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FEM and Analytical Modeling of the Incipient Chip Formation for the Generation of Micro-Features.

ABSTRACT: This paper explores the modeling of incipient cutting by Abaqus, LS-Dyna, and Ansys Finite Element Methods (FEMs), by comparing also experimentally the results on different material classes, including common aluminum and steel alloys and an acetal polymer. The target application is the sustainable manufacturing of gecko adhesives by micromachining a durable mold for injection molding. The challenges posed by the mold shape include undercuts and sharp tips, which can be machined by a special diamond blade, which enters the material, forms a chip, and exits. An analytical model to predict the shape of the incipient chip and of the formed grove as a function of the material properties and of the cutting parameters is provided. The main scientific merit of the current work is to approach theoretically, numerically, and experimentally the very early phase of the cutting tool penetration for new sustainable machining and micro-machining processes.

SUBMITTER: Lanzetta M

PROVIDER: S-EPMC8306726 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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FEM and Analytical Modeling of the Incipient Chip Formation for the Generation of Micro-Features.

Lanzetta Michele M Gharibi Armin A Picchi Scardaoni Marco M Vivaldi Claudia C

Materials (Basel, Switzerland) 20210706 14

This paper explores the modeling of incipient cutting by Abaqus, LS-Dyna, and Ansys Finite Element Methods (FEMs), by comparing also experimentally the results on different material classes, including common aluminum and steel alloys and an acetal polymer. The target application is the sustainable manufacturing of gecko adhesives by micromachining a durable mold for injection molding. The challenges posed by the mold shape include undercuts and sharp tips, which can be machined by a special diam ...[more]

PMID: 34300708

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Project description:BackgroundThe ChIP-chip technology has been used in a wide range of biomedical studies, such as identification of human transcription factor binding sites, investigation of DNA methylation, and investigation of histone modifications in animals and plants. Various methods have been proposed in the literature for analyzing the ChIP-chip data, such as the sliding window methods, the hidden Markov model-based methods, and Bayesian methods. Although, due to the integrated consideration of uncertainty of the models and model parameters, Bayesian methods can potentially work better than the other two classes of methods, the existing Bayesian methods do not perform satisfactorily. They usually require multiple replicates or some extra experimental information to parametrize the model, and long CPU time due to involving of MCMC simulations.ResultsIn this paper, we propose a Bayesian latent model for the ChIP-chip data. The new model mainly differs from the existing Bayesian models, such as the joint deconvolution model, the hierarchical gamma mixture model, and the Bayesian hierarchical model, in two respects. Firstly, it works on the difference between the averaged treatment and control samples. This enables the use of a simple model for the data, which avoids the probe-specific effect and the sample (control/treatment) effect. As a consequence, this enables an efficient MCMC simulation of the posterior distribution of the model, and also makes the model more robust to the outliers. Secondly, it models the neighboring dependence of probes by introducing a latent indicator vector. A truncated Poisson prior distribution is assumed for the latent indicator variable, with the rationale being justified at length.ConclusionThe Bayesian latent method is successfully applied to real and ten simulated datasets, with comparisons with some of the existing Bayesian methods, hidden Markov model methods, and sliding window methods. The numerical results indicate that the Bayesian latent method can outperform other methods, especially when the data contain outliers.

Dataset Information

FEM and Analytical Modeling of the Incipient Chip Formation for the Generation of Micro-Features.

Publications

FEM and Analytical Modeling of the Incipient Chip Formation for the Generation of Micro-Features.

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