Comparative Analysis of Additive Manufacturing over Conventional Manufacturing

Subramanyam, Bh.; Vineeta, T.V.; Garre, Parthasarathy; Bharadwaj, V.V.S. Nikhil; Shashank, P. Shiva

doi:10.1088/1757-899x/455/1/012102

Cited by 7 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PIM is applied to produce complex features and intricate shapes. They are less expensive than other methods like 3D printing [4,5]. By using the PIM method, a variety of thermoplastics, including ABS, Nylon, PLA, Polycarbonate, PEEK, Polyethylene, Polypropylene, Polystyrene, and Polyvinyl Chloride, are frequently utilized [6].…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of artificial neural network model and analysis of variance for predicting defect formation in plastic injection moulding processes

Dejene,

Wolla

2023

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

View full text Add to dashboard Cite

This study investigates the impact of plastic injection moulding process parameters on overflow defect formation. Experiments were conducted using a Taguchi L27 orthogonal array design. Multilayer Perceptron (MLP) artificial neural networks is explored and compared with ANOVA predictions. To assess model performance, the Root Mean Squared Error (RMSE) and the coefficient of determination (R2) is applied. The study considered temperature, speed, pressure, and packing force when constructing the MLP model using the back-propagation algorithm in Python. Results show that among the configured MLP neural networks, the 3-layer MLP architecture with sigmoid activation functions in hidden layers and a linear function in the output layer exhibited the lowest prediction error and the highest coefficient of determination. Comparative analysis reveals that the MLP neural network outperforms the ANOVA model, indicating superior prediction accuracy. The predicted outcomes from the ANN align well with experimental values, demonstrating the effectiveness of the ANN model in forecasting defect formation under specific process conditions. This research sheds light on the significance of process parameters and showcases the potential of MLP neural networks as a valuable tool in predicting and mitigating overflow defects in plastic injection moulding.

show abstract

Section: Introductionmentioning

confidence: 99%

Comparative analysis of artificial neural network model and analysis of variance for predicting defect formation in plastic injection moulding processes

Dejene,

Wolla

2023

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

View full text Add to dashboard Cite

show abstract

“…In recent years, automatic WAAM technology has replaced the traditional manual welding method gradually because of its higher accuracy and better economic efficiency, which will be a development trend in hot forging die remanufacturing field [3][4][5]. Different from conventional powder-based 3D printing, the WAAM process needs to satisfy some welding requirements such as reducing repetitive filling or underfilling, air travel, and arc breaking [6,7].…”

Section: Introductionmentioning

confidence: 99%

Filling Path Planning and Polygon Operations for Wire Arc Additive Manufacturing Process

Zhang

Wang

Xiao³

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

To improve the service life of hot forging die, the additive manufacturing algorithm and additive manufacturing device for die remanufacturing are developed. Firstly, a compound filling algorithm in which the inner zone is filled by linear scanning and the outer contour is filled by offsetting is developed in order to solve the problems encountered in filling path planning for wire arc additive manufacturing (WAAM) like staircase effect at marginal division, degenerated edge at outer contour, programming difficulty, and so forth. Meanwhile, the attitude control algorithm of welding gun is proposed to control the angle between welding gun and welding path so as to improve the welding forming quality. Secondly, the high-temperature and low-temperature wear resistances of Fe-based and Ni-based alloy are tested. The results show that Ni-based alloy has higher high-temperature wear resistance. Finally, a disabled crankshaft hot forging die is selected for application test and the results show that, using the techniques discussed in this paper, welding materials can be saved by more than 50% and machining cost can be saved by more than 60%. In addition, the surface of automatic-repaired die is smooth without oxidation, collapse, and other defects after forging 3000 times, which is much better than that of manual-repaired die.

show abstract

Inspection of 3D Printing and Advanced Manufacturing Processes Using Hybrid 3D Metrological Technologies

Barrenetxea

Mínguez

Etxaniz

et al. 2020

Lecture Notes in Mechanical Engineering

View full text Add to dashboard Cite

Comparative Analysis of Additive Manufacturing over Conventional Manufacturing

Cited by 7 publications

References 3 publications

Comparative analysis of artificial neural network model and analysis of variance for predicting defect formation in plastic injection moulding processes

Comparative analysis of artificial neural network model and analysis of variance for predicting defect formation in plastic injection moulding processes

Filling Path Planning and Polygon Operations for Wire Arc Additive Manufacturing Process

Inspection of 3D Printing and Advanced Manufacturing Processes Using Hybrid 3D Metrological Technologies

Contact Info

Product

Resources

About