Quality Inspection Research Articles

Chip Surface Character Recognition Based on OpenCV

Chip surface character recognition is an important part of quality inspection in the field of microelectronics manufacturing. By recognizing the character information on the chip, automated production, quality control, and data collection and analysis can be achieved. This article studies a chip surface character recognition method based on the OpenCV vision library. Firstly, the obtained chip images are preprocessed. Secondly, the template matching method is used to locate the chip position. In addition, the surface characters on the chip are individually segmented, and each character image is extracted separately. Finally, a Support Vector Machine (SVM) is used to classify and recognize characters. The results show that this method can accurately recognize the surface characters of chips and meet the requirements of chip quality inspection.

Effective Privacy Protection Strategies for Pregnancy and Gestation Information From Electronic Medical Records: Retrospective Study in a National Health Care Data Network in China.

Pregnancy and gestation information is routinely recorded in electronic medical record (EMR) systems across China in various data sets. The combination of data on the number of pregnancies and gestations can imply occurrences of abortions and other pregnancy-related issues, which is important for clinical decision-making and personal privacy protection. However, the distribution of this information inside EMR is variable due to inconsistent IT structures across different EMR systems. A large-scale quantitative evaluation of the potential exposure of this sensitive information has not been previously performed, ensuring the protection of personal information is a priority, as emphasized in Chinese laws and regulations. This study aims to perform the first nationwide quantitative analysis of the identification sites and exposure frequency of sensitive pregnancy and gestation information. The goal is to propose strategies for effective information extraction and privacy protection related to women's health. This study was conducted in a national health care data network. Rule-based protocols for extracting pregnancy and gestation information were developed by a committee of experts. A total of 6 different sub-data sets of EMRs were used as schemas for data analysis and strategy proposal. The identification sites and frequencies of identification in different sub-data sets were calculated. Manual quality inspections of the extraction process were performed by 2 independent groups of reviewers on 1000 randomly selected records. Based on these statistics, strategies for effective information extraction and privacy protection were proposed. The data network covered hospitalized patients from 19 hospitals in 10 provinces of China, encompassing 15,245,055 patients over an 11-year period (January 1, 2010-December 12, 2020). Among women aged 14-50 years, 70% were randomly selected from each hospital, resulting in a total of 1,110,053 patients. Of these, 688,268 female patients with sensitive reproductive information were identified. The frequencies of identification were variable, with the marriage history in admission medical records being the most frequent at 63.24%. Notably, more than 50% of female patients were identified with pregnancy and gestation history in nursing records, which is not generally considered a sub-data set rich in reproductive information. During the manual curation and review process, 1000 cases were randomly selected, and the precision and recall rates of the information extraction method both exceeded 99.5%. The privacy-protection strategies were designed with clear technical directions. Significant amounts of critical information related to women's health are recorded in Chinese routine EMR systems and are distributed in various parts of the records with different frequencies. This requires a comprehensive protocol for extracting and protecting the information, which has been demonstrated to be technically feasible. Implementing a data-based strategy will enhance the protection of women's privacy and improve the accessibility of health care services.

Hyperspectral imaging system for pre- and post-harvest defect detection in paprika fruit

