Search Results (69)

Warpage is among the most prevalent defects affecting injection molded parts. In this study, we aimed to develop methods to minimize warpage through mold design. Common strategies include matching the cavity geometry to the intended shape of the part, adjusting cavity dimensions to offset material shrinkage, and optimizing the cooling system and critical injection molding parameters. These optimization methods can offer significant improvements, but recently introduced methods that optimize the molded part and mold cavity shape result in higher levels of warpage reduction. In these methods, optimization of the shape of the molded part is achieved by shaping it in the opposite direction of warpage—a method known as inverse contouring. Inverse contouring of molded parts is a design technique in which mold cavities are intentionally modified to incorporate compensatory geometric deviations in regions anticipated to exhibit significant warpage. The final result after molded part ejection and warpage is a significant reduction in deviations between the warped and reference molded part geometries. In this study, a two-step approach for minimizing warpage was used: the first step was optimizing the most significant injection molding parameters, and the second was inverse contouring. In the first step, Response Surface Methodology (RSM) and Autodesk Moldflow Insight 2023 simulations were used to optimize molded part warpage based on three processing parameters: melt temperature, target mold temperature, and coolant temperature. For improved accuracy, a Computer-Aided Design (CAD) model of the warped molded part was exported into ZEISS Inspect 2023 software and aligned with the reference CAD geometry of the molded part. The maximal warpage value after the initial simulation was 1.85 mm based on Autodesk Moldflow Insight simulations and 1.67 mm based on ZEISS Inspect alignment. After RSM optimization, the maximal warpage was 0.73 mm. In the second step, inverse contouring was performed on the molded part, utilizing the initial injection molding simulation results to further reduce warpage. In this step, the CAD model of the redesigned, inverse-contoured molded part was imported into Moldflow Insight to conduct a second iteration of the injection molding simulation. The simulation results were exported into ZEISS Inspect software for a final analysis and comparison with the reference CAD model. The warpage values after inverse contouring were reduced within the range of ±0.30 mm, which represents a significant decrease in warpage of approximately 82%. Both steps are presented in a case study on an injection molded part made of polybutylene terephthalate (PBT) with 30% glass fiber (GF). Full article

Industrial Vision Inspection Systems (VISs) often struggle to adapt to increasing variability of modern manufacturing due to the inherent rigidity of their hardware architectures. Although the Reconfigurable Manufacturing System (RMS) paradigm was introduced in the early 2000s to overcome these limitations, designing such reconfigurable machines remains a complex, expert-dependent, and time-consuming task. This is primarily due to the lack of structured methodologies and the reliance on trial-and-error processes. In this context, this study proposes a novel theoretical framework to facilitate the design of fully reconfigurable handling systems for VISs, with a particular focus on fixture design. The framework is grounded in Model-Based Definition (MBD), embedding semantic information directly into the 3D CAD models of the inspected product. As an additional contribution, a general hardware architecture for the inspection of axisymmetric components is presented. This architecture integrates an anthropomorphic robotic arm, Numerically Controlled (NC) modules, and adaptable software and hardware components to enable automated, software-driven reconfiguration. The proposed framework and architecture were applied in an industrial case study conducted in collaboration with a leading automotive half-shaft manufacturer. The resulting system, implemented across seven automated cells, successfully inspected over 200 part types from 12 part families and detected more than 60 defect types, with a cycle below 30 s per part. Full article

