Parameter Design Problem Research Articles

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.

Automated design evolution for parametric design applied on computer-aided welding fixture designs

Design engineers are required to deliver design solutions by a fixed deadline. Increasing demand for shortening lead time limits the exploratory and creative freedom of the designer. This paper proposes an automated design system that can substitute part of the repetitive and standardized tasks in the design process, increasing efficiency and shifting the design engineer's efforts to creative work. Computer Aided Welding Fixture Design is an engineering domain increasingly being pressed for shortening lead time. Welding Fixture Designs are multi-part structures that secure an assembly product during a welding process. The compact and complex assembly product to be fixed and welded presents a configuration and geometric challenge for the fixture regarding sequence, collisions, tolerances, welding path, human interaction, etc. This paper presents the welding fixture design problem in its general form. To describe the freedom of automation, the paper introduces the term decision space as the subset of a design space feasible by automation. Depending on the desired outcome, the decision space can be exploited through various strategies and automation approaches. This paper presents one of these approaches by implementing an evolutionary algorithm to the parametric fixture design problem of the use case. The results show reduced engineering efforts and the potential for expanding the exploratory spectrum of design proposals during conceptualization.

Parametric design evolution for production setups; a case study for welding fixtures

Automated design evolution and optimization are concepts of utmost interest to the industry for reducing design time and cost. This paper addresses the development of an automated design solution based on an evolutionary algorithm for a parametric design problem. The design problem addresses welding fixture designs as an integrated geometric design and configuration problem. The suggested automation approach is based on the parameterization of the fixture geometry and the welding process to minimize collisions across moving and static bodies during the welding operation. The aim is to produce a welding fixture design simulation that can be adjusted for specific requirements based on expert knowledge without re-iterating the entire composition of the problem. The modularity is demonstrated by modifications of the design problem formulation aimed at altering the exploration spectrum and design proposals of the welding fixture. An early system integration shows a reduction of designer efforts to 30% compared to a manual process, with an increase in overall process time due to software computational times.

A Circulating Power Suppression Structure for Three-Winding-Transformer-Based Converter

A circulating power suppression structure for a three-winding-transformer-based converter is proposed in this letter. The equivalent circuit models of the dual-transformer-based and three-winding-transformer-based converter are derived, which illustrates their merits and demerits. With the proposed structure, the drawbacks of these two transformer-type converters can be overcome, and their advantages can be combined. Useless circulating loss in three-winding-transformer can be reduced to mW level and can be omitted. The complex and coupled dual-transformer parameter design problem can be solved. The analysis and performance of the proposed structure are fully validated by experimental results.

Multi-response robust optimization using GP model with variance calibration

Gaussian process (GP) modeling is a powerful surrogate modeling technique to deal with robust parameter design problems for complex products/processes. The predicted variance of GP models is a crucial criterion for measuring the output response variation. However, most existing robust optimization methods always ignore the variance uncertainty in the modeling process, which may weaken the effectiveness of the obtained optimal solution. This article proposes a novel robust optimization approach considering the variance uncertainty with calibration technique to address the foregoing challenges. Firstly, the multi-response Gaussian process (MGP) model with the sparse estimation technique is constructed to estimate the relationship between the input and output. Secondly, the predicted variance is calibrated using bootstrapped sampling and conditional simulation techniques. Then, the interval analysis technique is adopted to measure the response uncertainty. Finally, an optimization approach incorporating improved variance estimation is proposed to find the optimal robust parameter settings. A numerical simulation example and a real case study are used to illustrate the effectiveness of the proposed approach. The results show that the proposed method improves the robustness of the optimal solution compared to the existing ones.

A decision support tool for intelligent manufacturing systems via an elevator kinematic optimisation based method

This paper presents a novel centralised method to solve the manufacturing parameter design problem based on evolutionary computation elements using a novel elevator kinematic optimisation algorithm. The new method is referred to as compromise multi response surface optimisation method. Its evolutionary computation nature simultaneously produces several flexi-design plans to prevent a high number of experiments and is used within the design phase of Taguchi arrays. The new method also provides various channels via available information for choosing the parameter levels during the analysis phase without new individual fitting sets. The industrial application used for benchmarking the new approach revealed that the proposed design and analysis method could lead to better optimal solutions for complex processes such as resistance welding than those of a conventional Taguchi method.