External defects in paprika fruit (Capsicum annuum, L. var. angulosum) directly impact their storage and marketability, potentially leading to internal decay and altering fruit appearance. However, automatically detecting various paprika defects, including cracks, scars, diseases, and bruises, poses a significant challenge due to variations in defect size and visual similarities among different types of defects. This study proposed a comprehensive, accurate, and rapid defect detection method utilizing hyperspectral imaging (HSI) alongside multivariate analysis and imaging technologies. The reflective surface of fruit poses a primary limitation to efficient defect detection using imaging technologies. Therefore, a polarized HSI visible–near-infrared (VIS-NIR, 397–1000 nm) system was developed, and over 500 paprika fruit with various types of defects were imaged using the developed system. Additionally, fruit were manually bruised in different sizes to ensure spectral data variability and measured every 12 hours for two days. Spectral data (over 4580 spectral data) from the regions of interest (ROIs) of the paprika fruit samples, including manually bruised fruit, were extracted. Subsequently, a partial least squares discrimination analysis (PLS-DA) model was developed to distinguish between normal and defective fruit. Remarkably, the developed model achieved an accuracy of over 99 % in predicting normal and defective fruit. Moreover, the chemical images generated enhance the inspection process. The successive projection algorithm (SPA) and variable importance in projection (VIP) were further utilized for critical band selection, yielding an accuracy of 93 % and 98 % in the prediction set, respectively. Furthermore, a classification algorithm based on analysis of variance (ANOVA) was employed to determine the optimal wavelength band ratios (F-values). This approach yielded an impressive accuracy rate of over 88.8 % and further enhanced the performance of the band ratio by an improved watershed segmentation algorithm (IWSA) to reach an accuracy of 91.3 % in multi-defect detection. These findings offer a practical and efficient solution for the external quality inspection of paprika fruit, particularly in cases with naturally occurring defects. By integrating hyperspectral imaging, PLS-DA, band selection methods, ANOVA-based classification, and IWSA, this comprehensive analysis provides a reliable and practical means for identifying and categorizing defects in paprika fruit, ultimately enhancing product quality and consumer satisfaction.

Implementing mobile eye tracking in psychological research: A practical guide

Eye tracking provides direct, temporally and spatially sensitive measures of eye gaze. It can capture visual attention patterns from infancy through adulthood. However, commonly used screen-based eye tracking (SET) paradigms are limited in their depiction of how individuals process information as they interact with the environment in “real life”. Mobile eye tracking (MET) records participant-perspective gaze in the context of active behavior. Recent technological developments in MET hardware enable researchers to capture egocentric vision as early as infancy and across the lifespan. However, challenges remain in MET data collection, processing, and analysis. The present paper aims to provide an introduction and practical guide to starting researchers in the field to facilitate the use of MET in psychological research with a wide range of age groups. First, we provide a general introduction to MET. Next, we briefly review MET studies in adults and children that provide new insights into attention and its roles in cognitive and socioemotional functioning. We then discuss technical issues relating to MET data collection and provide guidelines for data quality inspection, gaze annotations, data visualization, and statistical analyses. Lastly, we conclude by discussing the future directions of MET implementation. Open-source programs for MET data quality inspection, data visualization, and analysis are shared publicly.

Quality and Efficiency of Coupled Iterative Coverage Path Planning for the Inspection of Large Complex 3D Structures

To enable unmanned aerial vehicles to generate coverage paths that balance inspection quality and efficiency when performing three-dimensional inspection tasks, we propose a quality and efficiency coupled iterative coverage path planning (QECI-CPP) method. First, starting from a cleaned and refined mesh model, this was segmented into narrow and normal spaces, each with distinct constraint settings. During the initialization phase of viewpoint generation, factors such as image resolution and orthogonality degree were considered to enhance the inspection quality along the path. Then, the optimization objective was designed to simultaneously consider inspection quality and efficiency, with the relative importance of these factors adjustable according to specific task requirements. Through iterative adjustments and optimizations, the coverage path was continuously refined. In numerical simulations, the proposed method was compared with three other classic methods, evaluated across five aspects: image resolution, orthogonality degree, path distance, computation time, and total path cost. The comparative simulation results show that the QECI-CPP achieves maximum image resolution and orthogonality degree while maintaining inspection efficiency within a moderate computation time, demonstrating the effectiveness of the proposed method. Additionally, the flexibility of the planned path is validated by adjusting the weight coefficient in the optimized objective function.

