[ { "subject": "Material Science - Additive Manufacturing", "paper_id": "1", "paper_title": "Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach", "question_id": "Q1", "question": "What type of additive manufacturing process is studied?", "choices": { "A": "LPBF", "B": "Inkjet printing", "C": "Aerojet printing", "D": "Direct ink writing", "E": "All of above", "F": "None of above" }, "answer": "C", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "AM process", "term_explanation": "Can you describe the particular kind of 3D printing or additive manufacturing approach that the work is centered on, since there are many different techniques available and each one uses a different way of building up materials layer by layer?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "1", "paper_title": "Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach", "question_id": "Q2", "question": "What type of material is used for printing?", "choices": { "A": "Ti64", "B": "Water", "C": "Silver", "D": "Glycerol", "E": "All of above", "F": "None of above" }, "answer": "C", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "Material type", "term_explanation": "What kind of material is being used in the 3D printing for the study? In other words, is the printing carried out with metals, plastics, ceramics, composites, or some other type of material, since different studies often focus on very different classes of materials?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "1", "paper_title": "Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach", "question_id": "Q3", "question": "What kind of shape or product is printed?", "choices": { "A": "Thin wall", "B": "Droplet", "C": "Single layer", "D": "Line", "E": "All of above", "F": "None of above" }, "answer": "D", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "Part and design", "term_explanation": "What kind of object or final shape is being created with the 3D printing process—for example, is it something simple like a droplet or test sample, or a more complex product meant for a specific use?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "1", "paper_title": "Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach", "question_id": "Q4", "question": "What is the primary defect being studied?", "choices": { "A": "Porosity", "B": "Crack", "C": "Satelitte", "D": "Overspray", "E": "All of above", "F": "None of above" }, "answer": "D", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "Defect", "term_explanation": "What is the main problem or imperfection in the 3D-printed part that the study is trying to detect or understand—for example, are they looking for tiny holes, cracks, weak spots, or other kinds of flaws that can appear during printing?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "1", "paper_title": "Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach", "question_id": "Q5", "question": "What sensors are used to measure the process?", "choices": { "A": "CCD camera", "B": "Strobing camera", "C": "Thermocouple", "D": "Acoustic", "E": "All of above", "F": "None of above" }, "answer": "A", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Sensor type", "term_explanation": "What kinds of tools or sensors are being used to watch, measure, or check the 3D printing process—for instance, are the paper looking at the process with cameras, tracking heat, listening for sounds, or scanning the part after it’s made?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "1", "paper_title": "Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach", "question_id": "Q6", "question": "What is the sampling rate (Hz)?", "choices": { "A": "0-20 Hz", "B": "20-40 Hz", "C": "40-60 Hz", "D": "Above 60 Hz", "E": "All of above", "F": "None of above" }, "answer": "F", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Sampling rate", "term_explanation": "How often are the measurements or data points being collected during the 3D printing process—for example, are they being taken many times per second, or less frequently?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "1", "paper_title": "Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach", "question_id": "Q7", "question": "If relevant, what is the spatial resolution (µm)?", "choices": { "A": "<50 µm", "B": "50 -100 µm", "C": "100 -150 µm", "D": "Above 150 µm", "E": "All of above", "F": "None of above" }, "answer": "F", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Spatial resolution", "term_explanation": "How detailed or sharp is the image—for example, how small of a feature can actually be seen in the measurement?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "1", "paper_title": "Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach", "question_id": "Q8", "question": "What is the machine learning objective in this study?", "choices": { "A": "Regression", "B": "Clustering", "C": "Dimension reduction", "D": "Classification", "E": "All of above", "F": "None of above" }, "answer": "D", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Machine Learning task", "term_explanation": "What kind of problem is the paper solving with the data—for example, is it trying to sort things into categories, make predictions about numbers, or group similar items together?