Particle Deposition of Diamond-Like-Carbon on Silicon Wafers using Inductively Coupled PECVD

Coating a surface with an appropriate particle layer changes the surface material properties and is an important tool for friction and wear reduction. Diamond-Like carbon (DLC) coatings are highly in use for various applications owing to their characteristic properties such as low friction and low wear resistance, and high hardness value. In the present work, DLC particle films on p-type Si substrates by inductively coupled plasma enhanced chemical vapour deposition(IC-PECVD) were deposited. Fourier transform infrared spectroscopy revealed that DLC films were composed of sp3 and sp2 C-H bonds. Raman Spectroscopy was used for investigations into sp3/sp2 ratio of the deposited carbon particles bonding. hardness and Young’s modulus were evaluated by indentation method using nano-hardness tester. The results showed that the deposited IC-PECVD DLC particle film had excellent chemical and mechanical properties. The developed films showed high value of hardness of 18 GPa, Young’s modulus 190 GPa and a minimum ID/IG ratio of 0.18

Machine Learning-Assisted Pattern Recognition Algorithms for Estimating Ultimate Tensile Strength in Fused Deposition Modeled Polylactic Acid Specimens

In this study, we investigate the application of supervised machine learning algorithms for estimating the Ultimate Tensile Strength (UTS) of Polylactic Acid (PLA) specimens fabricated using the Fused Deposition Modeling (FDM) process. A total of 31 PLA specimens were prepared, with Infill Percentage, Layer Height, Print Speed, and Extrusion Temperature serving as input parameters. The primary objective was to assess the accuracy and effectiveness of four distinct supervised classification algorithms, namely Logistic Classification, Gradient Boosting Classification, Decision Tree, and K-Nearest Neighbor, in predicting the UTS of the specimens. The results revealed that while the Decision Tree and K-Nearest Neighbor algorithms both achieved an F1 score of 0.71, the KNN algorithm exhibited a higher Area Under the Curve (AUC) score of 0.79, outperforming the other algorithms. This demonstrates the superior ability of the KNN algorithm in differentiating between the two classes of ultimate tensile strength within the dataset, rendering it the most favorable choice for classification in the context of this research. This study represents the first attempt to estimate the UTS of PLA specimens using machine learning-based classification algorithms, and the findings offer valuable insights into the potential of these techniques in improving the performance and accuracy of predictive models in the domain of additive manufacturing

Employing Explainable Artificial Intelligence (XAI) Methodologies to Analyze the Correlation between Input Variables and Tensile Strength in Additively Manufactured Samples

This research paper explores the impact of various input parameters, including Infill percentage, Layer Height, Extrusion Temperature, and Print Speed, on the resulting Tensile Strength in objects produced through additive manufacturing. The main objective of this study is to enhance our understanding of the correlation between the input parameters and Tensile Strength, as well as to identify the key factors influencing the performance of the additive manufacturing process. To achieve this objective, we introduced the utilization of Explainable Artificial Intelligence (XAI) techniques for the first time, which allowed us to analyze the data and gain valuable insights into the system's behavior. Specifically, we employed SHAP (SHapley Additive exPlanations), a widely adopted framework for interpreting machine learning model predictions, to provide explanations for the behavior of a machine learning model trained on the data. Our findings reveal that the Infill percentage and Extrusion Temperature have the most significant influence on Tensile Strength, while the impact of Layer Height and Print Speed is relatively minor. Furthermore, we discovered that the relationship between the input parameters and Tensile Strength is highly intricate and nonlinear, making it difficult to accurately describe using simple linear models

Quantum Machine Learning Approach for the Prediction of Surface Roughness in Additive Manufactured Specimens

