An efficient two-phase approach for reliable collaboration-aware service composition in cloud manufacturing

Real-time detection of early chatter is a vital strategy to improve machining quality and material removal rate in the high-speed milling processes. This paper proposes a maximum entropy (MaxEnt) feature-based reliability model method for real-time detection of early chatter based on multiple sampling per revolution (MSPR) technique and second-order reliability method (SORM). To enhance the detection reliability, the MSPR is used to acquire multiple sets of once-per-revolution sampled data (i.e., MSPR data) and to overcome the shortcoming of the once-per-revolution sampling. The proposed MaxEnt feature-based reliability model method solves the issue of the real-time detection of early chatter while ensuring its reliability. The failure hazard function (FHF) is estimated as a chatter indicator by using the SORM with the MaxEnt feature. The proposed method consists of five steps. First, set the prior parameters. Then collect data by using the MSPR technique. Next, calculate a set of the standard deviation of the data collected as a chatter feature and estimate the chatter indicator FHF by applying the SORM with the MaxEnt feature. Finally, implement the real-time detection of early chatter based on the estimated chatter indicator FHF and the threshold FHF0. The proposed method is applied to the high-speed milling process. Two examples prove that the proposed method can detect two kinds of early chatter: the early-stage of a severe chatter and the slightly intolerable chatter.

https://hal.univ-lorraine.fr/hal-03018329/document

MaxEnt feature-based reliability model method for real-time detection of early chatter in high-speed milling

Governments have been challenged to provide timely medical care to face the COVID-19 pandemic. Under this pandemic, the demand for pharmaceutical products has changed significantly. Some of these products are in high demand, while, for others, their demand falls sharply. These changes in the random demand patterns are connected with changes in the skewness (asymmetry) and kurtosis of their data distribution. Such changes are critical to determining optimal lots and inventory costs. The lot-size model helps to make decisions based on probabilistic demand when calculating the optimal costs of supply using two-stage stochastic programming. The objective of this study is to evaluate how the skewness and kurtosis of the distribution of demand data, collected through sensors, affect the modeling of inventories of hospital pharmacy products helpful to treat COVID-19. The use of stochastic programming allows us to obtain results under demand uncertainty that are closer to reality. We carry out a simulation study to evaluate the performance of our methodology under different demand scenarios with diverse degrees of skewness and kurtosis. A case study in the field of hospital pharmacy with sensor-related COVID-19 data is also provided. An algorithm that permits us to use sensors when submitting requests for supplying pharmaceutical products in the hospital treatment of COVID-19 is designed. We show that the coefficients of skewness and kurtosis impact the total costs of inventory that involve order, purchase, holding, and shortage. We conclude that the asymmetry and kurtosis of the demand statistical distribution do not seem to affect the first-stage lot-size decisions. However, demand patterns with high positive skewness are related to significant increases in expected inventories on hand and shortage, increasing the costs of second-stage decisions. Thus, demand distributions that are highly asymmetrical to the right and leptokurtic favor high total costs in probabilistic lot-size systems.

https://www.mdpi.com/1424-8220/21/15/5198/pdf

Lot-Size Models with Uncertain Demand Considering Its Skewness/Kurtosis and Stochastic Programming Applied to Hospital Pharmacy with Sensor-Related COVID-19 Data.

With the prevalence of online individual micro-loans, an increasing number of peer-to-peer lending platforms have been established during the last ten years. One main problem for these platforms is...

CF-NN: a novel decision support model for borrower identification on the peer-to-peer lending platform

Reliability updating and parameter inversion of micro-milling

A reliability analysis of the milling system with uncertainties is developed in this paper to predict reliable chatter-free machining parameters. The chatter reliability refers to the probability o...

Reliability analysis of chatter stability for milling process system with uncertainties based on neural network and fourth moment method

Analysis of the generation mechanism and evolution of the wheel high-order polygonal wear of subway trains

In machining parameters optimization of a chatter-free milling process, the inevitable surface location error (SLE) reflecting the machined workpiece dimension accuracy has been barely considered as one objective representing the machining quality, lowering the optimization accuracy. Therefore, this paper provides an approach to establish a multi-objective optimization model, where the material removal rate (MRR) represents the machining efficiency and the SLE predicted in time-domain represents the machining quality. The non-dominated sorting genetic algorithm (NSGA-II) method is used to solve the multi-objective model and provide pareto optimal solutions to first determine some ideal optimal solutions. Then the analytic hierarchy process (AHP) and grey target decision (GTD) methods are combined to select one most satisfactory optimal solution which has a well balance between the MRR and SLE. A multi-objective model was established and taken as a case study to maximize the MRR and minimize the SLE. Comparison study was performed on this multi-objective model and two other mono-objective models for obtaining the optimal MRR and SLE respectively, which was combined with the influences of machining parameters on SLE to show the necessity of conducting a multi-objective optimization. Milling tests were conducted based on the solved optimal machining parameters, and the well consistence between the measured and predicted SLEs shows that the proposed multi-objective optimization method can provide an effective approach to balance the machining efficiency and quality when there are conflicts between different objectives.

/pdf/multi-objective-machining-parameters-optimization-for-3f5r7ddmb3.pdf

Multi-Objective Machining Parameters Optimization for Chatter-Free Milling Process Considering Material Removal Rate and Surface Location Error

Multi-objective modelling and optimal parameter selection of a multi-pass milling process considering uncertain milling stability constraint

A dynamic textile weaving simulator is established to connect weaving actions to fabric patterns and microstructures. It utilizes the Digital Element Approach (DEA) under the framework of the software package Digital Fabric and Composite Analyser (DFCA). The key components of a Jacquard loom are explicitly modelled utilising the ‘hole/no hole’ principle. Yarn interlacing motion is guided by weaving matrix specified by steps. Shedding, weft insertion, beat-up, and take-up actions are modelled and explained. The inter-fibre contact force, fibre forces (tensile, shear, and bending), and boundary conditions in the weft direction are considered. The weaving process of five cells in the warp direction of a 10-layer 3D orthogonal woven fabric is simulated at the filament level to derive for its microstructure. The results show that the fabric microstructure continues to change after being woven, and the thickness and length of each individual cell decrease with further weaving steps. The microstructures of newly woven cells converge after the weaving of two further cells in the lengthwise direction. The microstructure of the second cell closely matches that of an actual weaved fabric, as evaluated by microscopy images. Penetration occurs between adjacent weft yarns in the same column, which fundamentally changes their microstructure, composition, and material properties. Parametric studies show that the ratio of binder to warp yarn tension determines the fabric thickness. Increases in the take-up length and binder yarn tension lead to a decrease and increase in the reed tension, respectively. This work provides valuable insights into fabric design and manufacture instruction.

https://doi.org/10.1177/15280837221139554

A dynamic weaving simulator for filament-level 3D woven fabric modelling

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