A framework for tolerance design considering systematic and random uncertainties due to operating conditions

2019 ◽  
Vol 39 (5) ◽  
pp. 854-871
Author(s):  
S. Khodaygan
Keyword(s):  
Operating Conditions ◽  
Tolerance Allocation ◽  
Manufacturing Cost ◽  
Tolerance Design ◽  
Element Analysis ◽  
Content Type ◽  
Multi Objective ◽  
Mechanical Assemblies ◽  
Conflicting Objectives ◽  
Random Uncertainties

Purpose The purpose of this paper is to present a novel Kriging meta-model assisted method for multi-objective optimal tolerance design of the mechanical assemblies based on the operating conditions under both systematic and random uncertainties. Design/methodology/approach In the proposed method, the performance, the quality loss and the manufacturing cost issues are formulated as the main criteria in terms of systematic and random uncertainties. To investigate the mechanical assembly under the operating conditions, the behavior of the assembly can be simulated based on the finite element analysis (FEA). The objective functions in terms of uncertainties at the operating conditions can be modeled through the Kriging-based metamodeling based on the obtained results from the FEA simulations. Then, the optimal tolerance allocation procedure is formulated as a multi-objective optimization framework. For solving the multi conflicting objectives optimization problem, the multi-objective particle swarm optimization method is used. Then, a Shannon’s entropy-based TOPSIS is used for selection of the best tolerances from the optimal Pareto solutions. Findings The proposed method can be used for optimal tolerance design of mechanical assemblies in the operating conditions with including both random and systematic uncertainties. To reach an accurate model of the design function at the operating conditions, the Kriging meta-modeling is used. The efficiency of the proposed method by considering a case study is illustrated and the method is verified by comparison to a conventional tolerance allocation method. The obtained results show that using the proposed method can lead to the product with a more robust efficiency in the performance and a higher quality in comparing to the conventional results. Research limitations/implications The proposed method is limited to the dimensional tolerances of components with the normal distribution. Practical implications The proposed method is practically easy to be automated for computer-aided tolerance design in industrial applications. Originality/value In conventional approaches, regardless of systematic and random uncertainties due to operating conditions, tolerances are allocated based on the assembly conditions. As uncertainties can significantly affect the system’s performance at operating conditions, tolerance allocation without including these effects may be inefficient. This paper aims to fill this gap in the literature by considering both systematic and random uncertainties for multi-objective optimal tolerance design of mechanical assemblies under operating conditions.

Sensors distribution optimization for impact localization using NSGA-II

Sensor Review ◽  
2015 ◽  
Vol 35 (4) ◽  
pp. 409-418 ◽  
Author(s):  
Peng Li ◽  
Yuhua Wang ◽  
Jingru Hu ◽  
Jianmin Zhou
Keyword(s):  
Principal Component ◽  
Optimization Method ◽  
Objective Functions ◽  
Nsga Ii ◽  
Element Analysis ◽  
Content Type ◽  
Time Frequency ◽  
Real World Problem ◽  
Conflicting Objectives ◽  
Impact Localization

Purpose – The purpose of this study which resulted in this work is to propose an optimization method of sensors distribution for structural impact localization. Design/methodology/approach – This paper presents a multi-objective optimization study of a novel sensors distribution technique, where two optimization objective functions are considered: sensors number and sensors location optimization performance index. In addition, the finite element analysis, the time-frequency transform and the principal component analysis are combined to quantize the above objective functions. The non-dominated sorting genetic algorithm II (NSGA-II) is used to acquire Pareto solutions. Findings – The effectiveness of this method is validated through a prototype laboratory called the piezoelectric intelligent structure where promising results are obtained. Originality/value – An optimization method of this novel sensors distribution technique is built and produced a set of efficiency solutions for the real-world problem of impact localization where two conflicting objectives are involved.