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "1", "paper_title": "Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach", "question_id": "Q9", "question": "What machine learning algorithm is used?", "choices": { "A": "PCA", "B": "SVM", "C": "CNN", "D": "RF", "E": "All of above", "F": "None of above" }, "answer": "C", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Machine Learning algorithm", "term_explanation": "A machine learning algorithm (such as linear regression, support vector machine, DNN etc. ) is a mathematical framework or procedure that processes input data to create a model capable of solving a specific ML task." } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "1", "paper_title": "Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach", "question_id": "Q10", "question": "How are the data split during machine learning?", "choices": { "A": "T-T split; 0.8, 0.2", "B": "T-D-T split; 0.8, 0.1, 0.1", "C": "K-fold; 5 fold", "D": "K-fold; 10 fold", "E": "All of above", "F": "None of above" }, "answer": "A", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Splitting strategy (tdt split or k-fold)", "term_explanation": "How is the data being divided up when training the model—for example, which part is used for teaching, which part is used for testing how well it learned?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "1", "paper_title": "Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach", "question_id": "Q11", "question": "How many replications are conducted during machine learning, if any?", "choices": { "A": "1", "B": "5", "C": "50", "D": "100", "E": "All of above", "F": "None of above" }, "answer": "A", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Number of Replications", "term_explanation": "If the paper did so, how many times is the experiment or test repeated to make sure the results are reliable?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "1", "paper_title": "Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach", "question_id": "Q12", "question": "How many epochs are used during machine learning, if any?", "choices": { "A": "5", "B": "256", "C": "500", "D": "1000", "E": "All of above", "F": "None of above" }, "answer": "C", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Number of Epochs", "term_explanation": "In training the model, how many times does it go through the whole set of data?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "1", "paper_title": "Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach", "question_id": "Q13", "question": "What metrics are used to evaluate the machine learning models?", "choices": { "A": "Accuracy", "B": "Precision", "C": "Recall", "D": "F1 score", "E": "All of above", "F": "None of above" }, "answer": "A", "metadata": { "Task-oriented Category": "Conclusions & results", "question_key_term": "Performance metrics", "term_explanation": "How do the researchers check whether the model is doing a good job—what measures or scores do they use to judge its performance?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "1", "paper_title": "Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach", "question_id": "Q14", "question": "What are the values for these metrics?", "choices": { "A": "Accuracy > 90%", "B": ">97.8 % accuracy, >98.2 % precision, >98.2 % recall and >98.2 % F1 score", "C": "p < 0.003 for any classical method compared to our method", "D": "89.5±2.5 % accuracy", "E": "All of above", "F": "None of above" }, "answer": "A", "metadata": { "Task-oriented Category": "Conclusions & results", "question_key_term": "Performance values", "term_explanation": "What were the actual results or scores from those metrics?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "2", "paper_title": "Geometrical defect detection for additive manufacturing with machine learning models", "question_id": "Q1", "question": "What type of additive manufacturing process is studied?", "choices": { "A": "LPBF", "B": "FFF", "C": "Aerojet printing", "D": "Direct ink writing", "E": "All of above", "F": "None of above" }, "answer": "B", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "AM process", "term_explanation": "Can you describe the particular kind of 3D printing or additive manufacturing approach that the work is centered on, since there are many different techniques available and each one uses a different way of building up materials layer by layer?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "2", "paper_title": "Geometrical defect detection for additive manufacturing with machine learning models", "question_id": "Q2", "question": "What type of material is used for printing?", "choices": { "A": "Ti64", "B": "ABS", "C": "Silver", "D": "Glycerol", "E": "All of above", "F": "None of above" }, "answer": "B", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "Material type", "term_explanation": "What kind of material is being used in the 3D printing for the study? In other words, is the printing carried out with metals, plastics, ceramics, composites, or some other type of material, since different studies often focus on very different classes of materials?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "2", "paper_title": "Geometrical defect detection for additive manufacturing with machine learning models", "question_id": "Q3", "question": "What kind of shape or product is printed?", "choices": { "A": "Thin wall", "B": "Droplet", "C": "Single layer", "D": "Full part", "E": "All of above", "F": "None of above" }, "answer": "D", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "Part and design", "term_explanation": "What kind of object or final shape is being created with the 3D printing process—for example, is it something simple like a droplet or test sample, or a more complex product meant for a specific use?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "2", "paper_title": "Geometrical defect detection for additive manufacturing with machine learning models", "question_id": "Q4", "question": "What is the primary defect being studied?", "choices": { "A": "Porosity", "B": "Crack", "C": "Satelitte", "D": "Overspray", "E": "All of above", "F": "None of above" }, "answer": "F", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "Defect", "term_explanation": "What is the main problem or imperfection in the 3D-printed part that the study is trying to detect or understand—for example, are they looking for tiny holes, cracks, weak spots, or other kinds of flaws that can appear during printing?