The objective of this study was to evaluate the effect of morpholine on saliva-contaminated resin-based composite (RBC)-CAD/CAM material repaired with resin composite. Fifty RBC-CAD/CAM materials were fabricated and assigned to five groups and surface-treated with saliva, phosphoric acid (PHR), morpholine (MRL), and a universal adhesive agent (Scotchbond universal plus, SCP) based on the following techniques: group 1, saliva; group 2, SCP; group 3, saliva + SCP; group 4, saliva + PHR + SCP; and group 5, saliva + MRL + SCP. An ultradent model was placed on the specimen center, and then the resin composite was pressed and light-cured for 20 s. A mechanical testing device was used to evaluate the samples’ shear bond strength (SBS) scores. The debonded specimen areas were inspected under a stereomicroscope to identify the failure mechanisms. The data were analyzed using one-way ANOVA, and the significance level (p < 0.05) was set with Tukey’s test. The highest SBS values were in groups 2, 4 and 5, with values of 21.43 ± 1.93, 20.93 ± 1.46, and 22.02 ± 1.77 MPa, respectively. However, they were not statistically different (p > 0.05). Group 1 had the lowest SBS value by a significant amount (1.88 ± 1.01 MPa). All specimens in group 1 showed adhesive failures. Moreover, groups 2–5 found cohesive and mixed failures. In conclusion, morpholine and phosphoric acid effectively enhance bond strength. These results indicate that alternative surface modifications with morpholine for saliva-contaminated RBC-CAD/CAM materials can significantly improve the outcome. Full article

In this paper, we are looking for the answer to the following question: what geometric deviations do polymer gears made by 3D printing have from the theoretical geometry? From a practical point of view, the question is whether the currently installed injection-molded gear can be replaced by a 3D-printed gear. Thus, the measurements are also carried out on the sample gear and the comparison is made with this data as well. Knowing the data of the existing gear wheel, the CAD model was created, and based on this, samples of the gear were printed using various 3D printing machines. The printed gears were then subjected to geometrical analysis. During the inspection, we performed the measurement of the chordal thickness of the gear wheel using a gear tool caliper, instead of pin measurement and span measurement using a special micrometer, and 3D scanning and analysis. A surface roughness measurement was carried out as well. By conducting measurements on the injection-molded and 3D-printed samples, this research seeks to evaluate the reliability and limitations of the 3D-printed gears, providing insights into their industrial use. This study aims to determine whether 3D printing technologies can produce gears with sufficient accuracy and surface quality for practical applications. Based on the conducted analysis, general conclusions were drawn regarding the potential applicability of the 3D-printed gears. The experimental results indicate notable differences in dimensional accuracy between gears manufactured using Fused Deposition Modeling (FDM) and Selective Laser Sintering (SLS). In terms of chordal thickness measurements, FDM gears exhibited a mean relative error of 1.96 mm, whereas SLS gears showed a significantly higher average deviation of 5.64 mm. For the pin measurement, the relative error averaged 0.193 mm in the case of FDM gears, compared to 0.616 mm for SLS gears. Similarly, the span over four teeth measurements resulted in an average deviation of 0.153 mm for FDM gears, while SLS gears demonstrated a markedly higher mean error of 0.773 mm. With regard to surface roughness, it can be concluded that SLS-manufactured gears exhibit superior performance compared to FDM gears, with an average Ra value of 2.65 µm versus 9.28 µm, although their surface quality remains inferior to that of the injection-molded gear. In light of the higher relative errors observed in SLS gears compared to FDM gears, the dimensions of the theoretical model should be refined to improve the manufacturing accuracy of SLS-produced gears. Full article

High-precision surface defect detection in manufacturing often relies on laser triangulation profilometric sensors for detailed surface measurements, providing detailed and accurate surface measurements over a line. Accurate motion between the sensor and workpiece, usually managed by robotic systems, is critical for maintaining optimal distance and orientation. This paper introduces a novel Reinforcement Learning (RL) approach to optimize inspection trajectories for profilometric sensors based on the boustrophedon scanning method. The RL model dynamically adjusts sensor position and tilt to ensure consistent profile distribution and high-quality scanning. We use a simulated environment replicating real-world conditions, including sensor noise and surface irregularities, to plan trajectories offline using CAD models. Key contributions include designing a state space, action space, and reward function tailored for profilometric sensor inspection. The Proximal Policy Optimization (PPO) algorithm trains the RL agent to optimize these trajectories effectively. Validation involves testing the model on various parts in simulation and performing real-world inspection with a UR3e robotic arm, demonstrating the approach’s practicality and effectiveness. Full article