Including decision-makers preferences in the cost-deprivation trade-off during disaster interventions

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Florian Diehlmann ◽  
Patrick Siegfried Hiemsch ◽  
Marcus Wiens ◽  
Markus Lüttenberg ◽  
Frank Schultmann
Keyword(s):  
Decision Making ◽  
Humanitarian Logistics ◽  
Decision Makers ◽  
Weighted Sum ◽  
Trade Off ◽  
Content Type ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
The Cost

Purpose In this contribution, the purpose of this study is to extend the established social cost concept of humanitarian logistics into a preference-based bi-objective approach. The novel concept offers an efficient, robust and transparent way to consider the decision-maker’s preference. In principle, the proposed method applies to any multi-objective decision and is especially suitable for decisions with conflicting objectives and asymmetric impact. Design/methodology/approach The authors bypass the shortcomings of the traditional approach by introducing a normalized weighted sum approach. Within this approach, logistics and deprivation costs are normalized with the help of Nadir and Utopia points. The weighting factor represents the preference of a decision-maker toward emphasizing the reduction of one cost component. The authors apply the approach to a case study for hypothetical water contamination in the city of Berlin, in which authorities select distribution center (DiC) locations to supply water to beneficiaries. Findings The results of the case study highlight that the decisions generated by the approach are more consistent with the decision-makers preferences while enabling higher efficiency gains. Furthermore, it is possible to identify robust solutions, i.e. DiCs opened in each scenario. These locations can be the focal point of interest during disaster preparedness. Moreover, the introduced approach increases the transparency of the decision by highlighting the cost-deprivation trade-off, together with the Pareto-front. Practical implications For practical users, such as disaster control and civil protection authorities, this approach provides a transparent focus on the trade-off of their decision objectives. The case study highlights that it proves to be a powerful concept for multi-objective decisions in the domain of humanitarian logistics and for collaborative decision-making. Originality/value To the best of the knowledge, the present study is the first to include preferences in the cost-deprivation trade-off. Moreover, it highlights the promising option to use a weighted-sum approach to understand the decisions affected by this trade-off better and thereby, increase the transparency and quality of decision-making in disasters.

Tangential inlet cyclone separators with low solid loading

2016 ◽  
Vol 33 (7) ◽  
pp. 2090-2116 ◽  
Author(s):  
Riccardo Amirante ◽  
Paolo Tamburrano
Keyword(s):  
Pareto Front ◽  
Industrial Design ◽  
Operating Conditions ◽  
Design Parameters ◽  
Solid Loading ◽  
Cfd Model ◽  
Content Type ◽  
Numerical Predictions ◽  
Conflicting Objectives ◽  
Tangential Inlet

Purpose The purpose of this paper is to propose an effective methodology for the industrial design of tangential inlet cyclone separators that is based on the fully three-dimensional (3D) simulation of the flow field within the cyclone coupled with an effective genetic algorithm. Design/methodology/approach The proposed fully 3D computational fluid dynamics (CFD) model makes use of the Reynold stress model for the accurate prediction of turbulence, while the particle trajectories are simulated using the one-way coupling discrete phase, which is a model particularly effective in case of low concentration of dust. To validate the CFD model, the numerical predictions are compared with experimental data available in the scientific literature. Eight design parameters were chosen, with the two objectives being the minimization of the pressure drop and the maximization of the collection efficiency. Findings The optimization procedure allows the determination of the Pareto Front, which represents the set of the best geometries and can be instrumental in taking an optimal decision in the presence of such a trade-off between the two conflicting objectives. The comparison among the individuals belonging to the Pareto Front with a more standard cyclone geometry shows that such a CFD global search is very effective. Practical implications The proposed procedure is tested for specific values of the operating conditions; however, it has general validity and can be used in place of typical procedures based on empirical models or engineers’ experience for the industrial design of tangential inlet cyclone separators with low solid loading. Originality/value Such an optimization process has never been proposed before for the design of cyclone separators; it has been developed with the aim of being both highly accurate and compatible with the industrial design time.

Tolerance simulation of composite wingbox assembly considering preloading-modified distribution

2016 ◽  
Vol 36 (3) ◽  
pp. 224-232 ◽  
Author(s):  
Hua Wang ◽  
Jun Liu
Keyword(s):  
Probability Distribution ◽  
Tolerance Analysis ◽  
Tolerance Allocation ◽  
Analysis Model ◽  
Element Analysis ◽  
Content Type ◽  
Analysis Process ◽  
Final Assembly ◽  
Thickness Variations ◽  
Input Probability