Field Evaluation and Application of Intelligent Quality Control Systems

During road construction, the accuracy of compaction work is critical for the structural stability and maintenance of the road. Although the plate load test (PLT) is commonly used for quality inspections, it is impractical to test every section due to time and cost constraints. Therefore, simpler testing methods are being extensively developed. This study compared quality inspection results using the commonly used PLT, the relatively simple dynamic cone penetrometer test (DCPT), the lightweight deflectometer (LWD) test, and an intelligent quality control system equipped with accelerometer and global positioning system (GPS) sensors in intelligent compaction (IC) rollers. The results showed a strong correlation between the conventional tests (PLT, DCPT, and LWD) and the values obtained from the intelligent quality control system. The correlation analysis between the intelligent quality control system and PLT, LWDT, and DCPT yielded R-square values of 0.69, 0.91, and 0.95, respectively, indicating significantly high correlations. The implementation of intelligent quality management systems in earthwork construction projects will facilitate a thorough verification of the compaction quality throughout all construction segments, ensuring consistent compaction across the project. By enabling real-time data acquisition and analysis, these systems differ markedly from traditional methods, reducing the frequency and necessity of manual inspections. This approach not only streamlines construction processes, but also enhances operational efficiency. As a result, integrating these intelligent systems is anticipated to significantly increase productivity by optimizing the workflow and resource utilization in earthwork construction.

A Bayesian hypothesis testing model for quality control of mass customized cabinet manufacturing process

The demand for customization of cabinets and homes has increased, and customized product components are cumbersome in size and time-consuming. In order to realize the mass production of customized cabinet furniture, research from the custom design, optimization of factory machine production process and quality inspection, as well as the flow of the way, is to realize the fundamental method of large-scale production of cabinet furniture. Based on modern intelligent information processing functions instead of manual ordering, order splitting, and unit parts for reasonable division of classification, optimize the topology of the production line, to achieve modern intelligent production of panels. However, in the production process, there is a lack of on-line inspection of some links, and the yield of the product is affected. This thesis establishes a Bayesian hypothesis testing model based on the topological model of the production line process, and through the actual production data, the defective nodes of the manufacturing process are retraced to achieve the purpose of optimizing the customized production.

A morphology-Euclidean-linear recognition method for rebar point clouds of highway tunnel linings during the construction phase

PurposeLaser point clouds are a 3D reconstruction method with wide range, high accuracy and strong adaptability. Therefore, the purpose is to discover a construction point cloud extraction method that can obtain complete information about the construction of rebar, facilitating construction quality inspection and tunnel data archiving, to reduce the cost and complexity of construction management.Design/methodology/approachFirstly, this paper analyzes the point cloud data of the tunnel during the construction phase, extracts the main features of the rebar data and proposes an M-E-L recognition method. Secondly, based on the actual conditions of the tunnel and the specifications of Chinese tunnel engineering, a rebar model experiment is designed to obtain experimental data. Finally, the feasibility and accuracy of the M-E-L recognition method are analyzed and tested based on the experimental data from the model.FindingsBased on tunnel morphology characteristics, data preprocessing, Euclidean clustering and PCA shape extraction methods, a M-E-L identification algorithm is proposed for identifying secondary lining rebars in highway tunnel construction stages. The algorithm achieves 100% extraction of the first-layer rebars, allowing for the three-dimensional visualization of the on-site rebar situation. Subsequently, through data processing, rebar dimensions and spacings can be obtained. For the second-layer rebars, 55% extraction is achieved, providing information on the rebar skeleton and partial rebar details at the construction site. These extracted data can be further processed to verify compliance with construction requirements.Originality/valueThis paper introduces a laser point cloud method for double-layer rebar identification in tunnels. Current methods rely heavily on manual detection, lacking objectivity. Objective approaches for automatic rebar identification include image-based and LiDAR-based methods. Image-based methods are constrained by tunnel lighting conditions, while LiDAR focuses on straight rebar skeletons. Our research proposes a 3D point cloud recognition algorithm for tunnel lining rebar. This method can extract double-layer rebars and obtain construction rebar dimensions, enhancing management efficiency.