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "2", "paper_title": "Geometrical defect detection for additive manufacturing with machine learning models", "question_id": "Q5", "question": "What sensors are used to measure the process?", "choices": { "A": "Thermal camera", "B": "Strobing camera", "C": "Thermocouple", "D": "3D scanner", "E": "All of above", "F": "None of above" }, "answer": "D", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Sensor type", "term_explanation": "What kinds of tools or sensors are being used to watch, measure, or check the 3D printing process—for instance, are the paper looking at the process with cameras, tracking heat, listening for sounds, or scanning the part after it’s made?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "2", "paper_title": "Geometrical defect detection for additive manufacturing with machine learning models", "question_id": "Q6", "question": "What is the sampling rate (Hz)?", "choices": { "A": "0-20 Hz", "B": "20-40 Hz", "C": "40-60 Hz", "D": "Above 60 Hz", "E": "All of above", "F": "None of above" }, "answer": "F", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Sampling rate", "term_explanation": "How often are the measurements or data points being collected during the 3D printing process—for example, are they being taken many times per second, or less frequently?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "2", "paper_title": "Geometrical defect detection for additive manufacturing with machine learning models", "question_id": "Q7", "question": "If relevant, what is the spatial resolution (µm)?", "choices": { "A": "<50 µm", "B": "50 -100 µm", "C": "100 -150 µm", "D": "Above 150 µm", "E": "All of above", "F": "None of above" }, "answer": "F", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Spatial resolution", "term_explanation": "How detailed or sharp is the image—for example, how small of a feature can actually be seen in the measurement?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "2", "paper_title": "Geometrical defect detection for additive manufacturing with machine learning models", "question_id": "Q8", "question": "What is the machine learning objective in this study?", "choices": { "A": "Regression", "B": "Clustering", "C": "Dimension reduction", "D": "Classification", "E": "All of above", "F": "None of above" }, "answer": "D", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Machine Learning task", "term_explanation": "What kind of problem is the paper solving with the data—for example, is it trying to sort things into categories, make predictions about numbers, or group similar items together?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "2", "paper_title": "Geometrical defect detection for additive manufacturing with machine learning models", "question_id": "Q9", "question": "What machine learning algorithm is used?", "choices": { "A": "Bagging", "B": "Boosting", "C": "RF", "D": "SVM", "E": "All of above", "F": "None of above" }, "answer": "E", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Machine Learning algorithm", "term_explanation": "A machine learning algorithm (such as linear regression, support vector machine, DNN etc. ) is a mathematical framework or procedure that processes input data to create a model capable of solving a specific ML task." } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "2", "paper_title": "Geometrical defect detection for additive manufacturing with machine learning models", "question_id": "Q10", "question": "How are the data split during machine learning?", "choices": { "A": "T-T split; 0.7, 0.3", "B": "T-D-T split; 0.8, 0.1, 0.1", "C": "K-fold; 5 fold", "D": "K-fold; 10 fold", "E": "All of above", "F": "None of above" }, "answer": "F", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Splitting strategy (tdt split or k-fold)", "term_explanation": "How is the data being divided up when training the model—for example, which part is used for teaching, which part is used for testing how well it learned?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "2", "paper_title": "Geometrical defect detection for additive manufacturing with machine learning models", "question_id": "Q11", "question": "How many replications are conducted during machine learning, if any?", "choices": { "A": "5", "B": "256", "C": "1000", "D": "35", "E": "All of above", "F": "None of above" }, "answer": "F", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Number of Replications", "term_explanation": "If the paper did so, how many times is the experiment or test repeated to make sure the results are reliable?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "2", "paper_title": "Geometrical defect detection for additive manufacturing with machine learning models", "question_id": "Q12", "question": "How many epochs are used during machine learning, if any?", "choices": { "A": "50", "B": "300", "C": "1000", "D": "3500", "E": "All of above", "F": "None of above" }, "answer": "F", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Number of Epochs", "term_explanation": "In training the model, how many times does it go through the whole set of data?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "2", "paper_title": "Geometrical defect detection for additive manufacturing with machine learning models", "question_id": "Q13", "question": "What metrics are used to evaluate the machine learning models?", "choices": { "A": "Accuracy", "B": "F-measure", "C": "G-mean", "D": "F-measure and G-mean", "E": "All of above", "F": "None of above" }, "answer": "E", "metadata": { "Task-oriented Category": "Conclusions & results", "question_key_term": "Performance metrics", "term_explanation": "How do the researchers check whether the model is doing a good job—what measures or scores do they use to judge its performance?