Purpose Tolerance simulation’s reliability depends on the concordance between the input probability distribution and the real variation. The prescribed clamp force introduced changes in parts’ variation, which should be reflected in the input probability distribution for the tolerance simulation. The paper aims to present a tolerance analysis process of the composite wingbox assembly considering the preloading-modified distribution and especially focuses on the spring-in deviation of the thin-walled C-section composite beam (TC2B). Design/methodology/approach Based on finite element analysis model of TC2B, the preloading-modified probability distribution function (PDF) of the spring-in deviation is obtained. Thickness variations of the TC2B are obtained from the data of the downscaled composite wingbox. These variations are input to the computer-aided tolerance tools, and the final assembly variations are obtained. The assembly of the downscaled wingbox illustrates the effect of preloading on the probability distribution of the spring-in deviation. Findings The results have shown that the final assembly variations estimated with the modified probability distribution is more reliable than the variation of the initial normal distribution. Originality/value The tolerance simulation work presented in the paper will enhance the understanding of the composite parts assembling with spring-in deviations, improve the chance to choose assembling processes that allow specifications to be met and help with tolerance allocation in composites assembly.

scholarly journals Developing a meat supply chain network design using a multi-objective possibilistic programming approach

2017 ◽  
Vol 119 (3) ◽  
pp. 690-706 ◽  
Author(s):  
Ahmed Mohammed ◽  
Qian Wang
Keyword(s):  
Supply Chain ◽  
Network Design ◽  
Meat Products ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Content Type ◽  
Multi Objective ◽  
Possibilistic Programming ◽  
Conflicting Objectives ◽  
Solution Methods

Purpose The purpose of this paper is to present a study in developing a cost-effective meat supply chain network design aiming to minimizing the total cost of transportation, the number of transportation vehicles and the delivery time of meat products. The developed model was also used for determining the optimum numbers and allocations of farms and abattoirs that need to be established and the optimal quantity flow of livestock from farms to abattoirs and meat products from abattoirs to retailers. Design/methodology/approach A multi-objective possibilistic programming model was formulated with a focus on minimizing the total cost of transportation, the number of transportation vehicles and the delivery time of meat products. Three sets of Pareto solutions were obtained using the three different solution methods. These methods are the LP-metrics method, the ɛ-constraint method and the weighted Tchebycheff method, respectively. The TOPSIS method was used for seeking a best Pareto solution as a trade-off decision when optimizing the three conflicting objectives. Findings A case study was also applied for examining the effectiveness and applicability of the developed multi-objective model and the proposed solution methods. The research concludes that the ɛ-constraint method has the superiority over the other two proposed methods as it offers a better solution outcome. Research limitations/implications This work addresses as interesting avenues for further research on exploring the delivery planner under different types of uncertainties and transportation means. Also, environmentalism has been increasingly becoming a significant global problem in the present century. Thus, the presented model could be extended to include the environmental aspects as an objective function. Practical implications The developed design methodology can be utilized for food supply chain designers. Also, it could be applied to realistic problems in the field of supply chain management. Originality/value The paper presents a methodology that can be used for tackling a multi-objective optimization problem of a meat supply chain network design. The proposed optimization method has the potential in solving the similar issue providing a compromising solution due to conflicting objectives in which each needs to be achieved toward an optimum outcome to survive in the competitive sector of food supply chains network.

Improved hybrid Strength Pareto Evolutionary Algorithms for multi-objective optimization

2018 ◽  
Vol 11 (1) ◽  
pp. 20-46 ◽  
Author(s):  
K. Shankar ◽  
Akshay S. Baviskar
Keyword(s):  
Engineering Design ◽  
Nsga Ii ◽  
Design Problems ◽  
Content Type ◽  
Multi Objective ◽  
Decision Variables ◽  
Engineering Design Problems ◽  
Wide Range ◽  
Conflicting Objectives ◽  
Utopia Point