An Efficient and Accurate Quality Inspection Model for Steel Scraps Based on Dense Small-Target Detection

Scrap steel serves as the primary alternative raw material to iron ore, exerting a significant impact on production costs for steel enterprises. With the annual growth in scrap resources, concerns regarding traditional manual inspection methods, including issues of fairness and safety, gain increasing prominence. Enhancing scrap inspection processes through digital technology is imperative. In response to these concerns, we developed CNIL-Net, a scrap-quality inspection network model based on object detection, and trained and validated it using images obtained during the scrap inspection process. Initially, we deployed a multi-camera integrated system at a steel plant for acquiring scrap images of diverse types, which were subsequently annotated and employed for constructing an enhanced scrap dataset. Then, we enhanced the YOLOv5 model to improve the detection of small-target scraps in inspection scenarios. This was achieved by adding a small-object detection layer (P2) and streamlining the model through the removal of detection layer P5, resulting in the development of a novel three-layer detection network structure termed the Improved Layer (IL) model. A Coordinate Attention mechanism was incorporated into the network to dynamically learn feature weights from various positions, thereby improving the discernment of scrap features. Substituting the traditional non-maximum suppression algorithm (NMS) with Soft-NMS enhanced detection accuracy in dense and overlapping scrap scenarios, thereby mitigating instances of missed detections. Finally, the model underwent training and validation utilizing the augmented dataset of scraps. Throughout this phase, assessments encompassed metrics like mAP, number of network layers, parameters, and inference duration. Experimental findings illustrate that the developed CNIL-Net scrap-quality inspection network model boosted the average precision across all categories from 88.8% to 96.5%. Compared to manual inspection, it demonstrates notable advantages in accuracy and detection speed, rendering it well suited for real-world deployment and addressing issues in scrap inspection like real-time processing and fairness.

Enhancing automated fiber placement process monitoring and quality inspection: A hybrid thermal vision based framework

Automated Fiber Placement (AFP) has revolutionized composite manufacturing, yet quality assurance remains challenging due to the significant impact of emerging defects on part quality and the current reliance on time-consuming manual inspection protocols. This paper presents a comprehensive hybrid framework that enhances AFP process monitoring and quality inspection by integrating thermal vision with innovative methodologies. Our framework combines model-based and data-driven algorithms across three modules to address key AFP inspection tasks, including in-situ monitoring, dynamic tow identification, defect detection, segmentation, localization, and quantitative lay-up quality evaluation. The setup-independent spatial–temporal analysis algorithm estimates tow boundaries with sub-pixel accuracy. An optimized SVM classifier, trained on an extensive AFP defect database, achieves a defect detection accuracy of 96.4% and an F1-score of 96.43%, meeting industry standards. The active contours-based segmentation and localization module provides critical qualitative traits such as defect shape, size, and location. Moreover, the novel Defect Area Percentage (DAP) metric enables precise quantitative defect impact evaluation at both the course and tow levels. By consolidating qualitative and quantitative outcomes, the system offers real-time high-level feedback for informed decision-making, significantly improving process performance and reducing machine downtimes. This proactive approach advances AFP process monitoring and quality inspection and positions our framework as a promising solution for next-generation composite manufacturing.

Unleashing Sustainable Efficiency: The Integration of Computer Vision into Industry 4.0

The Internet of Things, artificial intelligence, machine learning, and big data are just a few of the cutting-edge technologies that are being integrated into manufacturing processes as part of the “Industry 4.0” revolution. Computer vision is an essential component of Industry 4.0 regarding sustainability, developed as a disruptive technology that extracts and interprets visual information from digital photos or videos using image processing techniques and advanced models. In the context of Industry 4.0, this article offers an overview of computer vision, including its associated prospects, difficulties, and applications. A particular emphasis is placed on sustainability. It explores computer vision applications in robotics and automation, safety and security, process optimization, augmented reality, robotics and inspection, object identification and tracking, predictive maintenance, and quality control and inspection. The study also identifies the critical approaches used to overcome the difficulties in implementing computer vision solutions. Incorporating computer vision into Industry 4.0 holds promise for unleashing unprecedented levels of efficiency, novelty, and competitiveness in the industrial sector. The manufacturing and industrial sectors may use Industry 4.0‘s prospects and adopt sustainable practices by utilizing computer vision and overcoming its inherent limits. This will help to create an eco-conscious and efficient future.