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "2", "paper_title": "Geometrical defect detection for additive manufacturing with machine learning models", "question_id": "Q14", "question": "What are the values for these metrics?", "choices": { "A": "Accuracy of 98 %", "B": "98 % accuracy, >35 % F-measure, >46 % G-mean", "C": "p < 0.003", "D": "A strong correlation", "E": "All of above", "F": "None of above" }, "answer": "B", "metadata": { "Task-oriented Category": "Conclusions & results", "question_key_term": "Performance values", "term_explanation": "What were the actual results or scores from those metrics?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "3", "paper_title": "Layer-Wise Modeling and Anomaly Detection for LaserBased Additive Manufacturing", "question_id": "Q1", "question": "What type of additive manufacturing process is studied?", "choices": { "A": "LPBF", "B": "Inkjet printing", "C": "Aerojet printing", "D": "Direct ink writing", "E": "All of above", "F": "None of above" }, "answer": "A", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "AM process", "term_explanation": "Can you describe the particular kind of 3D printing or additive manufacturing approach that the work is centered on, since there are many different techniques available and each one uses a different way of building up materials layer by layer?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "3", "paper_title": "Layer-Wise Modeling and Anomaly Detection for LaserBased Additive Manufacturing", "question_id": "Q2", "question": "What type of material is used for printing?", "choices": { "A": "Ti64", "B": "Water", "C": "Silver", "D": "Glycerol", "E": "All of above", "F": "None of above" }, "answer": "A", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "Material type", "term_explanation": "What kind of material is being used in the 3D printing for the study? In other words, is the printing carried out with metals, plastics, ceramics, composites, or some other type of material, since different studies often focus on very different classes of materials?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "3", "paper_title": "Layer-Wise Modeling and Anomaly Detection for LaserBased Additive Manufacturing", "question_id": "Q3", "question": "What kind of shape or product is printed?", "choices": { "A": "Thin wall", "B": "Droplet", "C": "Single layer", "D": "Full part", "E": "All of above", "F": "None of above" }, "answer": "A", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "Part and design", "term_explanation": "What kind of object or final shape is being created with the 3D printing process—for example, is it something simple like a droplet or test sample, or a more complex product meant for a specific use?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "3", "paper_title": "Layer-Wise Modeling and Anomaly Detection for LaserBased Additive Manufacturing", "question_id": "Q4", "question": "What is the primary defect being studied?", "choices": { "A": "Porosity", "B": "Crack", "C": "Satelitte", "D": "Overspray", "E": "All of above", "F": "None of above" }, "answer": "A", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "Defect", "term_explanation": "What is the main problem or imperfection in the 3D-printed part that the study is trying to detect or understand—for example, are they looking for tiny holes, cracks, weak spots, or other kinds of flaws that can appear during printing?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "3", "paper_title": "Layer-Wise Modeling and Anomaly Detection for LaserBased Additive Manufacturing", "question_id": "Q5", "question": "What sensors are used to measure the process?", "choices": { "A": "Thermal camera", "B": "Strobing camera", "C": "Thermocouple", "D": "Acoustic", "E": "All of above", "F": "None of above" }, "answer": "A", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Sensor type", "term_explanation": "What kinds of tools or sensors are being used to watch, measure, or check the 3D printing process—for instance, are the paper looking at the process with cameras, tracking heat, listening for sounds, or scanning the part after it’s made?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "3", "paper_title": "Layer-Wise Modeling and Anomaly Detection for LaserBased Additive Manufacturing", "question_id": "Q6", "question": "What is the sampling rate (Hz)?", "choices": { "A": "0-20 Hz", "B": "20-40 Hz", "C": "40-60 Hz", "D": "Above 60 Hz", "E": "All of above", "F": "None of above" }, "answer": "A", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Sampling rate", "term_explanation": "How often are the measurements or data points being collected during the 3D printing process—for example, are they being taken many times per second, or less frequently?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "3", "paper_title": "Layer-Wise Modeling and Anomaly Detection for LaserBased Additive Manufacturing", "question_id": "Q7", "question": "If relevant, what is the spatial resolution (µm)?", "choices": { "A": "<50 µm", "B": "50 -100 µm", "C": "100 -150 µm", "D": "Above 150 µm", "E": "All of above", "F": "None of above" }, "answer": "A", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Spatial resolution", "term_explanation": "How detailed or sharp is the image—for example, how small of a feature can actually be seen in the measurement?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "3", "paper_title": "Layer-Wise Modeling and Anomaly Detection for LaserBased Additive Manufacturing", "question_id": "Q8", "question": "What is the machine learning objective in this study?", "choices": { "A": "Regression", "B": "Clustering", "C": "Dimension reduction", "D": "Classification", "E": "All of above", "F": "None of above" }, "answer": "D", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Machine Learning task", "term_explanation": "What kind of problem is the paper solving with the data—for example, is it trying to sort things into categories, make predictions about numbers, or group similar items together?