Surface roughness is a crucial factor influencing the performance and functionality of additive manufactured components. Accurate prediction of surface roughness is vital for optimizing manufacturing processes and ensuring the quality of the final product. Quantum computing has recently gained attention as a potential solution for tackling complex problems and creating precise predictive models. In this research paper, we conduct an in-depth comparison of three quantum algorithms i.e. the Quantum Neural Network (QNN), Quantum Forest (Q-Forest), and Variational Quantum Classifier (VQC) adapted for regression for predicting surface roughness in additive manufactured specimens for the first time. We assess the algorithms performance using Mean Squared Error (MSE), Mean Absolute Error (MAE), and Explained Variance Score (EVS) as evaluation metrics. Our findings show that the Q-Forest algorithm surpasses the other algorithms, achieving an MSE of 56.905, MAE of 7.479, and an EVS of 0.2957. In contrast, the QNN algorithm displays a higher MSE of 60.840 and MAE of 7.671, coupled with a negative EVS of -0.444, indicating that it may not be appropriate for predicting surface roughness in this application. The VQC adapted for regression exhibits an MSE of 59.121, MAE of 7.597, and an EVS of -0.0106, suggesting its performance is also inferior to the Q-Forest algorithm

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

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. 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

Performance Prediction of Data-Driven Knowledge summarization of High Entropy Alloys (HEAs) literature implementing Natural Language Processing algorithms

The ability to interpret spoken language is connected to natural language processing. It involves teaching the AI how words relate to one another, how they are meant to be used, and in what settings. The goal of natural language processing (NLP) is to get a machine intelligence to process words the same way a human brain does. This enables machine intelligence to interpret, arrange, and comprehend textual data by processing the natural language. The technology can comprehend what is communicated, whether it be through speech or writing because AI pro-cesses language more quickly than humans can. In the present study, five NLP algorithms, namely, Geneism, Sumy, Luhn, Latent Semantic Analysis (LSA), and Kull-back-Liebler (KL) al-gorithm, are implemented for the first time for the knowledge summarization purpose of the High Entropy Alloys (HEAs). The performance prediction of these algorithms is made by using the BLEU score and ROUGE score. The results showed that the Luhn algorithm has the highest accuracy score for the knowledge summarization tasks compared to the other used algorithms

Influence of Copper Filled Acrylonitrile Butadiene Styrene Composite's on Mechanical Properties in Injection Molding Process

Acrylonitrile Butadiene Styrene (ABS) is one of the ideal material for direct digital, functional prototyping and conceptual modeling manufacturing. The manufacturers are different technologies and the methods to enhance the properties of the product. The focus is shifted from the pure material to the composites for the designing the product. The objective of the present study was to investigate the effect of different composition of copper (Cu) powder on the mechanical properties of the ABS-Cu composites. Different compositions of ABS-Cu composite with different weight percentage of specific material were prepared using Injection Molding machine. Specimens of ABS-Cu composite were made for the dry sliding wear test and hardness test as per ASTM standard. Result shows that, with increase in copper percentage in the composite, there is a decrease in surface hardness and coefficient of friction in ABS-Cu composite. These conclusions will be consider in engineering designs and will give improvement towards the mechanical properties of the material

Multi-characteristics optimization in EDM of NiTi alloy, NiCu alloy and BeCu alloy using Taguchi’s approach and utility concept

The foremost aim of Electrical Discharge Machining (EDM) users and manufactures is to obtain a better process stability, maximum productivity, precise and accurate machining of the component with minimum tool wear. For achieving better efficiency in EDM, selection and setting of input parameter is a crucial step. This study investigated the effect of input parameters such as workpiece electrical conductivity, gap current, gap voltage, pulse-on-time and pulse-off-time on the responses namely material removal rate (MRR) and tool wear rate (TWR). Experiments were designed and performed as per the Taguchi’s L18 (61 × 34) array. Taguchi’s approach and utility concept were employed for optimizing conflicting responses. Obtained results showed that the overall utility was significantly affected by gap current, gap voltage, pulse-on-time and pulse-off-time. Thus the obtained optimal values of MRR and TWR are 9.157 mm3/min and 0.128 mm3/min respectively. Keywords: Multi-characteristics, Optimization, Taguchi’s method, Utility concept, Material removal rate, Tool wear rat