Process Monitoring Research Articles

Knowing the spectral directional emissivity of 316L and AlSi10Mg PBF-LB/M surfaces: gamechanger for quantitative in situ monitoring

For a deep process understanding of the laser powder bed fusion process (PBF-LB/M), recording of the occurring surface temperatures is of utmost interest and would help to pave the way for reliable process monitoring and quality assurance. A notable number of approaches for in-process monitoring of the PBF-LB/M process focus on the monitoring of thermal process signatures. However, due to the elaborate calibration effort and the lack of knowledge about the occurring spectral directional emissivity ε′λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\varepsilon }^{\\prime}}_{\\lambda }$$\\end{document}, only a few approaches attempt to measure real temperatures. In this study, to gain initial insights into ε′λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\varepsilon }^{\\prime}}_{\\lambda }$$\\end{document} occurring in the PBF-LB/M process, measurements on PBF-LB/M specimens and metal powder specimens were performed for higher temperatures up to T = 1290 °C by means of the emissivity measurement apparatus (EMMA) of the Center for Applied Energy Research (CAE, Wuerzburg, Germany). Also, measurements at ambient temperatures were performed with a suitable measurement setup. Two different materials—stainless steel 316L and aluminum AlSi10Mg—were examined. The investigated wavelength λ ranges from the visible range (λVIS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\lambda }_{VIS}$$\\end{document} = 0.40–0.75 µm) up to the infrared, λ = 20 µm. The influence of the following factors were investigated: azimuth angle φ, specimen temperature TS, surface texture as for PBF-LB/M surfaces with different scan angles α, and powder surfaces with different layer thicknesses t\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$t$$\\end{document}.

#1340 Validation of artificial intelligence algorithm classifying arteriovenous access aneurysms in hemodialysis: a blinded multicenter prospective study

Abstract Background and Aims In hemodialysis (HD) patients, arteriovenous (AV) access aneurysms may lead to severe and potentially life-threatening consequences, such as rupture. To address this issue, we developed an artificial intelligence-based application that utilizes images of the AV access to classify AV aneurysms [1]. We conducted a blinded, multicenter, prospective pilot study to assess the correlation between the classification results generated by the aneurysm classification application and the independent clinical examination conducted by physicians specializing in vascular access care. Method AV accesses were photographed using tablets. These images were then uploaded to the cloud, where they were classified as either "Advanced" or "Not Advanced" by a convolutional neural network algorithm [1] (Fig. 1A). The study compared AV aneurysm classifications generated by our application to those made by vascular access physicians blinded to the app results. The physicians’ classifications served as the ground truth for our analysis. Results The study was conducted at two vascular access care centers in New York, NY, USA. A total of 121 patients were included in the analysis (Table 1). The physicians’ assessment identified a 21% prevalence of advanced aneurysms. The application accurately classified 84 out of 95 aneurysm images as “Not Advanced” and 20 out of 26 as “Advanced,” resulting in an accuracy of 86.0% (95% CI: 78.5% to 91.6%), a sensitivity of 76.9% (95% CI: 56.4% to 91.0%), a specificity of 88.4% (95% CI: 80.2% to 94.1%), and an area under the receiver operating characteristics curve (AUROC) of 0.87 (95% CI: 0.78 to 0.94) (Fig. 1B). Conclusion Our results demonstrate that an AI-powered application exhibits actionable accuracy in classifying AV aneurysms within a demographically diverse HD population. These findings align with Zhang et al., where a team of vascular access experts classified 1,093 out of 1,341 (81.5%) images as “Not Advanced” aneurysms and 248 (18.5%) as “Advanced,” resulting in an AUROC of 0.96 in the validation set (n = 402) [1]. The difference in AUROCs can be attributed to the fact that, in Zhang et al., vascular access experts evaluated only AV access images, whereas in the present study, they performed a physical examination of the patient. Our tool may have the potential to support aneurysm monitoring processes, enabling timely detection, decision-making, and interventions.

