Additively Manufactured Parametric Universal Clip-System: An Open Source Approach for Aiding Personal Exposure Measurement in the Breathing Zone

Kukko, Kirsi; Akmal, Jan Sher; Kangas, Anneli; Salmi, Mika; Björkstrand, Roy; Viitanen, Anna-Kaisa; Partanen, Jouni; Pearce, Joshua M.

doi:10.3390/app10196671

Cited by 10 publications

(7 citation statements)

References 39 publications

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“…However, such open-source hardware and software options for single-point strain measurement were not found in the literature [36] , [37] , [38] , [39] . Nowadays, researchers are focusing on developing low-cost alternatives to costly commercial systems for various measurements, such as detection of malignant tumor cells, yarn parameterization, and exposure measurement in the breathing zone [40] , [41] , [42] . Therefore, an attempt has been made to develop a portable device to acquire the deformed grid images for further processing using the developed in-house software for strain analysis.…”

Section: Hardware In Contextmentioning

confidence: 99%

A portable device for single point strain analysis in sheet metal forming processes

Wankhede

Narayanaswamy²,

Suresh

et al. 2022

HardwareX

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Section: Hardware In Contextmentioning

confidence: 99%

A portable device for single point strain analysis in sheet metal forming processes

Wankhede

Narayanaswamy²,

Suresh

et al. 2022

HardwareX

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“…The impact of additive manufacturing (AM) in today's world is undoubtedly all-time high ranging from low-cost and educational applications to high-performance complex engineering components. AM is emerging into a general-purpose technology enabling industry 4.0 and offering a plethora of applications [1][2][3][4]. Out of the seven ISO/ASTM registered technologies, powder bed fusion (PBF) process is widely used for manufacturing metal AM components using laser as thermal input and metal powder as raw material [4,5].…”

Section: Introductionmentioning

confidence: 99%

Prediction and validation of melt pool dimensions and geometric distortions of additively manufactured AlSi10Mg

Ullah

Lian

Akmal

et al. 2023

Int J Adv Manuf Technol

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A finite element–based thermomechanical modeling approach is developed in this study to provide a prediction of the mesoscale melt pool behavior and part-scale properties for AlSi10Mg alloy. On the mesoscale, the widely adopted Goldak heat source model is used to predict melt pool formed by laser during powder bed fusion process. This requires the determination of certain parameters as they control temperature distribution and, hence, melt pool boundaries. A systematic parametric approach is proposed to determine parameters, i.e., absorption coefficient and transient temperature evolution. The simulation results are compared in terms of morphology of melt pool with the literature results. Considering the part-scale domain, there is increasing demand for predicting geometric distortions and analyzing underlying residual stresses, which are highly influenced by the mesh size and initial temperature setup. This study aims to propose a strategy for evaluating the correlation between the mesh size and the initial temperature to provide correct residual stresses when increasing the scale of the model for efficiency. The outcomes revealed that the predicted melt pool error produced by optimal Goldak function parameters is between 5 and 12%. On the part-scale, the finite element model is less sensitive to mesh size for distortion prediction, and layer-lumping can be used to increase the speed of simulation. The effect of large time increments and layer lumping can be compensated by appropriate initial temperature value for AlSi10Mg. The study aids practitioners and researchers to establish and validate design for additive manufacturing within the scope of desired part quality metrics.

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“…IOP Publishing doi:10.1088/1757-899X/1296/1/012013 2 prototyping [6,7], and end-use part [8][9][10][11] applications in a wide range of industries, e.g., medical, industrial machines, defence, automotive, and aerospace industries.…”

Section: Introductionmentioning

confidence: 99%

Defect detection in laser-based powder bed fusion process using machine learning classification methods

Akmal,

Macarie,

Björkstrand

et al. 2023

IOP Conf. Ser.: Mater. Sci. Eng.

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The aim of this study is to deploy machine learning (ML) classification methods to detect defective regions in additive manufacturing, colloquially known as 3D printing, particularly for the laser-based powder bed fusion process. A custom-designed test specimen composed of 316L was manufactured using EOS M 290 machine. Multinomial logistic regression (MLR), artificial neural network (ANN), and convolutional neural network (CNN) classification techniques were applied to train the ML models using optical tomography infrared images of each additively manufactured layer of test specimen. Based on the trained MLR, ANN, and CNN classifiers, the ML models predict whether the manufactured layer is standard or defective, yielding five classes. Defective layers were classified into two classes for lack of fusion and two classes for keyhole porosity. The supervised approach yielded impeccable accuracy (>99%) for all three classification methods, however CNN inherited the highest degree of performance with 100% accuracy for independent test dataset unfamiliar to the model for unbiased evaluation. The high performance and low cost of computing observed in this work can have the potential to detect and eliminate defective regions by tuning the processing parameters in real time resulting in significantly decreased costs, lead-time, and waste. The proposed quality control can enable mass adoption of additive manufacturing technologies in a vast number of industries for critical components that are design- and shape- agnostic.

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Additively Manufactured Parametric Universal Clip-System: An Open Source Approach for Aiding Personal Exposure Measurement in the Breathing Zone

Cited by 10 publications

References 39 publications

A portable device for single point strain analysis in sheet metal forming processes

A portable device for single point strain analysis in sheet metal forming processes

Prediction and validation of melt pool dimensions and geometric distortions of additively manufactured AlSi10Mg

Defect detection in laser-based powder bed fusion process using machine learning classification methods

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