Purpose The purpose of this paper is to design an improved multi-objective algorithm with better spread and convergence than some current algorithms. The proposed application is for engineering design problems. Design/methodology/approach This study proposes two novel approaches which focus on faster convergence to the Pareto front (PF) while adopting the advantages of Strength Pareto Evolutionary Algorithm-2 (SPEA2) for better spread. In first method, decision variables corresponding to the optima of individual objective functions (Utopia Point) are strategically used to guide the search toward PF. In second method, boundary points of the PF are calculated and their decision variables are seeded to the initial population. Findings The proposed methods are tested with a wide range of constrained and unconstrained multi-objective test functions using standard performance metrics. Performance evaluation demonstrates the superiority of proposed algorithms over well-known existing algorithms (such as NSGA-II and SPEA2) and recent ones such as NSLS and E-NSGA-II in most of the benchmark functions. It is also tested on an engineering design problem and compared with a currently used algorithm. Practical implications The algorithms are intended to be used for practical engineering design problems which have many variables and conflicting objectives. A complex example of Welded Beam has been shown at the end of the paper. Social implications The algorithm would be useful for many design problems and social/industrial problems with conflicting objectives. Originality/value This paper presents two novel hybrid algorithms involving SPEA2 based on: local search; and Utopia point directed search principles. This concept has not been investigated before.

A Unified Control Framework for Traction Machine Drive Using Linear Parameters Varying-Based Field-Oriented Control

2020 ◽  
Vol 142 (10) ◽  
Author(s):  
Athar Hanif ◽  
Qadeer Ahmed ◽  
Aamer Iqbal Bhatti ◽  
Giorgio Rizzoni
Keyword(s):  
Operating Conditions ◽  
Linear Matrix ◽  
Field Oriented Control ◽  
Optimization Approach ◽  
Direct Drive ◽  
Multi Objective ◽  
The Road ◽  
Control Framework ◽  
Conflicting Objectives ◽  
The Stability

Abstract The performance of an electrified powertrain in extreme operating conditions is greatly compromised. This is due to the fact that meeting the road loads, ensuring efficient powertrain operation, and minimizing the loss of lifetime (aging) of an electric machine are three essential but conflicting targets. In this paper, a unified multi-objective linear parameters varying (LPV)-based field-oriented control (FOC) framework is proposed to solve the problem of conflicting objectives mentioned above. The outer loop in a unified control framework generates the reference currents using linear matrix inequality (LMI)-based torque and flux controllers. Moreover, optimal flux is estimated using LPV observer to ensure efficient machine operations. In the inner loop of a unified control framework, the multi-objective controller (MOC) is synthesized by selecting optimal weighting functions using LMI-based convex optimization approach. The stability of the proposed unified control framework is also analyzed. The effectiveness of the proposed unified control framework is tested for a direct drive electrified powertrain of a three-wheeled vehicle commonly found in urban transportation for Asian countries. The urban driving schedule-based simulation results confirm that the lifetime of traction machine can be enhanced by appropriate control framework design without compromising its performance.

Shimming design and optimal selection for non-uniform gaps in wing assembly

2017 ◽  
Vol 37 (4) ◽  
pp. 471-482 ◽  
Author(s):  
Qing Wang ◽  
Yadong Dou ◽  
Liang Cheng ◽  
Yinglin Ke
Keyword(s):  
Load Capacity ◽  
Tensile Load ◽  
Manufacturing Cost ◽  
Initial Stiffness ◽  
Three Dimensional Model ◽  
Real Component ◽  
Element Analysis ◽  
Good Efficiency ◽  
Seed Point ◽  
Content Type

Purpose This paper aims to provide a shimming method based on scanned data and finite element analysis (FEA) for a wing box assembly involving non-uniform gaps. The effort of the present work is to deal with gap compensation problem using hybrid shims composed of solid and liquid forms. Design/methodology/approach First, the assembly gaps of the mating components are calculated based on the scanned surfaces. The local gap region is extracted by the seed point and region growth algorithm from the scattered point cloud. Second, with the constraints of hole margin, gap space and shim specification, the optional shimming schemes are designed by the exhaustive searching method. Finally, the three-dimensional model of the real component is reconstructed based on the reverse engineering techniques, such as section lines and sweeping. Using FEA software ABAQUS, the stress distribution and damage status of the joints under tensile load are obtained for optimal scheme selection. Findings With the scanned mating surfaces, the non-uniform gaps are digitally evaluated with accurate measurement and good visualization. By filling the hybrid shims in the assembly gaps, the joint structures possess similar load capacity but stronger initial stiffness compared to the custom-shimmed structures. Practical implications This method has been tested with the interface data of a wing tip, and the results have shown good efficiency and automation of the shimming process. Originality/value The proposed method can decrease the manufacturing cost of shims, shorten the shimming process cycle and improve the assembly efficiency.

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