Process detection and laser electrochemical composite deposition

Das Forschungsprojekt „Prozessintegrierte Softsensorik zur Oberflächenkonditionierung beim Außenlängsdrehen von 42CrMo4“ widmet sich der Entstehung und der In-process-Erfassung von industriell relevanten Randschichtzuständen. Im Speziellen werden sogenannte White Layer und Eigenspannungszustände untersucht. Durch die modulare Verknüpfung von zerstörungsfreier Prüftechnik, Simulationsergebnissen und Prozesswissen mittels Datenfusion wird ein Softsensor erforscht. Dieser soll im Rahmen einer adaptiven Regelung des Drehprozesses eingesetzt werden und eine gezielte Einstellung von vorteilhaften Randschichtzuständen erlauben. The research project „Process-integrated soft sensor technology for surface conditioning during external longitudinal turning of 42CrMo4“ is dedicated to the formation and in-process-detection of surface layers with industrial relevance. In particular, so-called white layers and residual stresses are investigated. A soft sensor is being researched through the modular combination of non-destructive testing technology and process knowledge by means of data fusion. This is to be used in the context of an adaptive control of the turning process in order to adjust beneficial surface states.

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Finite element modelling of fully-coupled flow/fiber-orientation effects in Polymer Composite Deposition Additive Manufacturing nozzle-extrudate flow

Composites Part B Engineering ◽

10.1016/j.compositesb.2021.108811 ◽

2021 ◽

pp. 108811

Author(s):

Zhaogui Wang ◽

Douglas E. Smith

Keyword(s):

Finite Element ◽

Additive Manufacturing ◽

Polymer Composite ◽

Fiber Orientation ◽

Finite Element Modelling ◽

Coupled Flow ◽

Element Modelling ◽

Orientation Effects ◽

Composite Deposition ◽

Fully Coupled

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A full-polarization GNSS-R Delay-Doppler-Map (DDM) simulator for bare soil freeze/thaw process detection

Geoscience Letters ◽

10.1186/s40562-020-00154-8 ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Xuerui Wu ◽

Shuanggen Jin ◽

Xinqiu Ouyang

Keyword(s):

Bare Soil ◽

Freeze Thaw ◽

Process Detection

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Computation of distribution of relaxation times by Tikhonov regularization for Li ion batteries: usage of L-curve method

Scientific Reports ◽

10.1038/s41598-021-91871-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

T. Paul ◽

P. W. Chi ◽

Phillip M. Wu ◽

M. K. Wu

Keyword(s):

Tikhonov Regularization ◽

Relaxation Times ◽

Detection Methods ◽

Similar Process ◽

Fredholm Integral Equations ◽

Li Ion Batteries ◽

Impedance Data ◽

Curve Method ◽

Li Ion ◽

Process Detection

AbstractIn this paper, the distribution of relaxation times (DRTs) functions are calculated numerically in Matlab for synthetic impedance data from single parallel $$RC$$ RC circuit and two parallel $$RC$$ RC circuits connected in series, experimental impedance data from supercapacitors and α-LiFeO2 anode based Li ion batteries. The quality of the impedance data is checked with the Kramers–Krönig (KK) relations. The DRTs are calculated within the KK compatible regime for all the systems using Tikhonov regularization (TR) method. Here we use a fast and simple L-curve method to estimate the TR parameter (λ) for regularization of the Fredholm integral equations of first kind in impedance. Estimation of the regularization parameters are performed effectively from the offset of the global corner of the L-curve rather than simply using the global corner. The physical significances of DRT peaks are also discussed by calculating the effective resistances and capacitances coupled with peak fitting program. For instance, two peaks in the DRTs justify the electrical double layer capacitance and ion diffusion phenomena for supercapacitors in low to intermediate frequencies respectively. Moreover, the surface film effect, Li/electrolyte and electrode/electrolyte charge transfer related processes are identified for α-LiFeO2 anode based Li-ion batteries. This estimation of the offset of the global corner extends the L-curve approach coupled with the Tikhonov regularization in the field of electrochemistry and can also be applied in similar process detection methods.

