bottom (ovens) can often lead to non-physical solutions such as reverse flow at the top vent, partly a function of initialization and sometimes dependent on boundary conditions. In this paper, development of a physics based robust CFD methodology is discussed. This model has been developed with rigorous experimental support and transient validation of this model with experiments show less than 3% discrepancy for a bake cycle. There is greater challenge in simulating a broil cycle, where the fluid inside the cavity is stably stratified and is also highlighted. A comparative analyses of bake and broil cycle thermal fields inside the oven are also presented.

This paper proposes a new practice of robust design methodology (RDM); to adopt a life cycle approach to noise factor identification. Such practice expands the boundary of traditional use of robust design where noise factors are generally known, observable and experimentally controllable. By exploiting information from claim data of a medium-sized Swedish manufacturing company, several other noise factors were identified and classified into product life cycle stages; factors that are often uncontrollable, unobservable, or unknown. Exploratory data analysis, interviews, and internal documents are used in this study. The results show that by analysing the claims data, the product's failure modes can be identified and classified into various product life cycle stages. This enables identification of noise factors in each product life cycle stage. The use of a life cycle approach has two major implications. One is to expand the focus of RDM to encompass all product life cycle stages ...

Two aspects of manufacturing systems are explored by means of statistical models. Specifically, reliability growth models and Taguchi-type considerations about quality control are taken upon. The provided discussions help manufacturing engineers apply the presented models.

Coupled problems in engineering inevitably lead to contradictory goals for single quality criteria. Applying numerical optimization to find the best solution requires the definition of an objective function based upon these criteria as a measure of merit for the whole design. It will be shown that the usual approach of a weighted sum is improper and unreliable. Also the consideration of the complete Pareto front is not only no solution to this problem, it usually requires a not maintainable amount of calculations. Therefore, a new approach for the definition of objective functions is proposed, which solves the dilemma of contradictory goals by finding an appropriate compromise based upon the intuition.

This paper addresses the optimization problem of symbollevel precoding (SLP) in the downlink of a multiuser multiple-input multiple-output (MU-MIMO) wireless system while the precoder's output is subject to partially-known distortions. In particular, we assume a linear distortion model with bounded additive noise. The original signal-tointerference-plus-noise ratio (SINR) -constrained SLP problem minimizing the total transmit power is first reformulated as a penalized unconstrained problem, which is referred to as the relaxed robust formulation. We then adopt a worst-case design approach to protect the users' intended symbols and the targeted constructive interference with a desired level of confidence. Due to the non-convexity of the relaxed robust formulation, we propose an iterative algorithm based on the block coordinate ascentdescent method. We show through simulation results that the proposed robust design is flexible in the sense that the CI constraints can be relaxed so as to keep a desirable balance between achievable rate and power consumption. Remarkably, the new formulation yields more energyefficient solutions for appropriate choices of the penalty parameter, compared to the original SLP problem.

Traffic Classification (TC) is pivotal for network management, cybersecurity, and Quality of Experience (QoE) monitoring. However, while Deep Learning (DL) has significantly advanced TC, most existing works assume static, idealized conditions, overlooking key challenges of real-world deployments-such as traffic variability, routing asymmetries, out-of-order packet arrivals, and partial visibility at the Vantage Points (VPs). This motivates the need for robustness evaluations under such scenarios. In this work, we investigate the robustness of state-of-the-art (SOTA) TC models under realistic, yet controlled, perturbation scenarios. Specifically, we introduce novel, model-agnostic traffic perturbations-simulating time jitter, retransmissions, and partial visibility-to reflect conditions commonly encountered in live network traffic. We evaluate our approach on three public datasetsi.e., VPN-16, MIRAGE-19, and MIRAGE-24-and show how Mimetic-Enhanced, a multimodal model, tends to outperform two representative single-modal counterparts both in terms of TC effectiveness on clean traffic and robustness under perturbations. Nonetheless, our analysis also reveals that multimodal models remain vulnerable under specific perturbation settings. To address this limitation, we propose a model-agnostic perturbation-aware training framework based on Supervised Data Augmentation (Aug) and Contrastive Learning (CL)-considering both self-supervised and supervised variants. Unlike architecture-specific solutions, our approach operates at the learning strategy level, allowing it to be seamlessly applied to diverse classifiers without requiring structural modifications. Adopting Mimetic-Enhanced as a primary multimodal case study, we integrate the proposed strategies into its two-stage training pipeline. Experimental results demonstrate that perturbation-aware training not only improves TC effectiveness on clean (i.e., unperturbed) traffic-particularly when applied across both training stages-but also significantly strengthens the model's robustness under diverse and realistic perturbation scenarios. Furthermore, we investigate Out-of-Distribution (OOD) detection, model calibration, and TC effectiveness in low-data regimes. Finally, we explicitly demonstrate the framework's generalizability by validating it on other SOTA architectures, spanning both single-and multi-modal approaches.

Reliable vehicle localization is a foundational requirement for autonomous and intelligent transportation systems. While Global Positioning System (GPS) technology has traditionally supported vehicle positioning, its effectiveness is significantly reduced in GPS-denied environments such as urban canyons, tunnels, and underground infrastructures. This article presents a conceptual discussion of a robust vehicle localization framework designed for such challenging conditions. By emphasizing resilient information integration and adaptive data organization principles, the framework enhances localization reliability without relying on GPS availability. The study focuses on system robustness, decision stability, and security awareness, highlighting the relevance of such frameworks for future urban mobility and smart transportation ecosystems.

A Distortion Contribution Analysis (DCA) obtains the distortion at the output of an analog electronic circuit as a sum of distortion contributions of its sub-circuits. Similar to a noise analysis, a DCA helps a designer to pinpoint the actual source of the distortion. Classically, the DCA uses the Volterra theory to model the circuit and its sub-circuits. This DCA has been proven useful for small circuits or heavily simplified examples. In more complex circuits however, the amount of contributions increases quickly, making the interpretation of the results difficult. In this paper, the Best Linear Approximation (BLA) is used to perform the DCA instead. The BLA represents the behaviour of a sub-circuit as a linear circuit with the unmodelled distortion represented by a noise source. Combining the BLA with a classic noise analysis yields a DCA that is simple to understand, yet capable to handle complex excitation signals and complex strongly non-linear circuits.

This paper proposes a parametric identification method for multi-input multi-output parallel Wiener systems. The linear dynamic parts of the system are modeled by a parametric rational function in the continuous or discrete time variable, while the static nonlinearities are represented by a linear combination of nonlinear basis functions. The identification method uses a three step procedure to obtain initial estimates. In the first step, the frequency response matrix of the best linear approximation is estimated for different input excitation levels. In the second step, the power dependent dynamics are decomposed over a number of parallel orthogonal branches. In the last step, the static nonlinearities are estimated using a linear least squares estimation. Finally both linear and nonlinear parameters are estimated together using a nonlinear optimization procedure. The method is illustrated on a simulation example.