Assessing Electronics with Advanced 3D X-ray Imaging Techniques, Nanoscale Tomography, and Deep Learning

This paper presents advanced workflows that combine 3D X-ray microscopy (XRM), nanoscale tomography, and deep learning (DL) to generate a detailed visualization of the interior of electronic devices and assemblies to enable the study of internal components for failure analysis (FA). Newly developed techniques, such as the integration of DL-based algorithms for 3D image reconstruction to improve scan quality through increased contrast and denoising, are also discussed in this article. In addition, a DL-based tool called DeepScout is presented. DeepScout uses 3D XRM scans in targeted regions of interest as training data for upscaling high-resolution in a low-resolution dataset, of a wider field of view, using a neural network model. Ultimately, these workflows can be run independently or complementary to other multiscale correlative microscopy evaluations, e.g., electron microscopy, and they will provide valuable insights into the inner workings of electronic packages and integrated circuits at multiple length scales, from macroscopic features on electronic devices (i.e., hundreds of mm) to microscopic details in electronic components (in the tens of nm). Understanding advanced electronic systems through X-ray imaging and machine learning—perhaps complemented with some additional correlative microscopy investigations—can speed development time, increase cost efficiency, and simplify FA and quality inspection of printed circuit boards (PCBs) and electronic devices assembled with new emerging technologies.

Does outsourcing enable the survival of good care homes? A longitudinal analysis of all care homes in England, 2011–2023

BackgroundIt is unclear whether outsourcing has enabled the growth and survival of the best care homes, as intended. We aimed to test whether ownership (for-profit, public and third sector (non-profit))...

Disparities in Access to Highly Rated Skilled Nursing Facilities among Medicare Beneficiaries with Opioid Use Disorder

ObjectivesTo investigate disparities in admissions to highly rated skilled nursing facilities (SNFs) between Medicare beneficiaries with and without opioid use disorder (OUD). DesignNationwide, retrospective observational cohort. Setting and ParticipantsMedicare Fee-for-Service beneficiaries aged ≥18 years admitted to SNFs following hospitalization during 2016-2020 (n = 30,922 with OUD and n = 137,454 without OUD). MethodsData used were 100% Medicare inpatient claims, nursing home administrative databases, and Nursing Home Compare. We identified hospitalized patients with and without OUD and matched them on age, sex, Part D low-income subsidy (LIS), and residential county. We compared the overall and component (quality, staffing, and health inspections) star ratings of SNFs that beneficiaries entered. Beneficiary-level regression models were conducted adjusting for race and ethnicity, Medicare-Medicaid dual status, comorbidity score, hospital length of stay, and state and year fixed effects. ResultsThe overall study sample had a mean (SD) age of 71.4 (11.4) years, 63.9% were female, and 57.4% had LIS. Among beneficiaries with OUD, 50.3% entered SNFs with above-average (4 or 5) overall rating compared with 51.3% among those without OUD. Distributions of above-average ratings among beneficiaries with and without OUD were as follows: 63.9% vs 62.2% for quality, 32.8% vs 34.9% for health inspections, and 46.2% vs 45.0% for staffing, respectively. Adjusted regression models indicated that beneficiaries with OUD were less likely to be admitted to facilities with above-average overall (OR 0.90, 95% CI 0.87-0.92), health inspection (OR 0.90, 95% CI 0.88-0.93), and staffing (OR 0.91, 95% CI 0.89-0.94) ratings compared with beneficiaries without OUD, whereas quality (OR 0.98, 95% CI 0.95-1.01) ratings did not differ. Conclusions and ImplicationsDespite mixed results on component ratings, our findings suggest a concerning disparity in the overall quality of SNFs admitting Medicare beneficiaries with OUD. Enhancing equitable access to high-quality SNF care for individuals with OUD is imperative amid rising demand and legal protections under the American Disabilities Act.