HR analytics and the data collection process: the role of attributions and perceived legitimacy in explaining employees’ fear of datafication

PurposeThis paper analyzes employees’ perceptions of data collection processes for human resource analytics (HRA). More specifically, we study the effect that information sharing practices have on employees’ attributions (i.e. benevolent vs malevolent) through the perceived legitimacy of data collection and monitoring processes. Moreover, we investigate whether employees’ emotional reaction (i.e. fear of datafication) depends on their perceived legitimacy and attributions.Design/methodology/approachThe research is based on a sample of 259 employees operating for an Italian consulting firm that developed and implemented HRA processes in the last 3 years. The hypothesized model has been tested using structural equation modeling (SEM) on Stata 14.FindingsThis paper demonstrates the mediating role of perceived legitimacy in the relationship between information sharing practices and employees’ benevolent and malevolent attributions about data collection and monitoring processes for HRA practices. Results also reveal that perceived legitimacy predicts employees’ fear of datafication, with benevolent attributions that partially mediate this relationship.Practical implicationsThis research indicates that employees perceive, try to make sense of and emotionally react to HRA processes. Moreover, we reveal the crucial role of information sharing practices and perceived legitimacy in determining employees’ attributions and emotional reactions to data collection and monitoring processes.Originality/valueCombining human resource (HR) attributions, HR system strength, information processing and signaling theories, this work explores employees’ perception, attributive processes and emotional reactions to data collection processes for HRA practices.

Long-term monitoring of desert restoration processes using historical stereoscopic imagery and laser scanning data: An example of the Błędów Desert (Poland)

This study aimed to present the spatiotemporal changes associated with the loss and subsequent regaining of the original character of the Błędów Desert (Poland) as a result of restoration efforts. The study area is one of the few places in Europe with a sandy desert character. The research period covered nearly 40 years, covering an area of approximately 1400 ha.The methodology is based on comparing the height structure parameters by analyzing point clouds created from contemporary and historical stereoscopic images. For this purpose, four sets of stereoscopic images were used, including two historical analog images from 1982 to 1996 and contemporary digital data from 2012 to 2018. Additional datasets such as airborne laser scanning data and cadastral databases were used as auxiliary data. The results showed an increase in the area covered by tall vegetation from 2% in 1982 to 18% and 41% in 1996 and 2012, respectively, which decreased to 30% after the completion of restoration activities in 2018. The restoration process has enabled the revival of the original desert character of the study area, although the total area covered by sand decreased compared to its state in the early 1980s. These results indicate the significant potential of the presented methodology in supporting ecosystem management systems, where changes in the height structure of vegetation play a crucial role. This applies to monitoring land abandonment, forest management, and large-scale reclamation work.

Near-Infrared-II Nanomaterials for Activatable Photodiagnosis and Phototherapy.

Near-Infrared-II (NIR-II) spans wavelengths between 1,000 to 1,700 nanometers, featuring deep tissue penetration and reduced tissue scattering and absorption characteristics, providing robust support for cancer treatment and tumor imaging research. This review explores the utilization of activatable NIR-II photodiagnosis and phototherapy based on tumor microenvironments (e. g., reactive oxygen species, pH, glutathione, hypoxia) and external stimulation (e. g., laser, ultrasound, photothermal) for precise tumor treatment and imaging. Special emphasis is placed on the advancements and advantages of activatable NIR-II nanomedicines in novel therapeutic modalities like photodynamic therapy, photothermal therapy, and photoacoustic imaging. This encompasses achieving deep tumor penetration, real-time monitoring of the treatment process, and obtaining high-resolution, high signal-to-noise ratio images even at low material concentrations. Lastly, from a clinical perspective, the challenges faced by activatable NIR-II phototherapy are discussed, alongside potential strategies to overcome these hurdles.

Computational and Experimental Research on the Influence of Supplied Gas Fuel Mixture on High-Temperature Fuel Cell Performance Characteristics

