The intensifying of the manufacturing process and increasing the efficiency of production planning of precise and non-rigid parts, mainly crankshafts, are the first-priority task in modern manufacturing. The use of various methods for controlling the cutting force under cylindrical infeed grinding and studying its impact on crankpin machining quality and accuracy can improve machining efficiency. The paper deals with developing a comprehensive scientific and methodological approach for determining the experimental dependence parameters’ quantitative values for cutting-force calculation in cylindrical infeed grinding. The main stages of creating a method for conducting a virtual experiment to determine the cutting force depending on the array of defining parameters obtained from experimental studies are outlined. It will make it possible to get recommendations for the formation of a valid route for crankpin machining. The research’s scientific novelty lies in the developed scientific and methodological approach for determining the cutting force, based on the integrated application of an artificial neural network (ANN) and multi-parametric quasi-linear regression analysis. In particular, on production conditions, the proposed method allows the rapid and accurate assessment of the technological parameters’ influence on the power characteristics for the cutting process. A numerical experiment was conducted to study the cutting force and evaluate its value’s primary indicators based on the proposed method. The study’s practical value lies in studying how to improve the grinding performance of the main bearing and connecting rod journals by intensifying cutting modes and optimizing the structure of machining cycles.

https://www.mdpi.com/1996-1944/13/23/5357/pdf

Parameter Identification of Cutting Forces in Crankshaft Grinding Using Artificial Neural Networks.

Active-learning surrogate model–based reliability analysis is widely employed in engineering structural reliability analysis to alleviate the computational burden of the Monte Carlo method. To date, most of these methods are built based on the single-fidelity surrogate model, such as the Kriging model. However, the computational burden of constructing a fine Kriging model may be still expensive if the high-fidelity (HF) simulation is extremely time-consuming. To solve this problem, an active-learning method based on the multi-fidelity (MF) Kriging model for structural reliability analysis (abbreviated as AMK-MCS+AEFF), which is an online data-driven method fusing information from different fidelities, is proposed in this paper. First, an augmented expected feasibility function (AEFF) is defined by considering the cross-correlation, the sampling density, and the cost query between HF and low-fidelity (LF) models. During the active-learning process of AMK-MCS+AEFF, both the location and fidelity level of the updated sample can be determined objectively and adaptively by maximizing the AEFF. Second, a new stopping criterion that associates with the estimated relative error is proposed to ensure that the iterative process terminates in a proper iteration. The proposed method is compared with several state-of-the-art methods through three numerical examples and an engineering case. Results show that the proposed method can provide an accurate failure probability estimation with a less computational cost.

An active-learning method based on multi-fidelity Kriging model for structural reliability analysis

Active learning for structural reliability: Survey, general framework and benchmark

The application of white box models in digital twins is often hindered by missing knowledge, uncertain information and computational difficulties. Our aim was to overview the difficulties and challenges regarding the modelling aspects of digital twin applications and to explore the fields where surrogate models can be utilised advantageously. In this sense, the paper discusses what types of surrogate models are suitable for different practical problems as well as introduces the appropriate techniques for building and using these models. A number of examples of digital twin applications from both continuous processes and discrete manufacturing are presented to underline the potentials of utilising surrogate models. The surrogate models and model-building methods are categorised according to the area of applications. The importance of keeping these models up to date through their whole model life cycle is also highlighted. An industrial case study is also presented to demonstrate the applicability of the concept.

https://www.mdpi.com/2227-9717/9/3/476/pdf

Modelling for Digital Twins—Potential Role of Surrogate Models

Vectorial surrogate modeling method for multi-objective reliability design

AK-DS: An adaptive Kriging-based directional sampling method for reliability analysis

It is widely recognized that the active learning kriging (AK) combined with Monte Carlo simulation (AK-MCS) is a very efficient strategy for failure probability estimation. However, for the rare failure event, the AK-MCS would be time-consuming due to the large size of the sample pool. Therefore, an efficient method coupling the subset simulation (SS) with AK is proposed to overcome the time-consuming character of AK-MCS in case of estimating the small failure probability. The SS strategy is firstly employed by the proposed method to transform the small failure probability into the product of a series of larger conditional failure probabilities of the introduced intermediate failure events. Then, a kriging model is iteratively updated for each intermediate failure event until all the conditional failure probabilities are obtained by the well-trained kriging model, on which the failure probability will be estimated by the product of these conditional failure probabilities. The proposed method significantly reduces the number of evaluating the actual complicated limit state function compared with AK-MCS, and it overcomes the time-consuming character of AK-MCS since the sample pool size of SS is significantly smaller than that of MCS. The presented examples demonstrate the efficiency and accuracy of the proposed method.

A coupled subset simulation and active learning kriging reliability analysis method for rare failure events

Reliability index function approximation based on adaptive double-loop Kriging for reliability-based design optimization

Surrogate models are often used as surrogates for computationally intensive simulations. And there are a variety of surrogate models which are widely used in aerospace engineering–related investigation and design. In general, there is an optimal individual surrogate for a certain research object. However, the behavior of an individual surrogate is unknown in advance. Building an ensemble of surrogates by combining different individual surrogates into a weighted-sum formulation is an efficient method to enhance the accuracy and robustness of the surrogate model. Motivated by the previous researches on the ensemble of surrogates, we propose an optimal pointwise weighted ensemble (OPWE) method, wherein the optimal pointwise weight factors are obtained based on the minimization of the local mean square error which is constructed by the global-local error (GLE). By using six well-known mathematical problems and four engineering problems, it is proved that the OPWE proposed in this paper is better than the other ensembles of surrogates in terms of both accuracy and robustness.

Xiaobo Zhang

Papers

AK-DS: An adaptive Kriging-based directional sampling method for reliability analysis

A coupled subset simulation and active learning kriging reliability analysis method for rare failure events

Reliability index function approximation based on adaptive double-loop Kriging for reliability-based design optimization

An optimal pointwise weighted ensemble of surrogates based on minimization of local mean square error

Sequential ensemble optimization based on general surrogate model prediction variance and its application on engine acceleration schedule design