Statistical Analysis and Modelling of an Yb: KGW Femtosecond Laser Micro-drilling Process

Casalino, Giuseppe; Losacco, A.M.; Arnesano, Angela; Facchini, Francesco; Pierangeli, Maurizio; Bonserio, C.

doi:10.1016/j.procir.2016.06.111

Cited by 30 publications

(12 citation statements)

References 20 publications

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“…In this case an adaptive learning rate allows to keep the learning step size as large as possible, ensuring, in this way, the learning rate stable. The LM algorithm allows to minimize the squares of the differences (E) between the desirable output, identified as y d (t), and the predicted output y p (t) [73]. 'E' is given by the follow equation:…”

Section: Learning Algorithmmentioning

confidence: 99%

ANN Modelling to Optimize Manufacturing Process

Filippis¹,

Serio²,

Facchini³

et al. 2018

Advanced Applications for Artificial Neural Networks

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Neural network (NN) model is an efficient and accurate tool for simulating manufacturing processes. Various authors adopted artificial neural networks (ANNs) to optimize multiresponse parameters in manufacturing processes. In most cases the adoption of ANN allows to predict the mechanical proprieties of processed products on the basis of given technological parameters. Therefore the implementation of ANN is hugely beneficial in industrial applications in order to save cost and material resources. In this chapter, following an introduction on the application of the ANN to the manufacturing process, it will be described an important study that has been published on international journals and that has investigated the use of the ANNs for the monitoring, controlling and optimization of the process. Experimental observations were collected in order to train the network and establish numerical relationships between process-related factors and mechanical features of the welded joints. Finally, an evaluation of time-costs parameters of the process, using the control of the ANN model, is conducted in order to identify the costs and the benefits of the prediction model adopted.

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Section: Learning Algorithmmentioning

confidence: 99%

ANN Modelling to Optimize Manufacturing Process

Filippis¹,

Serio²,

Facchini³

et al. 2018

Advanced Applications for Artificial Neural Networks

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“…To overcome these shortcomings, various types of models can be utilized [7][8][9][10][11]. For example, a numerical model based on previous studies [7,10,11] can be used as an alternative means to obtain quantitative information on solidification parameters.…”

Section: Introductionmentioning

confidence: 99%

Numerical and Experimental Investigations of Laser Metal Deposition (LMD) Using STS 316L

Park

Kim

et al. 2020

Applied Sciences

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This study aimed to understand the effect of heat accumulation on microstructure formation on STS 316L during multilayer deposition by a laser metal deposition (LMD) process and to predict the microstructure morphology. A comprehensive experimental and numerical study was conducted to quantify the solidification parameters (temperature gradient (G) and growth rate (R)) in the LMD multilayer deposition process. During deposition, the temperature profile at a fixed point in the deposit was measured to validate the numerical model, and then the solidification parameters were quantified using the model. Simultaneously, the microstructure of the deposit was investigated to confirm the microstructure morphology. Then, a relationship between the microstructure morphology and the G/R was proposed using a solidification map. The findings of this study can guide the design of scanning paths to produce deposits with a uniform structure.

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“…Various codes and standards as well as recommended practices (e.g., [6,7]) are widely adopted for fatigue evaluation of welded joints. The fatigue life of a welded joint is significantly influenced by the stress concentration effect at welded joints [8][9][10][11]. The study of characterizing the stress concentration effects and achieving effective notch stress calculation at welds are important research directions in the field of predicting the fatigue life of welded components.…”

Section: Introductionmentioning

confidence: 99%

An Improved 2D Meshfree Radial Point Interpolation Method for Stress Concentration Evaluation of Welded Component

et al. 2020

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The study of characterizing the stress concentration effects at welds is one of the most important research directions for predicting the fatigue life of welded components. Stress solutions at the weld toe obtained from conventional meshfree methods are strongly influenced by parameters used in the methods as a result of stress singularity. In this study, an improved 2D meshfree radial point interpolation method (RPIM) is proposed for stress concentration evaluation of a welded component. The stress solutions are insensitive to parameters used in the improved RPIM. The improved RPIM-based scheme for consistently calculating stress concentration factor (SCF) and stress intensity factor at weld toe are presented. Our studies provide a novel approach to apply global weak-form meshfree methods in consistently computing SCFs and stress intensity factors at welds.

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Statistical Analysis and Modelling of an Yb: KGW Femtosecond Laser Micro-drilling Process

Cited by 30 publications

References 20 publications

ANN Modelling to Optimize Manufacturing Process

ANN Modelling to Optimize Manufacturing Process

Numerical and Experimental Investigations of Laser Metal Deposition (LMD) Using STS 316L

An Improved 2D Meshfree Radial Point Interpolation Method for Stress Concentration Evaluation of Welded Component

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