Investigation of the Effect of Built Orientation on Mechanical Properties and Total Cost of FDM Parts

AbstractFused deposition modeling (FDM) is one of the rapid prototyping methods that produce prototypes from plastic materials such as acrylonitrile butadiene styrene (ABS) by laying tracks of semi-molten plastic filament onto a platform in a layer wise manner from bottom to top. In FDM, one of the critical factor is to select the build up orientation of the model since it affects the different areas of the model like main material, support material, built up time, total cost per part and most important the mechanical properties of the part. In view of this, objective of the present study was to investigate the effect of the built-up orientation on the mechanical properties and total cost of the FDM parts. Experiments were carried out on STRATASYS FDM type rapid prototyping machine coupled with CATALYST software and ABS as main material. Tensile and Flexural specimens were prepared as per the ASTM standard with different built-up orientation and in three geometrical axes. It can be concluded from the experimental analysis that built orientation has significant affect on the tensile, flexural and total cost of the FDM parts. These conclusions will help the design engineers to decide on proper build orientation, so that FDM parts can be fabricated with good mechanical properties at minimum manufacturing cost

Computer Vision Algorithm for the detection of fracture cracks in Oil Hardening Non-Shrinking (OHNS) die steel after machining process

A variant of neural network for processing with images is a convolutional neural network (CNN). This type of neural network receives input from an image and extracts features from the image while also providing learnable parameters to effectively do the classification, detection, and many other tasks. In the present work, U-Net convolutional neural network is implemented on Jupyter platform by using Python programming for fracture surface image segmentation in Oil Hardening Non-Shrinking (OHNS) die steel after the machining process. The results showed that the fracture cracks can be validated by testing with higher accuracy

Computer Vision Algorithm for the detection of fracture cracks in Oil Hardening Non-Shrinking (OHNS) die steel after machining process

A variant of neural network for processing with images is a convolutional neural network (CNN). This type of neural network receives input from an image and extracts features from the image while also providing learnable parameters to effectively do the classification, detection, and many other tasks. In the present work, U-Net convolutional neural network is implemented on Jupyter platform by using Python programming for fracture surface image segmentation in Oil Hardening Non-Shrinking (OHNS) die steel after the machining process. The results showed that the fracture cracks can be validated by testing with higher accuracy. The plot of accuracy vs. number of epochs showed the obtained accuracy score 0f 1.0 which means that 100 % of points were correctly labeled by our implemented algorithm

Mechanical Properties of 3D-Printed Components Using Fused Deposition Modeling: Optimization Using the Desirability Approach and Machine Learning Regressor

The fused deposition modelling (FDM) technique involves the deposition of a fused layer of material according to the geometry designed in the software. Several parameters affect the quality of parts produced by FDM. This paper investigates the effect of FDM printing process parameters on tensile strength, impact strength, and flexural strength. The effects of process parameters such as printing speed, layer thickness, extrusion temperature, and infill percentage are studied. Polyactic acid (PLA) was used as a filament material for printing test specimens. The experimental layout is designed according to response surface methodology (RSM) and responses are collected. Specimens are prepared for testing of these parameters as per ASTM standards. A mathematical model for each of the responses is developed based on the nonlinear regression method. The desirability approach, nonlinear regression, as well as experimental values are in close agreement with each other. The desirability approach predicted the tensile strength, impact strength, and flexural strength with a less percentage error of 3.109, 6.532, and 3.712, respectively. The nonlinear regression approach predicted the tensile strength, impact strength, and flexural strength with a less percentage error of 2.977, 6.532, and 3.474, respectively. The desirability concept and nonlinear regression approach resulted in the best mechanical property of the FDM-printed part

Machine Learning Based Predictive Modeling of Electrical Discharge Machining of Cryo-Treated NiTi, NiCu and BeCu Alloys

The advancement in technology has attracted researchers to electric discharge machining (EDM) for providing a practical solution for overcoming the limitations of conventional machining. The current study focused on predicting the Material Removal Rate (MRR) using machine learning (ML) approaches. The process parameters considered are namely, workpiece electrical conductivity, gap current, gap voltage, pulse on time and pulse off time. Cryo-treated workpiece viz, Nickel-Titanium (NiTi) alloys, Nickel Copper (NiCu) alloys, and Beryllium copper (BCu) alloys and cryo-treated pure copper as tool electrode was considered. In the present research work, four supervised machine learning regression and three supervised machine learning classification-based algorithms are used for predicting the MRR. Machine learning result showed that gap current, gap voltage and pulse on time are most significant parameters that effected MRR. It is observed from the results that the Gradient boosting regression-based algorithm resulted in the highest coefficient of determination value for predicting MRR while Random Forest classification based resulted in the highest F1-Score for obtaining MRR

Machine Learning Based Predictive Modeling of Electrical Discharge Machining of Cryo-Treated NiTi, NiCu and BeCu Alloys

The advancement in technology has attracted researchers to electric discharge machining (EDM) for providing a practical solution for overcoming the limitations of conventional machining. The current study focused on predicting the Material Removal Rate (MRR) using machine learning (ML) approaches. The process parameters considered are namely, workpiece electrical conductivity, gap current, gap voltage, pulse on time and pulse off time. Cryo-treated workpiece viz, Nickel-Titanium (NiTi) alloys, Nickel Copper (NiCu) alloys, and Beryllium copper (BCu) alloys and cryo-treated pure copper as tool electrode was considered. In the present research work, four supervised machine learning regression and three supervised machine learning classification-based algorithms are used for predicting the MRR. Machine learning result showed that gap current, gap voltage and pulse on time are most significant parameters that effected MRR. It is observed from the results that the Gradient boosting regression-based algorithm resulted in the highest coefficient of determination value for predicting MRR while Random Forest classification based resulted in the highest F1-Score for obtaining MRR