A Method for Designing Assembly Tolerance Networks of Mechanical Assemblies

Zhang, Yi; Li, Zongbin; Gao, Jianmin; Hong, Jun; Villecco, Francesco; Li, Yunlong

doi:10.1155/2012/513958

Cited by 24 publications

(17 citation statements)

References 20 publications

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“…The principal goal of the authors' research is the development of new methods for the design and the analysis of complex mechanical systems [41][42][43][44][45][46][47][48][49][50][51]. In general, the designer of complex systems is often called upon to select a suitable solution among several alternatives.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Uncertainties in the Design Process of Complex Mechanical Systems

Villecco

Pellegrino

2017

Entropy

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Abstract:In this paper, the problem of the evaluation of the uncertainties that originate in the complex design process of a new system is analyzed, paying particular attention to multibody mechanical systems. To this end, the Wiener-Shannon's axioms are extended to non-probabilistic events and a theory of information for non-repetitive events is used as a measure of the reliability of data. The selection of the solutions consistent with the values of the design constraints is performed by analyzing the complexity of the relation matrix and using the idea of information in the metric space. Comparing the alternatives in terms of the amount of entropy resulting from the various distribution, this method is capable of finding the optimal solution that can be obtained with the available resources. In the paper, the algorithmic steps of the proposed method are discussed and an illustrative numerical example is provided.

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Section: Discussionmentioning

confidence: 99%

Evaluation of Uncertainties in the Design Process of Complex Mechanical Systems

Villecco

Pellegrino

2017

Entropy

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“…The main goal of the research of the authors is the development of nonconventional methodologies for the design and analysis of engineering solutions for complex mechanical systems [33][34][35][36][37][38][39][40][41][42][43]. In this paper, stochastic and epistemic uncertainty were analyzed, extending Wiener-Shannon's information theory to non-probabilistic events by introducing the theory of information for non-repetitive events as a measure of data consistency.…”

Section: Discussionmentioning

confidence: 99%

Entropic Measure of Epistemic Uncertainties in Multibody System Models by Axiomatic Design

Villecco

Pellegrino

2017

Entropy

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Abstract:In this paper, the use of the MaxInf Principle in real optimization problems is investigated for engineering applications, where the current design solution is actually an engineering approximation. In industrial manufacturing, multibody system simulations can be used to develop new machines and mechanisms by using virtual prototyping, where an axiomatic design can be employed to analyze the independence of elements and the complexity of connections forming a general mechanical system. In the classic theories of Fisher and Wiener-Shannon, the idea of information is a measure of only probabilistic and repetitive events. However, this idea is broader than the probability alone field. Thus, the Wiener-Shannon's axioms can be extended to non-probabilistic events and it is possible to introduce a theory of information for non-repetitive events as a measure of the reliability of data for complex mechanical systems. To this end, one can devise engineering solutions consistent with the values of the design constraints analyzing the complexity of the relation matrix and using the idea of information in the metric space. The final solution gives the entropic measure of epistemic uncertainties which can be used in multibody system models, analyzed with an axiomatic design.

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“…In this context, the estimation process is an iterative process in which the mathematical structure of a dynamical model is devised and, once a suitable mathematical model is constructed for capturing the physics of the system of interest, one can identify the unknown parameters in the model by using an experimental dataset [6][7][8][9]. This process is guided by the intuition of the analyst and is based on the useful information embedded in the dataset available from the experimental campaign [10][11][12][13][14][15][16]. In the validation process, on the other hand, the analyst verifies that the selected dynamical model and the identified model parameters are able to reproduce the input-output datasets that are different from those used for constructing the system model by means of the system identification methods.…”

Section: Introductionmentioning

confidence: 99%

System Identification Algorithm for Computing the Modal Parameters of Linear Mechanical Systems

Pappalardo

Guida

2018

Machines

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Abstract:The goal of this investigation is to construct a computational procedure for identifying the modal parameters of linear mechanical systems. The methodology employed in the paper is based on the Eigensystem Realization Algorithm implemented in conjunction with the Observer/Kalman Filter Identification method (ERA/OKID). This method represents an effective and efficient system identification numerical procedure based on the time domain. The algorithm developed in this work is tested by means of numerical experiments on a full-car vehicle model. To this end, the modal parameters necessary for the design of active and semi-active suspension systems are obtained for the vehicle system considered as an illustrative example. In order to analyze the performance of the methodology developed in this investigation, the system identification numerical procedure was tested considering two case studies, namely a full state measurement and an incomplete state measurement. As expected, the numerical results found for the identified dynamical model showed a good agreement with the modal parameters of the mechanical system model. Furthermore, numerical results demonstrated that the proposed method has good performance considering a scenario in which the signal-to-noise ratio of the input and output measurements is relatively high. The method developed in this paper can be effectively used for solving important engineering problems such as the design of control systems for road vehicles.

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A Method for Designing Assembly Tolerance Networks of Mechanical Assemblies

Cited by 24 publications

References 20 publications

Evaluation of Uncertainties in the Design Process of Complex Mechanical Systems

Evaluation of Uncertainties in the Design Process of Complex Mechanical Systems

Entropic Measure of Epistemic Uncertainties in Multibody System Models by Axiomatic Design

System Identification Algorithm for Computing the Modal Parameters of Linear Mechanical Systems

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