Currently, the process of creating industrial installations is associated with digital technologies and must involve the stage of developing digital models. It is also necessary to combine installations with different properties, functions, and operational principles into a single system. Some tasks require the use of predictive modeling and the creation of “digital twins”. The main processes during the fuel cell modeling involve electrochemical transformations as well as the movement of heat and mass flows, including monitoring and control processes. Numerical methods are utilized in addressing various challenges related to fuel cells, such as electrochemical modeling, collector design, performance evaluation, electrode microstructure impact, thermal stress analysis, and the innovation of structural components and materials. A digital model of the membrane-electrode unit for a solid oxide fuel cell (SOFC) is presented in the article, incorporating factors like fluid dynamics, mass transfer, and electrochemical and thermal effects within the cell structure. The mathematical model encompasses equations for momentum, mass, mode, heat and charge transfer, and electrochemical and reforming reactions. Experimental data validates the model, with a computational mesh of 55 million cells ensuring numerical stability and simulation capability. Detailed insights on chemical flow distribution, temperature, current density, and more are unveiled. Through a numerical model, the influence of various fuel types on SOFC efficiency was explored, highlighting the promising performance of petrochemical production waste as a high-efficiency, low-reagent consumption fuel with a superior fuel utilization factor. The recommended voltage range is 0.6–0.7 V, with operating temperatures of 900–1300 K to reduce temperature stresses on the cell when using synthesis gas from petrochemical waste. The molar ratio of supplied air to fuel is 6.74 when operating on synthesis gas. With these parameters, the utilization rate of methane is 0.36, carbon monoxide CO is 0.4, and hydrogen is 0.43, respectively. The molar ratio of water to synthesis gas is 2.0. These results provide an opportunity to achieve electrical efficiency of the fuel cell of 49.8% and a thermal power of 54.6 W when using synthesis gas as fuel. It was demonstrated that a high-temperature fuel cell can provide consumers with heat and electricity using fuel from waste from petrochemical production.

Modified exponentially weighted moving average control chart for monitoring process dispersion

The exponentially weighted moving average ( EWMA ) charts are widely used memory-type charts for monitoring small to moderate shifts in process parameters. However, the performance of the EWMA chart has been improved over time due to various modifications and enhancements. This paper proposes two modified EWMA ( M ‐ EWMA ) charts to monitor process dispersion. The upper-sided modified EWMA chart is denoted by M ‐ EWMA U , while a lower-sided modified EWMA chart is symbolized as the M ‐ EWMA L chart. Monte Carlo simulations determine the proposed schemes’ run length (RL) properties in zero- and steady-state scenarios. The proposed upper- and lower-sided M ‐ EWMA charts are compared to the upper- and lower-sided CH ‐ EWMA and SJ-EWMA charts. The comparisons reveal that the proposed M-EWMA charts have better detection ability than the upper- and lower-sided CH-EWMA, and SJ-EWMA charts, respectively. Finally, a real-life application is provided to illustrate the implementation of the upper-sided M-EWMA and CH-EWMA charts practically in both zero-state and steady-state cases.

A lightweight sensor ontology for supporting sensor selection, deployment, and data processing in forming processes

AbstractIn the era of smart manufacturing, modern manufacturing systems face high demands for enhancing process performance and reducing machine downtime. Sensors and process data are essential for successfully implementing data-driven approaches to guarantee robust and reliable process monitoring, tool conditioning, or quality assurance. However, the accuracy and performance of such approaches are highly dependent on the quality of the gathered sensor data and influenced by the implemented data acquisition and processing methods. For this purpose, this work proposes a lightweight sensor ontology to provide a comprehensive overview to characterize underlying relationships between the physical environment and the quality of the data sets. The extended sensor ontology, in combination with domain knowledge, aims to support engineers in fully exploiting the potential of sensor data to obtain trustworthy data sets in forming technologies. As a result, this approach can improve the implementation of automated and data-driven process monitoring of forming systems and tools.

Collaborative optimization decision making of cement grinding process operational indicators considering dual dynamic problem

The cement grinding process requires monitoring of unit power consumption and specific surface area indicators to improve production efficiency and product quality. The strong coupling between operational variables necessitates careful adjustment of these variables to prevent production from becoming instability. Furthermore, the continuous and time-dependent nature of the process makes this adjustment particularly challenging. To address this dual dynamic problem, we have developed a multi-objective optimization model, with global optimization as the desired outcome, to optimize unit power consumption and specific surface area. Our optimization algorithm, termed “Optimization Algorithm - Dynamic Search Space and Rolling Time Domain” (OA-DSTR), considers the problem of dynamic production process. First, the algorithm uses a dynamic search space strategy, allowing for changes in the constraint range of the operational variables and introducing a fluctuation coefficient (FC) to measure solution rationality. Second, to deal with the time-dependent problem, we have added a rolling time domain strategy, enabling real-time monitoring of the cement grinding process. The experimental results show that OA-DSTR not only ensures global optimization, but also realizes the tracking of dynamic conditions, improving the stability of the cement grinding process and achieving a lower FC value.