Removing cuttings from inclined and horizontal wells: Numerical analysis of the required drilling fluid rheology and flow rate

Inclined and horizontal wells are prone to the accumulation of drilling cuttings. Determination of the optimal drilling parameters that facilitate cuttings removal is a key problem. Existing experimental, theoretical and field-based empirical analyses of the hole cleaning problem have failed to fully reveal the processes of cuttings transport, settlement, and accumulation concerning drilling string rotation in large annuli with high-density non-Newtonian drilling fluids. In this study, a coupled computational fluid dynamics-discrete element method (CFD-DEM) simulation model of the interaction between cuttings and drilling fluid was established. This allows the influences of well inclination, drill pipe rotational rate, flow rate, fluid density, and rheological properties on hole cleaning efficiency to be analyzed. A multi-parameter correlation equation between wellbore cleaning efficiency and wellbore size, inclination angle, drill string eccentricity, drill string rotational speed, rate of penetration, cuttings size, flow rate, and fluid density was obtained using dimensional analysis and multivariate nonlinear regression of simulation data. Finally, with consideration of wellbore cleaning characteristics, formation pressure bearing capacity, and pump condition constraints, the proposed multi-parameter correlation model was used to provide optimal wellbore cleaning parameters. The cleaning efficiency of horizontal wells is lowest in the inclined section. Hole cleaning efficiency increases significantly with drill pipe rotational rate, flow rate, and 6 RPM Fann dial reading. The proposed model can correlate hole cleaning efficiency with the 6 RPM Fann dial reading of drilling fluid and flow rate. At a given hole cleaning efficiency, the required minimum flow rate decreases with increases in the 6 RPM Fann dial reading. Similarly, the required minimum 6 RPM Fann dial reading decreases with increases in flow rate. The models and design layout proposed in this paper provide a new approach to solving hole cleaning parameter design problems in highly inclined and horizontal wells.

A Study on the Optimization of In-Process Inspection Procedure for Active Pharmaceutical Ingredients Manufacturing Process

The in-process inspection procedure is one of the critical operations in the active pharmaceutical ingredients manufacturing process. This study aims to improve the performance of the IPI service system in terms of three main criteria, namely service level, cycle time, and maximum tardy time. In solving this multiple-criteria decision-making problem, the proposed study seeks to redesign three process control factors, namely the service configuration, the dispatching rule, and the scheduling rule. The problem is solved using the Taguchi robust design methodology. Since the Taguchi method handles parameter design problems with only one criterion, Technique for Order Preference by Similarity to an Ideal Solution, a multiple-criteria decision-making method, is used to provide a surrogate response to the Taguchi method. The numerical results show that the redesigned IPI system improves the service level by 28.75%, the cycle time by 18.32%, and the maximum tardy time by 22.22%.

Proper Generalized Decomposition for Parametric Study and Material Distribution Design of Multi-Directional Functionally Graded Plates Based on 3D Elasticity Solution.

The use of mesh-based numerical methods for a 3D elasticity solution of thick plates involves high computational costs. This particularly limits parametric studies and material distribution design problems because they need a large number of independent simulations to evaluate the effects of material distribution and optimization. In this context, in the current work, the Proper Generalized Decomposition (PGD) technique is adopted to overcome this difficulty and solve the 3D elasticity problems in a high-dimensional parametric space. PGD is an a priori model order reduction technique that reduces the solution of 3D partial differential equations into a set of 1D ordinary differential equations, which can be solved easily. Moreover, PGD makes it possible to perform parametric solutions in a unified and efficient manner. In the present work, some examples of a parametric elasticity solution and material distribution design of multi-directional FGM composite thick plates are presented after some validation case studies to show the applicability of PGD in such problems.

The optimal detuning approach based centralized control design for MIMO processes

This manuscript introduces an optimal detuning approach for designing a centralized PI control system for multi-input multi-output (MIMO) processes. Based on the approach, two multivariable PI controller designs are proposed in this manuscript. The proposed approach formulates the centralized PI controller design problem in an optimization framework and transforms it into an equivalent detuning parameter(s) design problem. While the centralized controller design with the proposed 1-Optimal Detuning Parameter (ODP) method is carried out as a multi-stage problem, the proposed 2-ODP method formulates the controller design as a single-stage optimization problem. An effective transfer function (ETF) parameterization followed by controller synthesis using the IMC theory and further the optimal detuning comprises the various stages in 1-ODP design. In this regard, a novel ETF parameterization procedure for large scale processes or systems with higher-order elements is presented in this manuscript. Contrary to the centralized controller design in the kp,ki space, where the dimensionality of the optimization problem blows at O(2n2), the proposed 1-ODP and 2-ODP design methods always aim at solving a uni- and bi-dimensional optimization problem, respectively, irrespective of the dimensionality of the MIMO process. Hence, the proposed method is easily applicable even to high-dimensional MIMO processes. Several illustrative industrial MIMO systems are considered to demonstrate the applicability of the proposed methods to highly interacting and large scale processes. The simulation studies show that the proposed methods performs better as compared to the other centralized PI controller designs. Even with parameter variations, the proposed methods give satisfactory closed-loop performance. Further, robust stability analysis for the proposed designs is performed by considering multiplicative input and output uncertainties.