Enhancing Adaptability and Autonomy in Cooperative Selective Compliance Assembly Robot Arm Robots: Implementation of Coordination and Rapidly Exploring Random Tree Algorithms for Safe and Efficient Manipulation Tasks

In this study, a cooperative robotic system comprising two Selective Compliance Assembly Robot Arm (SCARA) robots was developed and simulated. An algorithm was proposed for the coordination of robots in cooperative tasks, along with a Rapidly exploring Random Tree (RRT) path planner for obstacle avoidance. The proposed system proved effective in transferring objects between robots and in handling various scenarios of variable complexity without collisions. The implementation of advanced trajectory planning and coordination algorithms significantly improves the adaptability and autonomy of robotic systems, allowing robots to predict and react to the movements of their counterparts and changes in the environment in real time. This capability is crucial for maintaining a safe and efficient work environment. The importance of synchronization and effective communication between robots is highlighted to avoid collisions and optimize trajectories and cycle times. All tests were conducted in virtual environments, allowing for the evaluation and refinement of the performance of the robots’ performance under controlled conditions. The positive results obtained in the simulations suggest that the system is well suited for future practical implementation in industrial and manufacturing applications, such as chemical handling, collaborative welding, quality inspection, among others. These findings underscore the potential of the cooperative SCARA system to improve the efficiency and safety in industrial applications using advanced algorithms and control techniques, establishing a solid foundation for future research and development in the field of cooperative robotics.

Rapid and Non-Destructive Geographical Origin Identification of Chuanxiong Slices Using Near-Infrared Spectroscopy and Convolutional Neural Networks

Ligusticum Chuanxiong, a perennial herb of considerable medicinal value commonly known as Chuanxiong, holds pivotal importance in sliced form for ensuring quality and regulating markets through geographical origin identification. This study introduces an integrated approach utilizing Near-Infrared Spectroscopy (NIRS) and Convolutional Neural Networks (CNNs) to establish an efficient method for rapidly determining the geographical origin of Chuanxiong slices. A dataset comprising 300 samples from 6 distinct origins was analyzed using a 1D-CNN model. In this study, we initially established a traditional classification model. By utilizing the Spectrum Outlier feature in TQ-Analyst 9 software to exclude outliers, we have enhanced the performance of the model. After evaluating various spectral preprocessing techniques, we selected Savitzky–Golay filtering combined with Multiplicative Scatter Correction (S-G + MSC) to process the raw spectral data. This approach significantly improved the predictive accuracy of the model. After 2000 iterations of training, the CNN model achieved a prediction accuracy of 92.22%, marking a 12.09% improvement over traditional methods. The application of the Class Activation Mapping algorithm not only visualized the feature extraction process but also enhanced the traditional model’s classification accuracy by an additional 7.41% when integrated with features extracted from the CNN model. This research provides a powerful tool for the quality control of Chuanxiong slices and presents a novel perspective on the quality inspection of other agricultural products.

Key parameters and effects in image processing and aggregate–aggregate contact calculation of asphalt mixtures

At present, there is no standard way of acquiring images, addressing errors in various image processing steps, and analyzing skeleton contact parameters. To promote the application of image processing technique in the design, construction monitoring, and quality inspection of asphalt mixtures, this study investigates the key factors affecting the two-dimensional image processing accuracy of asphalt mixtures, and the impact of parameters on the contact structure of asphalt mixture skeleton during the contact analysis is studied. Finally, data accuracy requirements for image processing and contact analysis are proposed. The results indicate that the camera lens should be parallel to the slice with a tilt angle of less than 15°, the error of the passing rate of each sieve remains within 5.0 %. The smaller the aggregate particle size, the greater the impact of aggregate edge recognition error on the aggregate particle size, aggregates with a particle size greater than 2.36 mm are less affected by edge recognition error. For aggregate–aggregate contact analysis, it is appropriate to set the contact threshold to 0.23 times the minimum calculated particle size. To ensure the accuracy of image processing and contact analysis, at least 10 cross-sectional images should be prepared, and the average value can be taken as the representative value of the indices. The conclusions can improve the accuracy and reliability of image processing, providing more accurate guidance for the design and construction of asphalt mixture.

