Machining Chatter Prediction Using a Data Learning Model

Cherukuri, Harish P.; Perez‐Bernabeu, Elena; Sellès, M.A.; Schmitz, Tony L.

doi:10.3390/jmmp3020045

Cited by 32 publications

(18 citation statements)

References 30 publications

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“…The Deep Neural Networks concept came about as a result of using many hidden layers in networks (DNN) [ 18 ]. The ANN is trained once weights obtain the best possible value and then a very minor error.…”

Section: Materials and Methodsmentioning

confidence: 99%

Use of a Novel Polymer-Coated Steel as an Alternative to Traditional Can Manufacturing in the Food Industry

et al. 2021

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Metal containers (both food and beverage cans) are made from huge steel or aluminum coils that are transformed into two- or three-piece products. During the manufacturing process, the metal is sprayed on both sides and the aerosol acts as insulation, but unfortunately produces volatile organic compounds (VOCs). The present work presents a different way to manufacture these containers using a novel prelaminated two-layer polymer steel. It was experimentally possible to verify that the material survives all the involved manufacturing processes. Thus tests were carried out in an ironing simulator to measure roughness, friction coefficient and surface quality. In addition, two theoretical ironing models were developed: upper bound model and artificial neural network. These models are useful for packaging designers and manufacturers.

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Section: Materials and Methodsmentioning

confidence: 99%

Use of a Novel Polymer-Coated Steel as an Alternative to Traditional Can Manufacturing in the Food Industry

et al. 2021

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“…It is possible to have several layers between these two basic layers, and they are known as hidden layers. If there are many hidden layers, then the ANN is called a deep neural network (DNN) [17]. The ANN will be trained once the weights have the best possible value, then obtaining a really low error.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

An Economical and Environmental Alternative to Traditional Can Manufacturing Using a New Pre-Laminated Steel

Sanchez-Caballero

Schmid

Sánchez-Caballero

et al. 2020

2nd Coatings and Interfaces Web Conference (CIWC-2 2020)

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Metal containers are the most commonly used packaging worldwide in both the food processing industry. Usually, the production processes involved in the canning industry include multi-step transformations that take large aluminum or steel coils and make them into two or three-piece cans. During this process, these parts are sprayed to obtain a better surface for the contents; however, this spray produces volatile organic compounds (VOC). This paper presents a new and environmentally friendly can manufacturing method, which uses a novel pre-laminated two-layer polymer steel. As experimentally proven, this innovative polymer-coated steel successfully withstands every manufacturing requirement. The specimens were tested in an ironing simulator, measuring roughness, and friction coefficients. The development of an upper bound ironing model, along with a supporting neural network, allows an insight into the design of new materials for can manufacturing.

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“…Usually, stability lobes diagrams (SLDs) are created before machining, where the boundary between stable and unstable machining is defined in the domain of cutting tool rotational speed and depth of cut [ 2 , 3 ]. Various models are proposed, e.g., artificial neural networks [ 4 ], to describe a stable machining area and predict machining chatter. In practice, the area of stable machining may be different than expected, due to simplifications regarding model linearity and inaccuracy in identifying model parameters.…”

Section: Introductionmentioning

confidence: 99%

Chatter Detection in Milling of Carbon Fiber-Reinforced Composites by Improved Hilbert–Huang Transform and Recurrence Quantification Analysis

Rusinek

Lajmert

2020

Materials

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In the paper, the problem of chatter vibration detection in the milling process of carbon fiber-reinforced plastic is investigated. Chatter analysis may be considered theoretically based on data from impact test of an end mill cutter. However, a stability region obtained in such way may not agree with the real one. Therefore, this paper presents a method that can predict chatter vibrations based on cutting force components measurements. At the beginning, a stability lobe diagram is created to establish the range of experimental test in the plane of tool rotational speed and depth of cut. Next, an experiment of composite milling is performed. The experimentally-measured time series of cutting forces are decomposed with the use of the improved Hilbert–Huang transform (HHT). To detect chatter, statistical methods and recurrence quantification analysis (RQA) are used. However, much better results are obtained when new chatter indexes are proposed. The indexes, derived directly from the HHT and RQA methods, can be used to build an effective chatter prediction system.

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Machining Chatter Prediction Using a Data Learning Model

Cited by 32 publications

References 30 publications

Use of a Novel Polymer-Coated Steel as an Alternative to Traditional Can Manufacturing in the Food Industry

Use of a Novel Polymer-Coated Steel as an Alternative to Traditional Can Manufacturing in the Food Industry

An Economical and Environmental Alternative to Traditional Can Manufacturing Using a New Pre-Laminated Steel

Chatter Detection in Milling of Carbon Fiber-Reinforced Composites by Improved Hilbert–Huang Transform and Recurrence Quantification Analysis

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