Over-the-Air Computation Systems: Optimal Design With Sum-Power Constraint

Over-the-air computation (AirComp), which leverages the superposition property of wireless multiple-access channel (MAC) and the mathematical tool of function representation, has been considered as a promising technique for effective collection and computation of massive sensor data in wireless Big Data applications. In most of the existing work on AirComp, optimal system-parameter design is commonly considered under the peak-power constraint of each sensor. In this paper, we propose an optimal transmitter-receiver (Tx-Rx) parameter design problem to minimize the computation mean-squared error (MSE) of an AirComp system under the sum-power constraint of the sensors. We solve the non-convex problem and obtain a closed-form solution. Also, we investigate another problem that minimizes the sum power of the sensors under the constraint of computation MSE. Our results show that in both of the problems, the sensors with poor and good channel conditions should use less power than the ones with moderate channel conditions.

A Parameter Eigenstructure Assignment Design with Any Desired State Vector in Linear Time-Invariant System

The parametric design problem for eigenstructure assignment with arbitrary expected state vector is studied via state feedback in linear time invariant systems. The main idea of the considered problem is to design a parametric form of the state feedback, so that the designed closed-loop system has the desired eigenvalues, the desired eigenvectors and arbitrary expected state vector. Based on the parametric solution for a class of Sylvester matrix equation, the parametric expression of all the state feedback gain matrices is given in this paper. The free parameters including in the state feedback gain matrix can be further used to meet other performance requirements for the control system design. Finally, a simulation experiment is given to illustrate the effectiveness and simplicity of the proposed parametric method for the state feedback eigenstructure assignment problem with arbitrary expected state vector in the linear time invariant systems.

Stochastic Event-Based Sensor Schedules for Remote State Estimation in Cognitive Radio Sensor Networks

We consider the problem of communication allocation for remote state estimation in a cognitive radio sensor network~(CRSN). A sensor collects measurements of a physical plant, and transmits the data to a remote estimator as a secondary user (SU) in the shared network. The existence of the primal users (PUs) brings exogenous uncertainties into the transmission scheduling process, and how to design an event-based scheduling scheme considering these uncertainties has not been addressed in the literature. In this work, we start from the formulation of a discrete-time remote estimation process in the CRSN, and then analyze the hidden information contained in the absence of data transmission. In order to achieve a better tradeoff between estimation performance and communication consumption, we propose both open-loop and closed-loop schedules using the hidden information under a Bayesian setting. The open-loop schedule does not rely on any feedback signal but only works for stable plants. For unstable plants, a closed-loop schedule is designed based on feedback signals. The parameter design problems in both schedules are efficiently solved by convex programming. Numerical simulations are included to illustrate the theoretical results.

A biased-randomized algorithm for optimizing efficiency in parametric earthquake (Re) insurance solutions

Natural catastrophes with their widespread damage can overwhelm the financial systems of large communities. Catastrophe insurance is a well-understood financial risk transfer mechanism, aiming to provide resilience in the face of adversity. However, catastrophe insurance has generally a low penetration, mainly due to its high cost or to distrust of the product in providing a fast financial recovery. Parametric insurance is a form of derivative insurance that pays quickly and transparently based on a few measurable features of the event, offering a promising avenue to increase catastrophe insurance coverage. In the context of seismic risk, parametric policies may use location and magnitude of an earthquake to determine whether a payment should be made. In this paper we follow a design typology referred to as ‘cat-in-a-box’, where magnitude thresholds are defined over a set of cuboids that partition Earth’s crust. The main challenge in the design of these tools consists in finding the optimal magnitude thresholds for a large set of cubes that maximize efficiency for the insured, subjected to a budgetary constraint. Additional geometric constraints aim to reduce the volatility of payments under uncertainty. The parametric design problem is a combinatorial problem, which is NP-hard and large scale. In this paper we propose a fast heuristic and a biased-randomized algorithm to solve large-sized problems in reasonably low computing times. Experimental results illustrate the computational limits and solution quality associated with the proposed approaches.

Online robust parameter design considering observable noise factors

Variation is the root cause of product quality problems. Variations caused by noise factors often affect the quality of the product during manufacturing, resulting in products that fail to meet customer requirements. In this article, a Bayesian online robust parameter design method is proposed to address the parameter design problem with observable noise factors. The proposed method not only takes into account the online observations of noise factors through using a time-series model, but also adjusts the optimal settings of the control factors in real time to reduce the impact of the variations from noise factors on product quality by using a response surface model and an expected loss function. The advantages of the proposed method are illustrated by a numerical example and a case study. The results show that the proposed method can give more reasonable results than the existing methods.