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "3", "paper_title": "Layer-Wise Modeling and Anomaly Detection for LaserBased Additive Manufacturing", "question_id": "Q9", "question": "What machine learning algorithm is used?", "choices": { "A": "Bagging", "B": "Boosting", "C": "RF", "D": "SVM", "E": "All of above", "F": "None of above" }, "answer": "D", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Machine Learning algorithm", "term_explanation": "A machine learning algorithm (such as linear regression, support vector machine, DNN etc. ) is a mathematical framework or procedure that processes input data to create a model capable of solving a specific ML task." } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "3", "paper_title": "Layer-Wise Modeling and Anomaly Detection for LaserBased Additive Manufacturing", "question_id": "Q10", "question": "How are the data split during machine learning?", "choices": { "A": "T-T split; 5/6, 1/6", "B": "T-D-T split; 0.8, 0.1, 0.1", "C": "K-fold; 5 fold", "D": "K-fold; 10 fold", "E": "All of above", "F": "None of above" }, "answer": "A", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Splitting strategy (tdt split or k-fold)", "term_explanation": "How is the data being divided up when training the model—for example, which part is used for teaching, which part is used for testing how well it learned?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "3", "paper_title": "Layer-Wise Modeling and Anomaly Detection for LaserBased Additive Manufacturing", "question_id": "Q11", "question": "How many replications are conducted during machine learning, if any?", "choices": { "A": "5", "B": "256", "C": "500", "D": "35", "E": "All of above", "F": "None of above" }, "answer": "C", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Number of Replications", "term_explanation": "If the paper did so, how many times is the experiment or test repeated to make sure the results are reliable?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "3", "paper_title": "Layer-Wise Modeling and Anomaly Detection for LaserBased Additive Manufacturing", "question_id": "Q12", "question": "How many epochs are used during machine learning, if any?", "choices": { "A": "50", "B": "300", "C": "1000", "D": "3500", "E": "All of above", "F": "None of above" }, "answer": "F", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Number of Epochs", "term_explanation": "In training the model, how many times does it go through the whole set of data?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "3", "paper_title": "Layer-Wise Modeling and Anomaly Detection for LaserBased Additive Manufacturing", "question_id": "Q13", "question": "What metrics are used to evaluate the machine learning models?", "choices": { "A": "Precision and Recall", "B": "Precision", "C": "Recall", "D": "F1 score", "E": "All of above", "F": "None of above" }, "answer": "E", "metadata": { "Task-oriented Category": "Conclusions & results", "question_key_term": "Performance metrics", "term_explanation": "How do the researchers check whether the model is doing a good job—what measures or scores do they use to judge its performance?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "3", "paper_title": "Layer-Wise Modeling and Anomaly Detection for LaserBased Additive Manufacturing", "question_id": "Q14", "question": "What are the values for these metrics?", "choices": { "A": "Accuracy > 90%", "B": ">95 % precision, >91 % recall and >93 % F1 score", "C": "p < 0.003 for any classical method compared to our method", "D": "89.5±2.5 % accuracy", "E": "All of above", "F": "None of above" }, "answer": "B", "metadata": { "Task-oriented Category": "Conclusions & results", "question_key_term": "Performance values", "term_explanation": "What were the actual results or scores from those metrics?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "4", "paper_title": "Online droplet anomaly detection from streaming videos in inkjet printing", "question_id": "Q1", "question": "What type of additive manufacturing process is studied?", "choices": { "A": "LPBF", "B": "Inkjet printing", "C": "Aerojet printing", "D": "Direct ink writing", "E": "All of above", "F": "None of above" }, "answer": "B", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "AM process", "term_explanation": "Can you describe the particular kind of 3D printing or additive manufacturing approach that the work is centered on, since there are many different techniques available and each one uses a different way of building up materials layer by layer?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "4", "paper_title": "Online droplet anomaly detection from streaming videos in inkjet printing", "question_id": "Q2", "question": "What type of material is used for printing?", "choices": { "A": "Ti64", "B": "Water", "C": "Silver", "D": "Glycerol", "E": "All of above", "F": "None of above" }, "answer": "B", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "Material type", "term_explanation": "What kind of material is being used in the 3D printing for the study? In other words, is the printing carried out with metals, plastics, ceramics, composites, or some other type of material, since different studies often focus on very different classes of materials?