Research on electronic optical monitoring technology and system design for vacuum electron beam welding process

Electron beam welding technology is increasingly used in industrial manufacturing due to its high power density and the purity of welded joints. In vacuum environments, traditional optical and machine vision monitoring methods are susceptible to contamination by metal vapors, which hinders the monitoring effect. Due to the limitations of optical monitoring methods, electronic optical imaging technology is a new alternative monitoring method that has been introduced into the electron beam processing in recent years, but the complexity of the industrial electron beam welding environment makes it relatively less studied in the field of electron beam welding. In this study, a YAG scintillator detector is proposed to collect the backscattered electron information generated by the interaction of electrons with the sample, and an FPGA real-time processing system is used to realize the weld quality monitoring in the industrial electron beam welding processes. The distribution law of backscattered electrons in space is simulated by Monte Carlo method, and the results show that the number of distribution in the 40°∼60° angle region is more, the backscattering coefficient increases with the increase of the incident angle of the electron beam, and the backscattering coefficient of the samples with large atomic numbers is relatively high. The field monitoring experiments show that the designed system exhibits good sensitivity to the characteristic morphology, and the imaging resolution reaches more than 200 μm, and the weld hole defects are clearly visible. A good linear correlation between the current change curve during scanning and the actual shape profile is shown, while the image resolution is better when the spot current is 1 mA.

Simultaneous monitoring of competing photo-induced weathering processes in colloidal CdSe/ZnS semiconductor quantum dots using multivariate frequency-domain dynamic photoluminescence measurements

Coincident photo-induced weathering processes in oleic acid capped CdSe/ZnS semiconductor quantum dots (QDs), stimulated by exposure to artificial sunlight in samples with different headspace volumes, were monitored using multichannel frequency-domain photoluminescence (PL) decay measurements. The combination of process monitoring using matrix-formatted measurements and multivariate data mining permits resolution of the spectra, frequency-domain decays and kinetic profiles of the PL emitted by distinct QD subpopulations. The data indicate that three components contribute to the PL: photodarkened QDs that emit bluer, shorter-lived spectra reflecting photoionization-induced QD charging and ligand desorption; photobrightened QDs that emit redder, short-lived spectra indicative of photooxidative QD surface passivation; and photoripened QDs that emit weak, red-shifted, large bandwidth, very long-lived spectra reflecting increasing nanoparticle growth and polydispersity. Analysis of the kinetic traces of the QD subpopulations during photoirradiation using systems of exponentials revealed the extent of coupling between the coincident photoinduced processes. The assignment of these components was supported by their emission maxima, spectral bandwidths, average and irradiation-time specific PL decay times, irradiation-dependent kinetic profiles, TEM images and literature precedents. As expected, the results show that QD photodarkening is faster and photobrightening is slower in the samples that have more exposure to water and oxygen. They also show that the dominant photodarkening and photobrightening pathways in those samples are not as strongly coupled, indicating the importance of contributions by additional processes in that case. Transmission electron microscopy (TEM) images were consistent with the PL results, but the QD samples proved too polydisperse to be characterized by single-beam dynamic light scattering measurements.

Dielectric Monitoring of Microwave Extraction Processes

Our research investigated the dielectric traceability of microwave and ultrasound intensified extraction processes of plant by-products. For the experiments, I used plant by-products from beetroot, carrot and raspberry, which were extracted in 5% suspensions. The dielectric behaviour of the extracts was investigated in the frequency range 300-2400 MHz using an open-ended coaxial probe. For both the ultrasonic and microwave intensified procedures, the reported energy was 30, 45 and 60 kJ, respectively. Based on my research results, I found that the dielectric constant measured in the frequency range 700-900 MHz is closely related to the yield of total polyphenol and pectin regardless of the feedstock and treatment used, but the method has limitations due to the presence of components that affect the physical structure and the concentration of target components below the limit of detection.

