Development of graphics user interface (GUI) for process planning in extrusion based additive manufacturing

Krishnanand,; Soni, Shivam; Nayak, Ankit; Taufik, Mohammad

doi:10.1016/j.matpr.2021.02.306

Cited by 7 publications

(3 citation statements)

References 21 publications

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“…Unlike the filament extrusion process, where the material extrusion depends upon feeding rate of the filament 126 and hence it can directly include in part program. 5,127 However, in pellets extrusion-based processes material extrusion depends on interaction of various process parameters such as spindle speed, temperature, and so on, 128 due to this reason material extrusion is dependent parameter and hence it cannot directly include in part program as shown in Supplemental Figure 34(a).…”

Section: Tool Path Generation For Pellets Extrusion 118mentioning

confidence: 99%

Extrusion strategies in fused deposition additive manufacturing: A review

Krishnanand

Singh

Mittal

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part E:

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Fused deposition or fused layer additive manufacturing is a technique in which a part is fabricated layer by layer using filament or pellets-form input material. The extruder is the central part of the fused deposition additive manufacturing (FDAM) system. The extrusion mechanism is unique to the geometry and form of the feedstock material. Extrusion of filament-form material through the nozzle needs to design of a material feeding mechanism based on various filament extrusion parameters. At the same time if the material is in the form of pellets, then the design of the feeding mechanism is very different due to the shape and size of the pellets. These two different design strategies for material extrusion namely the design of the feeding mechanism of filament and pellets form materials have been a major concern to avoid unwanted deformation of extruded materials in FDAM. These two design strategies for filament and pellet-form materials have led to the development of several material extrusion strategies. The present paper reviews various material extrusion strategies developed for FDAM of filament or pellets form input material. This article presents the review of design, development, performance evaluation, and process planning for each extrusion strategy. Based on the literature review authors have suggested a possible extrusion method that can accept both forms of materials to fulfill the need for the filament as well as pellets-based additive manufacturing.

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Section: Tool Path Generation For Pellets Extrusion 118mentioning

confidence: 99%

Extrusion strategies in fused deposition additive manufacturing: A review

Krishnanand

Singh

Mittal

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part E:

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“…Numerous past research used BPNN to optimize parameter, predict fatigue life and predict weld quality of RSW [6]- [12]. A graphical user interface (GUI) is used to make the ANN algorithm in graphical form that is easy to use by end user to make predictions [13], [14]. A user friendly and easy to use GUI is beneficial to be developed as to effectively deal with the developed ANN algorithm.…”

Section: Introductionmentioning

confidence: 99%

Development of Open-sourced, Self-executable and GUI-based Application Tool Q-Check for Quality Prediction of Resistance Spot Weld using Artificial Neural Network

Raziq

Manurung

Halim

et al. 2022

Preprint

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Optimizing Resistance spot welding (RSW), often used as a time and cost-effective process in many industrial sectors, is very time-consuming due to the obscurity inherent within process with numerous interconnected welding parameters. Small changes in values will give effect to the quality of welds which actually can be easily analysed using application tool. Unfortunately, existing software to optimize the parameters are expensive, licensed and inflexible which makes small industries and research centres refused to acquire. In this study, application tool using open-sourced and customized algorithm based on artificial neural networks (ANN) was developed to enable better, fast, cheap and practical predictions of major parameters such as welding time, current and electrode force on tensile shear load bearing capacity (TSLBC) and weld quality classifications (WQC). A supervised learning algorithm implemented in standard backpropagation neural network gradient descent (GD), stochastic gradient descent (SGD) and Levenberg-Marquardt (LM) was constructed using TensorFlow with Spyder IDE in python language. All the display and calculation processes are developed and compiled in the form of graphical user interface (GUI) using Qt designer and PyQt5. The standalone capability was created with customized icon by using Pyinstaller to enable a self-execution in windows platform. Results showed that this low-cost application tool Q-Check based on ANN models can predict with 80% training and 20% test set on TSLBC with an accuracy of 87.220%, 92.865% and 93.670% for GD, SGD and LM algorithms respectively while on WQC 62.5% for GD and 75% for both SGD and LM. It is also expected that tool with flexible GUI can be widely used and enhanced by practitioner with minimum knowledge in the domain.

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“…Numerous past research used BPNN to optimize parameter, predict fatigue life and predict weld quality of RSW [6][7][8][9][10][11][12] . A graphical user interface (GUI) is used to make the ANN algorithm in graphical form that is easy to use by end user to make predictions 13,14 . A user friendly and easy to use GUI is beneficial to be developed as to effectively deal with the developed ANN algorithm.…”

mentioning

confidence: 99%

Quality prediction and classification of resistance spot weld using artificial neural network with open-sourced, self-executable and GUI-based application tool Q-Check

Halim

Manurung

Raziq

et al. 2023

Sci Rep

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Optimizing Resistance spot welding, often used as a time and cost-effective process in many industrial sectors, is very time-consuming due to the obscurity inherent within process with numerous interconnected welding parameters. Small changes in values will give effect to the quality of welds which actually can be easily analysed using application tool. Unfortunately, existing software to optimize the parameters are expensive, licensed and inflexible which makes small industries and research centres refused to acquire. In this study, application tool using open-sourced and customized algorithm based on artificial neural networks (ANN) was developed to enable better, fast, cheap and practical predictions of major parameters such as welding time, current and electrode force on tensile shear load bearing capacity (TSLBC) and weld quality classifications (WQC). A supervised learning algorithm implemented in standard backpropagation neural network gradient descent (GD), stochastic gradient descent (SGD) and Levenberg–Marquardt (LM) was constructed using TensorFlow with Spyder IDE in python language. All the display and calculation processes are developed and compiled in the form of application tool of graphical user interface (GUI). Results showed that this low-cost application tool Q-Check based on ANN models can predict with 80% training and 20% test set on TSLBC with an accuracy of 87.220%, 92.865% and 93.670% for GD, SGD and LM algorithms respectively while on WQC 62.5% for GD and 75% for both SGD and LM. It is also expected that tool with flexible GUI can be widely used and enhanced by practitioner with minimum knowledge in the domain.

show abstract

Development of graphics user interface (GUI) for process planning in extrusion based additive manufacturing

Cited by 7 publications

References 21 publications

Extrusion strategies in fused deposition additive manufacturing: A review

Extrusion strategies in fused deposition additive manufacturing: A review

Development of Open-sourced, Self-executable and GUI-based Application Tool Q-Check for Quality Prediction of Resistance Spot Weld using Artificial Neural Network

Quality prediction and classification of resistance spot weld using artificial neural network with open-sourced, self-executable and GUI-based application tool Q-Check

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