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "4", "paper_title": "Online droplet anomaly detection from streaming videos in inkjet printing", "question_id": "Q3", "question": "What kind of shape or product is printed?", "choices": { "A": "Thin wall", "B": "Droplet", "C": "Single layer", "D": "Full part", "E": "All of above", "F": "None of above" }, "answer": "B", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "Part and design", "term_explanation": "What kind of object or final shape is being created with the 3D printing process—for example, is it something simple like a droplet or test sample, or a more complex product meant for a specific use?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "4", "paper_title": "Online droplet anomaly detection from streaming videos in inkjet printing", "question_id": "Q4", "question": "What is the primary defect being studied?", "choices": { "A": "Porosity", "B": "Crack", "C": "Satelitte", "D": "Overspray", "E": "All of above", "F": "None of above" }, "answer": "C", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "Defect", "term_explanation": "What is the main problem or imperfection in the 3D-printed part that the study is trying to detect or understand—for example, are they looking for tiny holes, cracks, weak spots, or other kinds of flaws that can appear during printing?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "4", "paper_title": "Online droplet anomaly detection from streaming videos in inkjet printing", "question_id": "Q5", "question": "What sensors are used to measure the process?", "choices": { "A": "Thermal camera", "B": "Strobing camera", "C": "Thermocouple", "D": "Acoustic", "E": "All of above", "F": "None of above" }, "answer": "B", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Sensor type", "term_explanation": "What kinds of tools or sensors are being used to watch, measure, or check the 3D printing process—for instance, are the paper looking at the process with cameras, tracking heat, listening for sounds, or scanning the part after it’s made?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "4", "paper_title": "Online droplet anomaly detection from streaming videos in inkjet printing", "question_id": "Q6", "question": "What is the sampling rate (Hz)?", "choices": { "A": "0-20 Hz", "B": "20-40 Hz", "C": "40-60 Hz", "D": "Above 60 Hz", "E": "All of above", "F": "None of above" }, "answer": "B", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Sampling rate", "term_explanation": "How often are the measurements or data points being collected during the 3D printing process—for example, are they being taken many times per second, or less frequently?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "4", "paper_title": "Online droplet anomaly detection from streaming videos in inkjet printing", "question_id": "Q7", "question": "If relevant, what is the spatial resolution (µm)?", "choices": { "A": "<50 µm", "B": "50 -100 µm", "C": "100 -150 µm", "D": "Above 150 µm", "E": "All of above", "F": "None of above" }, "answer": "F", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Spatial resolution", "term_explanation": "How detailed or sharp is the image—for example, how small of a feature can actually be seen in the measurement?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "4", "paper_title": "Online droplet anomaly detection from streaming videos in inkjet printing", "question_id": "Q8", "question": "What is the machine learning objective in this study?", "choices": { "A": "Regression", "B": "Clustering", "C": "Dimension reduction", "D": "Classification", "E": "All of above", "F": "None of above" }, "answer": "D", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Machine Learning task", "term_explanation": "What kind of problem is the paper solving with the data—for example, is it trying to sort things into categories, make predictions about numbers, or group similar items together?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "4", "paper_title": "Online droplet anomaly detection from streaming videos in inkjet printing", "question_id": "Q9", "question": "What machine learning algorithm is used?", "choices": { "A": "SVM", "B": "CNN", "C": "LR", "D": "Bayesian online change detection", "E": "All of above", "F": "None of above" }, "answer": "D", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Machine Learning algorithm", "term_explanation": "A machine learning algorithm (such as linear regression, support vector machine, DNN etc. ) is a mathematical framework or procedure that processes input data to create a model capable of solving a specific ML task." } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "4", "paper_title": "Online droplet anomaly detection from streaming videos in inkjet printing", "question_id": "Q10", "question": "How are the data split during machine learning?", "choices": { "A": "T-T split; 0.7, 0.3", "B": "T-D-T split; 0.8, 0.1, 0.1", "C": "K-fold; 5 fold", "D": "K-fold; 10 fold", "E": "All of above", "F": "None of above" }, "answer": "F", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Splitting strategy (tdt split or k-fold)", "term_explanation": "How is the data being divided up when training the model—for example, which part is used for teaching, which part is used for testing how well it learned?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "4", "paper_title": "Online droplet anomaly detection from streaming videos in inkjet printing", "question_id": "Q11", "question": "How many replications are conducted during machine learning, if any?", "choices": { "A": "5", "B": "256", "C": "1000", "D": "35", "E": "All of above", "F": "None of above" }, "answer": "F", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Number of Replications", "term_explanation": "If the paper did so, how many times is the experiment or test repeated to make sure the results are reliable?