Ultrasonic mode conversion for in-line foam structure measurement in highly aerated batters using machine learning

An ultrasonic-based method was developed to enable in-line measurements of foam structure parameters for highly aerated batters by mode conversion. Biscuit batters were foamed to different degrees (density: 364–922 g/L) by varying the mixing head speed and pressure. Density and foam structure changes were detected by efficient offline analytics (nref measurement = 96). Ultrasonic signal data were recorded using two ultrasonic sensors attached to an industry-standard tube. Mode conversion effects in the ultrasonic signals were obtained to predict the rheological parameters of the batters. The frequency range in which surface waves are expected was particularly suitable for detecting rheological changes in highly aerated batters. An ultrasonic-based, online-capable method for process monitoring was implemented and established regarding feature selection in combination with machine learning and 5-fold cross-validation. The developed ultrasonic sensor system shows high accuracy for online density measurement (R2 = 0.98) and offers decent accuracy for measurements of foam structure parameters (Bubble count: R2 = 0.95, Relative span: R2 = 0.93, Sauter diameter: R2 = 0.83). The main benefit of this novel technique is that integrating ultrasonic signal features based on mode conversion leads to a robust foam structure analysis, which has the advantage of being retrofitable into existing processes.

Integrity monitoring for decentralized redundant IMUs/GNSS integrated navigation system with correlated measurements

In civil aviation systems, the inertial measurement unit (IMU)/Global Navigation Satellite System (GNSS) integrated navigation remains the most effective navigation scheme. Based upon this, the utilization of redundant IMUs and a multi-constellation system can significantly enhance navigation and integrity performance. However, redundant IMUs/GNSS integrated navigation systems are susceptible to more complex failure modes, including GNSS faults (satellite and constellation faults) and IMU faults. Additionally, dealing with correlated measurements between redundant constructions presents a challenging issue. Therefore, we propose an integrity monitoring method for decentralized redundant IMUs/GNSS integrated navigation systems. This method considers GNSS faults and IMU fault in the integrity monitoring process, specifically designed for decentralized filter construction with redundancy. Furthermore, due to the correlated measurements between redundant filters, optimal fusion coefficients are determined by considering the unbiased estimate and minimizing the trace covariance of the primary filter. An optimal allocation scheme for continuity risk is also established to improve navigation and integrity performance. Simulation results demonstrate the feasibility and effectiveness of the proposed integrity monitoring method. Moreover, in the context of correlated measurements, the integrated navigation system also exhibits superiority.

Semi-supervised process monitoring based on self-training PCA model

Data-centric engineering has recently become as a hot spot in both areas of artificial intelligence and data science, which shifts the focus of engineering application from model to data itself. The model for data-driven process monitoring is usually developed upon a large number of normal data samples, which have been assumed to be well evaluated in advance. However, the actual quality of those training data samples has a great impact to the monitoring performance. If those samples with low quality are included for modeling, the monitoring performance could be severely deteriorated. In practice, due to expensive and time-consuming data quality evaluation procedures, we may only have a quite limited number of evaluated data samples, and hold a large number of unevaluated data samples. This paper aims to develop a semi-supervised monitoring model, which can simultaneously incorporate the evaluated dataset and the original unevaluated dataset. As a result, the new semi-supervised monitoring scheme can save a lot of human efforts, and could be particularly useful in those processes which can only provide a small portion of evaluated data samples in time. The feasibility and efficiency of the new proposed monitoring scheme are examined through case studies of a numerical example and the TE benchmark process.

Optimizing Zakat Management to Improve Education Quality in Palu City, Central Sulawesi, Indonesia

This study aims to discuss the zakat management optimization in improving education quality at National Zakat Charity Agency (BAZNAS) of Palu. This study used a qualitative approach and the data was gathered through field observation, in-depth interviews, and written document analysis. The data analysis, then, was analysed through data reduction, data presentation, and conclusion drawing. The results of this study show that the process of optimizing zakat to improve education quality at Baznas Palu city was conducted according to Islamic zakat management principles which includes planning, implementation, reporting, and monitoring process in the program of collection, distribution, and utilization at National Zakat Charity Agency Palu. The agency also follows professional administration principles such as transparency, accountability, responsibility, independence, fairness, and Islamic shariah compliance. This study also found that the agency has focused zakat distribution for specific educational institutions, both formal and informal. The agency also provide scholarships for academic achievement, and educational cost assistance for underprivileged children. Furthermore, the agency provides capital support for productive businesses runned by disadvantaged families or communities to improve their incomes.