DSN-BR-Based Online Inspection Method and Application for Surface Defects of Pharmaceutical Products in Aluminum-Plastic Blister Packages

Ensuring high product quality is of paramount importance in pharmaceutical drug manufacturing, as it is subject to rigorous regulatory practices. This study presents a research focused on the development of an on-line detection method and system for identifying surface defects in pharmaceutical products packaged in aluminum-plastic blisters. Firstly, the aluminum-plastic blister packages exhibit multi-scale features and inter-class indistinction. To address this, the deep semantic network with boundary refinement (DSN-BR) model is proposed, which leverages semantic segmentation domain knowledge, to accurately segment the defects in pixel level. Additionally, a specialized image acquisition module that minimizes the impact of ambient light is established, ensuring high-quality image capture. Finally, the image acquisition module, image detection module, and data management module are designed to construct a comprehensive online surface defect detection system. To validate the effectiveness of our approach, we employ a real dataset for instance verification on the implemented system. The experimental results substantiate the outstanding performance of the DSN-BR, achieving the mean intersection over union (MIoU) of 90.5%. Furthermore, the proposed system achieves an inference speed of up to 14.12 f/s, while attaining an F1-Score of 98.25%. These results demonstrate that the system meets the actual needs of the enterprise and provides theoretical and methodological support for intelligent inspection of product surface quality. By standardizing the control process of pharmaceutical manufacturing and improving the management capability of the manufacturing process, our approach holds significant market application prospects.

Optimizing crash box design to meet injury criteria: a protocol for accurate simulation and material selection

The design of a deformation element or crash box that meets a given injury criterion based on deceleration requires careful consideration of physical properties and space requirements. Variations in material yield stress or geometry can result in statistical variations in the injury criterion output. Optimizing the crash box to fulfil two different injury criteria and two different energy levels may require more space than initially specified. In this study, we propose a protocol where the crash box is collapsed, and force–displacement is fitted to an equation. This fit is carried out with just two simulations and compared to 30 possible scenarios, obtaining a maximum error of 38.9%. With this initial fit, the appropriate thickness and yield stress can be chosen to perform crashes with two energy levels and monitor four injury values. With the ideal yield stress and sheet metal thickness, we introduce real statistical distributions using Monte Carlo design to perform 200 simulations and obtain 400 injury values for each design proposal. This technique ensures that the design will meet injury requirements for any possible combination of thickness and yield stress accepted by quality inspection. If only one simulation is performed, all designs meet the requirements, but only the last proposed design decreased the average injury to 9.2 g with a standard deviation of 2.68 g and a maximum value of 14.4 g, which is less than the required 15 g. This technique minimizes the risk of finding combinations of yield stress and thickness that produce an undesirable injury criterion.

Compressive Strength Predictive Analysis using Spectroradiometer.

Abstract Rapid urbanization in emerging economies has prompted a focus on quality inspections of construction materials, particularly concrete. Standard tests assessing the mechanical strength of concrete involve breaking samples. Remote sensing presents an opportunity to map and monitor these materials, enabling the identification of properties like mechanical strength without causing damage. This non-destructive approach allows on-site inspection, swiftly and inexpensively, which is crucial during ongoing construction phases. Using a Field Spec® 3 spectroradiometer, spectroscopic analysis was performed to observe the spectral behavior of mechanical strengths in cement-based mortar materials based on spectral curves that were obtained from the reflected and absorbed electromagnetic radiation of the samples. This study is groundbreaking as there is no existing literature on utilizing this technique to differentiate compressive strengths of mortar cubes.