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "4", "paper_title": "Online droplet anomaly detection from streaming videos in inkjet printing", "question_id": "Q12", "question": "How many epochs are used during machine learning, if any?", "choices": { "A": "50", "B": "170", "C": "600", "D": "1700", "E": "All of above", "F": "None of above" }, "answer": "F", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Number of Epochs", "term_explanation": "In training the model, how many times does it go through the whole set of data?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "4", "paper_title": "Online droplet anomaly detection from streaming videos in inkjet printing", "question_id": "Q13", "question": "What metrics are used to evaluate the machine learning models?", "choices": { "A": "Precision and Recall", "B": "Precision", "C": "Recall", "D": "F1 score", "E": "All of above", "F": "None of above" }, "answer": "A", "metadata": { "Task-oriented Category": "Conclusions & results", "question_key_term": "Performance metrics", "term_explanation": "How do the researchers check whether the model is doing a good job—what measures or scores do they use to judge its performance?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "4", "paper_title": "Online droplet anomaly detection from streaming videos in inkjet printing", "question_id": "Q14", "question": "What are the values for these metrics?", "choices": { "A": "Accuracy > 90%", "B": ">80 % precision, >70 % recall and >75 % F1 score", "C": "p < 0.003 for any classical method compared to our method", "D": "89.5±2.5 % accuracy", "E": "All of above", "F": "None of above" }, "answer": "B", "metadata": { "Task-oriented Category": "Conclusions & results", "question_key_term": "Performance values", "term_explanation": "What were the actual results or scores from those metrics?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "5", "paper_title": "Toward the digital twin of additive manufacturing- Integrating thermal simulations, sensing, and analytics to detect process faults", "question_id": "Q1", "question": "What type of additive manufacturing process is studied?", "choices": { "A": "LPBF", "B": "DED", "C": "Aerojet printing", "D": "LPBF and DED", "E": "All of above", "F": "None of above" }, "answer": "D", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "AM process", "term_explanation": "Can you describe the particular kind of 3D printing or additive manufacturing approach that the work is centered on, since there are many different techniques available and each one uses a different way of building up materials layer by layer?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "5", "paper_title": "Toward the digital twin of additive manufacturing- Integrating thermal simulations, sensing, and analytics to detect process faults", "question_id": "Q2", "question": "What type of material is used for printing?", "choices": { "A": "Ti64", "B": "Inconel 625", "C": "Silver", "D": "Ti64 and Inconel 625", "E": "All of above", "F": "None of above" }, "answer": "D", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "Material type", "term_explanation": "What kind of material is being used in the 3D printing for the study? In other words, is the printing carried out with metals, plastics, ceramics, composites, or some other type of material, since different studies often focus on very different classes of materials?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "5", "paper_title": "Toward the digital twin of additive manufacturing- Integrating thermal simulations, sensing, and analytics to detect process faults", "question_id": "Q3", "question": "What kind of shape or product is printed?", "choices": { "A": "Thin wall", "B": "Overhang part", "C": "Single layer", "D": "Overhang part and thin wall", "E": "All of above", "F": "None of above" }, "answer": "D", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "Part and design", "term_explanation": "What kind of object or final shape is being created with the 3D printing process—for example, is it something simple like a droplet or test sample, or a more complex product meant for a specific use?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "5", "paper_title": "Toward the digital twin of additive manufacturing- Integrating thermal simulations, sensing, and analytics to detect process faults", "question_id": "Q4", "question": "What is the primary defect being studied?", "choices": { "A": "Porosity", "B": "Crack", "C": "Satelitte", "D": "Overspray", "E": "All of above", "F": "None of above" }, "answer": "A", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "Defect", "term_explanation": "What is the main problem or imperfection in the 3D-printed part that the study is trying to detect or understand—for example, are they looking for tiny holes, cracks, weak spots, or other kinds of flaws that can appear during printing?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "5", "paper_title": "Toward the digital twin of additive manufacturing- Integrating thermal simulations, sensing, and analytics to detect process faults", "question_id": "Q5", "question": "What sensors are used to measure the process?", "choices": { "A": "Thermal camera", "B": "Photodetector", "C": "Thermal camera and photodetector", "D": "Acoustic", "E": "All of above", "F": "None of above" }, "answer": "C", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Sensor type", "term_explanation": "What kinds of tools or sensors are being used to watch, measure, or check the 3D printing process—for instance, are the paper looking at the process with cameras, tracking heat, listening for sounds, or scanning the part after it’s made?