Conceptual design and parametric optimization of self-propelled semi-submersible repair ships: a novel equipment providing maintenance and repair support at sea

Safety of ships and/or offshore structures at sea is increasingly crucial as seaborne activities intensify. However, maintenance and repair of them suffering marine accidents away from shipyards are an often overlooked area, with existing maintenance and/or repair ships either being of limited workshops and facilities or incapable lifting them out of water for underwater engineering. A conceptual design of a self-propelled semi-submersible repair ship is proposed for the first time to facilitate the spot maintenance and repair of damaged ships and/or offshore structures at sea, which is obviously able to reduce the time, costs and risks of transporting them from accident scenes to shipyards onshore. Furthermore, this paper focuses on parametric design and multi-objective optimization problem of this novel equipment. The ratio of deadweight to principal dimensions, working deck area and average daily cost considering marine emissions trading scheme are simultaneously chosen as objectives of this problem. Both the weighted ideal point method and the NSGA-II algorithm are used to obtain the optimization results of a 50 thousand dwt self-propelled semi-submersible repair ship and the relations and differences between the optimization results of two methods are analyzed. The research results indicate that the parametric design and multi-objective optimization method can provide theoretical support for the preliminary design.

Optimal Parameter Design for Estimation Theoretic Secure Broadcast

In this letter, estimation theoretic secure broadcast of a random parameter is investigated. In the considered setting, each receiver device employs a fixed estimator and carries a certain security risk such that its decision can be available to a malicious third party with a certain probability. The encoder at the transmitter is allowed to use a random mapping to minimize the weighted sum of the conditional Bayes risks of the estimators under secrecy and average power constraints. After formulating the optimal parameter design problem, it is shown that the optimization problem can be solved individually for each parameter value and the optimal mapping at the transmitter involves a randomization among at most three different signal levels. Sufficient conditions for improvability and non-improvability of the deterministic design via stochastic encoding are obtained. Numerical examples are provided to corroborate the theoretical results.

Tracking control for nonlinear time‐delay multiagent systems with input saturation

SummaryIn this paper, the tracking control problem for a class of nonlinear time‐delay multiagent systems with input saturation is considered. The nonlinear dynamics are dominated by strict‐feedback form and satisfy Lipschitz conditions with constant gains. First, it indicated that the tracking control problem is equivalent to a general bound problem of high‐dimensional multivariable systems. Second, an ingenious state transformation is utilized to change the bound problem into the parameter design problem. Third, by the static gain control technique and the hyperbolic tangent function, both state feedback and output feedback controllers are built such that all signals of the closed‐loop systems are globally bounded, and the tracking errors between the followers and the leader can converge to a small neighborhood around the origin by appropriately selecting parameters. Finally, an example is shown to verify the feasibility of our results.

UAV Power Component—DC Brushless Motor Design With Merging Adjacent-Disturbances and Integrated-Dispatching Pigeon-Inspired Optimization

As a widely used power component of unmanned aerial vehicle, the direct current (dc) brushless motor has the prominent advantages of high efficiency, long period, low noise, and better speed–torque. In this paper, a novel parameter design method is presented for a dc brushless motor. To maximize the efficiency of the power system, the pigeon-inspired optimization algorithm is utilized by converting the dc brushless motor parameter design problem to an optimization problem. A modified model is proposed to avoid falling into the local optimal value and increase the population diversity by introducing the adjacent-disturbances and integrated-dispatching strategies. Comparative experiments are conducted to verify the convergence speed and the effectiveness of the proposed method.

Parametric Design of Scalable Mechanisms for Additive Manufacturing

Additive manufacturing allows a direct fabrication of any sophisticated mechanism when the clearance of each joint is sufficiently large to compensate the fabrication error, which frees the designers of cumbersome assembly jobs. Clearance design for assembly mechanism whose parts are fabricated by subtractive manufacturing has been well defined. However, the related standard for parts fabricated by additive manufacturing is still under exploration due to the fabrication error and the diversity of printing materials. For saving time and materials in a design process, a designer may fabricate a series of small mechanisms to examine their functionality before the final fabrication of a large mechanism. As a mechanism is scaled, its joint clearances may be reduced, which affects the kinematics of the mechanisms. Maintaining certain clearance for the joints during the scaling process, especially for gear mechanisms, is an intricate problem involving the analysis of nonlinear systems. In this paper, we focus on the parametric design problem for the major types of joints, which allows the mechanisms to be scaled to an arbitrary level while maintaining their kinematics. Simulation and experimental results are present to validate our designs.