Optimization of the concentration of ozone generated by DBD using PSO algorithm for water treatment process

The water treatment process with ozone is influenced by various operating parameters and environmental factors that can impact its efficiency. In this study, experiments were conducted using a Venturi pumping frame to investigate the effects of three controllable variables: oxygen flow height, applied voltage level, and water flow rate. The tests aimed to develop a mathematical model that accurately represents the relationship between these input variables and the resulting ozone concentration in the treated water. The experimental data was analyzed using the MODDE 5.0 software, a specialized application for statistical modeling and design of experiments. By fitting the data to appropriate model equations, a mathematical model was obtained that quantifies the influence of each variable and their interactions on the ozone concentration response. To optimize the process performance, a particle swarm optimization (PSO) algorithm was employed to extract the best-fit parameters for the mathematical model. PSO is a computational technique inspired by the social behavior of bird flocks or fish schools, utilizing a population of candidate solutions that evolve iteratively to converge on the global optimum solution. In this case, PSO searched for the model parameter values that minimized the error between predicted and experimentally measured ozone concentrations, rapidly converging to an accurate solution. The optimized mathematical model enables predicting the ozone concentration under any combination of oxygen flow height, voltage, and water flow rate within the experimental range. This predictive capability facilitates identifying the optimum operating conditions to maximize ozone concentration, thereby enhancing the efficiency of the water treatment process. The model serves as a valuable tool for process control, monitoring, and optimization, ensuring consistent treatment quality while minimizing resource consumption and operational costs.

Analysis of critical states based on acoustic emission signals during progressive failure of wood.

The analysis of critical states during fracture of wood materials is crucial for wood building safety monitoring, wood processing, etc. In this paper, beech and camphor pine are selected as the research objects, and the acoustic emission signals during the fracture process of the specimens are analyzed by three-point bending load experiments. On the one hand, the critical state interval of a complex acoustic emission signal system is determined by selecting characteristic parameters in the natural time domain. On the other hand, an improved method of b_value analysis in the natural time domain is proposed based on the characteristics of the acoustic emission signal. The K-value, which represents the beginning of the critical state of a complex acoustic emission signal system, is further defined by the improved method of b_value in the natural time domain. For beech, the analysis of critical state time based on characteristic parameters can predict the "collapse" time 8.01 s in advance, while for camphor pines, 3.74 s in advance. K-value can be analyzed at least 3 s in advance of the system "crash" time for beech and 4 s in advance of the system "crash" time for camphor pine. The results show that compared with traditional time-domain acoustic emission signal analysis, natural time-domain acoustic emission signal analysis can discover more available feature information to characterize the state of the signal. Both the characteristic parameters and Natural_Time_b_value analysis in the natural time domain can effectively characterize the time when the complex acoustic emission signal system enters the critical state. Critical state analysis can provide new ideas for wood health monitoring and complex signal processing, etc.

Tensile testing data of additive manufactured ASTM D638 standard specimens with embedded internal geometrical features

Additive manufacturing (AM) is now widely used for research and industrial production. The benchmark data for mechanical properties of additively manufactured specimens is very useful for many communities. This data article presents a tensile testing dataset of ASTM D638 size specimens without and with embedded internal geometrical features printed using polylactic acid (PLA) in a Fused Filament Fabrication (FFF) additive manufacturing process. The added features can mimic defects of various shapes and sizes. This work is a supplement to the published research article Assisted defect detection by in-process monitoring of additive manufacturing using optical imaging and infrared thermography (Additive Manufacturing, 2023, 103483). The printed specimens were tensile tested. Stress-strain graphs were developed and used to calculate the mechanical properties such as ultimate tensile strength (UTS) and strain at UTS. The mechanical properties, the correlations between mechanical properties and size, shape and location of geometrical features (defects), and the trends in mechanical properties can be useful in benchmarking the results of other researchers.

Prediction of mechanical properties of LPBF built part based on process monitoring and Gaussian process regression

As a highly promising technology in additive manufacturing, the laser powder bed fusion has only limited application due to its low reproducibility. In this study, the image information of the 316L specimen after laser scanning and powder paving of each layer was acquired by a complementary metal–oxide–semiconductor industrial camera. The important features were selected, extracted and quantificated by analyzing the tensile test results. Finally, combined with the laser power, the quantified features were as input of a Gaussian process regression model based on optimization algorithm of grid search to predict the tensile strength of 316L specimen. The results show that the quantized image features have a significant improvement on the regression effect, and the coefficient of determination (R 2) is improved from 63% to 90.57% compared to using only the laser power as input.