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "5", "paper_title": "Toward the digital twin of additive manufacturing- Integrating thermal simulations, sensing, and analytics to detect process faults", "question_id": "Q6", "question": "What is the sampling rate (Hz)?", "choices": { "A": "0-20 Hz", "B": "20-40 Hz", "C": "40-60 Hz", "D": "Above 60 Hz", "E": "All of above", "F": "None of above" }, "answer": "D", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Sampling rate", "term_explanation": "How often are the measurements or data points being collected during the 3D printing process—for example, are they being taken many times per second, or less frequently?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "5", "paper_title": "Toward the digital twin of additive manufacturing- Integrating thermal simulations, sensing, and analytics to detect process faults", "question_id": "Q7", "question": "If relevant, what is the spatial resolution (µm)?", "choices": { "A": "<50 µm", "B": "50 -100 µm", "C": "100 -150 µm", "D": "Above 150 µm", "E": "All of above", "F": "None of above" }, "answer": "F", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Spatial resolution", "term_explanation": "How detailed or sharp is the image—for example, how small of a feature can actually be seen in the measurement?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "5", "paper_title": "Toward the digital twin of additive manufacturing- Integrating thermal simulations, sensing, and analytics to detect process faults", "question_id": "Q8", "question": "What is the machine learning objective in this study?", "choices": { "A": "Regression", "B": "Classification", "C": "Dimension reduction", "D": "Classification and regression", "E": "All of above", "F": "None of above" }, "answer": "B", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Machine Learning task", "term_explanation": "What kind of problem is the paper solving with the data—for example, is it trying to sort things into categories, make predictions about numbers, or group similar items together?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "5", "paper_title": "Toward the digital twin of additive manufacturing- Integrating thermal simulations, sensing, and analytics to detect process faults", "question_id": "Q9", "question": "What machine learning algorithm is used?", "choices": { "A": "SVM", "B": "CNN", "C": "LR", "D": "Regression", "E": "All of above", "F": "None of above" }, "answer": "A", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Machine Learning algorithm", "term_explanation": "A machine learning algorithm (such as linear regression, support vector machine, DNN etc. ) is a mathematical framework or procedure that processes input data to create a model capable of solving a specific ML task." } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "5", "paper_title": "Toward the digital twin of additive manufacturing- Integrating thermal simulations, sensing, and analytics to detect process faults", "question_id": "Q10", "question": "How are the data split during machine learning?", "choices": { "A": "T-T split; 0.7, 0.3", "B": "T-D-T split; 0.8, 0.1, 0.1", "C": "K-fold; 5 fold", "D": "K-fold; 10 fold", "E": "All of above", "F": "None of above" }, "answer": "C", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Splitting strategy (tdt split or k-fold)", "term_explanation": "How is the data being divided up when training the model—for example, which part is used for teaching, which part is used for testing how well it learned?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "5", "paper_title": "Toward the digital twin of additive manufacturing- Integrating thermal simulations, sensing, and analytics to detect process faults", "question_id": "Q11", "question": "How many replications are conducted during machine learning, if any?", "choices": { "A": "10", "B": "256", "C": "1000", "D": "35", "E": "All of above", "F": "None of above" }, "answer": "F", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Number of Replications", "term_explanation": "If the paper did so, how many times is the experiment or test repeated to make sure the results are reliable?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "5", "paper_title": "Toward the digital twin of additive manufacturing- Integrating thermal simulations, sensing, and analytics to detect process faults", "question_id": "Q12", "question": "How many epochs are used during machine learning, if any?", "choices": { "A": "100", "B": "200", "C": "300", "D": "4700", "E": "All of above", "F": "None of above" }, "answer": "F", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Number of Epochs", "term_explanation": "In training the model, how many times does it go through the whole set of data?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "5", "paper_title": "Toward the digital twin of additive manufacturing- Integrating thermal simulations, sensing, and analytics to detect process faults", "question_id": "Q13", "question": "What metrics are used to evaluate the machine learning models?", "choices": { "A": "Accuracy", "B": "F score", "C": "Recall", "D": "MAE", "E": "All of above", "F": "None of above" }, "answer": "B", "metadata": { "Task-oriented Category": "Conclusions & results", "question_key_term": "Performance metrics", "term_explanation": "How do the researchers check whether the model is doing a good job—what measures or scores do they use to judge its performance?" } }, { "subject": "Material Science - Additive Manufacturing", "paper_id": "5", "paper_title": "Toward the digital twin of additive manufacturing- Integrating thermal simulations, sensing, and analytics to detect process faults", "question_id": "Q14", "question": "What are the values for these metrics?", "choices": { "A": "Acc, 0.999; R square 0.98", "B": "Acc, 0.999", "C": "R square 0.98", "D": "F score >0.9", "E": "All of above", "F": "None of above" }, "answer": "D", "metadata": { "Task-oriented Category": "Conclusions & results", "question_key_term": "Performance values", "term_explanation": "What were the actual results or scores from those metrics?" } } ]