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Graph node rank based important keyword detection from Twitter

Applied Computing and Informatics ◽

10.1016/j.aci.2018.08.002 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Mukesh Kumar ◽

Palak Rehan

Keyword(s):

Real Time ◽

English Language ◽

Hybrid Approach ◽

Levenshtein Distance ◽

Social Media Networks ◽

The Social ◽

Keyword Detection ◽

Product Launches ◽

Process Detection ◽

Good Resource

Social media networks like Twitter, Facebook, WhatsApp etc. are most commonly used medium for sharing news, opinions and to stay in touch with peers. Messages on twitter are limited to 140 characters. This led users to create their own novel syntax in tweets to express more in lesser words. Free writing style, use of URLs, markup syntax, inappropriate punctuations, ungrammatical structures, abbreviations etc. makes it harder to mine useful information from them. For each tweet, we can get an explicit time stamp, the name of the user, the social network the user belongs to, or even the GPS coordinates if the tweet is created with a GPS-enabled mobile device. With these features, Twitter is, in nature, a good resource for detecting and analyzing the real time events happening around the world. By using the speed and coverage of Twitter, we can detect events, a sequence of important keywords being talked, in a timely manner which can be used in different applications like natural calamity relief support, earthquake relief support, product launches, suspicious activity detection etc. The keyword detection process from Twitter can be seen as a two step process: detection of keyword in the raw text form (words as posted by the users) and keyword normalization process (reforming the users’ unstructured words in the complete meaningful English language words). In this paper a keyword detection technique based upon the graph, spanning tree and Page Rank algorithm is proposed. A text normalization technique based upon hybrid approach using Levenshtein distance, demetaphone algorithm and dictionary mapping is proposed to work upon the unstructured keywords as produced by the proposed keyword detector. The proposed normalization technique is validated using the standard lexnorm 1.2 dataset. The proposed system is used to detect the keywords from Twiter text being posted at real time. The detected and normalized keywords are further validated from the search engine results at later time for detection of events.

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An Effective Measured Data Preprocessing Method in Electrical Impedance Tomography

The Scientific World JOURNAL ◽

10.1155/2014/208765 ◽

2014 ◽

Vol 2014 ◽

pp. 1-15 ◽

Cited By ~ 2

Author(s):

Chenglong Yu ◽

Shihong Yue ◽

Jianpei Wang ◽

Huaxiang Wang

Keyword(s):

Theoretical Model ◽

Electrical Impedance Tomography ◽

Electrical Impedance ◽

Data Preprocessing ◽

Measured Data ◽

General Criterion ◽

Impedance Tomography ◽

Preprocessing Method ◽

Detection Technology ◽

Process Detection

As an advanced process detection technology, electrical impedance tomography (EIT) has widely been paid attention to and studied in the industrial fields. But the EIT techniques are greatly limited to the low spatial resolutions. This problem may result from the incorrect preprocessing of measuring data and lack of general criterion to evaluate different preprocessing processes. In this paper, an EIT data preprocessing method is proposed by all rooting measured data and evaluated by two constructed indexes based on all rooted EIT measured data. By finding the optimums of the two indexes, the proposed method can be applied to improve the EIT imaging spatial resolutions. In terms of a theoretical model, the optimal rooting times of the two indexes range in [0.23, 0.33] and in [0.22, 0.35], respectively. Moreover, these factors that affect the correctness of the proposed method are generally analyzed. The measuring data preprocessing is necessary and helpful for any imaging process. Thus, the proposed method can be generally and widely used in any imaging process. Experimental results validate the two proposed indexes.

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Electrodeposition Kinetics of Ni/Nano-Y2O3 Composite Coatings

Metals ◽

10.3390/met8090669 ◽

2018 ◽

Vol 8 (9) ◽

pp. 669

Author(s):

Xinyu Zhou ◽

Yiyong Wang ◽

Xianglin Liu ◽

Zhipeng Liang ◽

Hui Jin

Keyword(s):

Electrochemical Impedance ◽

Composite Coatings ◽

Composite Films ◽

Linear Sweep Voltammetry ◽

Electrochemical Measurement ◽

Charge Transfer Resistance ◽

Solid Particles ◽

Kinetics Parameters ◽

Transfer Resistance ◽

Composite Deposition

Ni/nano-Y2O3 composite films were successfully prepared by electrochemical deposition using an acid sulfamate bath. The influence of solid particles added to electrolyte on electrodeposition was investigated by electrochemical measurement methods. The linear sweep voltammetry test showed that the composite deposition took place at a greater potential than that of nickel, and the presence of nano-Y2O3 decreased cathodic polarization. Chronoamperometry studies indicated that the nucleation model of both deposits similarly approached the theoretical instantaneous nucleation mode based on the Scharifker–Hills model. The Y2O3 particles adsorbed on the cathodic surface were shown to facilitate the nucleation/growth of the nickel matrix which is consistent with the deposition kinetics parameters calculated by non-linear fitting experimental curves. The results of electrochemical impedance spectroscopy showed that the presence of Y2O3 particles in a bath is beneficial for the decrease in charge transfer resistance in the deposition. The atomic force microscopy observations of both deposits obtained in the initial electrodeposition stage confirmed that the Ni-Y2O3 composite had a higher grain number and finer mean grain size.

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