The early detection of cancerous lesions is a challenging task given the cancer biology and the variability in tissue characteristics, thus rendering medical image analysis tedious and time-inefficient. In the past, conventional computer-aided diagnosis (CAD) and detection methods have heavily relied on the visual inspection of medical images, which is ineffective, particularly for large and visible cancerous lesions in such images. Additionally, conventional methods face challenges in analyzing objects in large images due to overlapping/intersecting objects and the inability to resolve their image boundaries/edges. Nevertheless, the early detection of breast cancer lesions is a key determinant for diagnosis and treatment. In this study, we present a deep learning-based technique for breast cancer lesion detection, namely blob detection, which automatically detects hidden and inaccessible cancerous lesions in unsupervised human breast histology images. Initially, this approach prepares and pre-processes data through various augmentation methods to increase the dataset size. Secondly, a stain normalization technique is applied to the augmented images to separate nucleus features from tissue structures. Thirdly, morphology operation techniques, namely erosion, dilation, opening, and a distance transform, are used to enhance the images by highlighting foreground and background pixels while removing overlapping regions from the highlighted nucleus objects in the image. Subsequently, image segmentation is handled via the connected components method, which groups highlighted pixel components with similar intensity values and assigns them to their relevant labeled components (binary masks). These binary masks are then used in the active contours method for further segmentation by highlighting the boundaries/edges of ROIs. Finally, a deep learning recurrent neural network (RNN) model automatically detects and extracts cancerous lesions and their edges from the histology images via the blob detection method. This proposed approach utilizes the capabilities of both the connected components method and the active contours method to resolve the limitations of blob detection. This detection method is evaluated on 27,249 unsupervised, augmented human breast cancer histology dataset images, and it shows a significant evaluation result in the form of a $98.82\%$ F1 accuracy score. Full article

To improve the accuracy and completeness of three-dimensional sculpture reconstruction, this study proposes a global–local two-step scanning method for industrial robot-based scanning. First, a global model is generated through stepped rotary scanning based on the object’s dimensions. Subsequently, local viewpoint planning is conducted to refine regions that were incompletely captured in the initial step, with a genetic algorithm optimizing the scanning paths to enhance efficiency. The local models are then aligned and fused with the global model to produce the final 3D reconstruction. Comparative experiments on sculptures made of different materials were conducted to validate the effectiveness of the proposed method. Compared with CAD-slicing and surface-partitioning methods, the proposed approach achieved superior model completeness, a scanning accuracy of 0.26 mm, a standard deviation of 0.31 mm, and a total scanning time of 152 s. The results indicate that the proposed method enhances reconstruction integrity and overall quality while maintaining high efficiency, making it a viable approach for high-precision 3D surface inspection tasks. Full article

An estimated 60,000 people die annually from skin cancer, predominantly melanoma. The diagnosis of skin lesions primarily relies on visual inspection, but around half of lesions pose diagnostic challenges, often necessitating a biopsy. Non-invasive detection methods like Computer-Aided Diagnosis (CAD) using Deep Learning (DL) are becoming more prominent. This study focuses on the use of multispectral (MS) imaging to improve skin lesion classification of DL models. We trained two convolutional neural networks (CNNs)—a simple CNN with six two-dimensional (2D) convolutional layers and a custom VGG-16 model with three-dimensional (3D) convolutional layers—using a dataset of MS images. The dataset included spectral cubes from 327 nevi, 112 melanomas, and 70 basal cell carcinomas (BCCs). We compared the performance of the CNNs trained with full spectral cubes versus using only three spectral bands closest to RGB wavelengths. The custom VGG-16 model achieved a classification accuracy of 71% with full spectral cubes and 45% with RGB-simulated images. The simple CNN achieved an accuracy of 83% with full spectral cubes and 36% with RGB-simulated images, demonstrating the added value of spectral information. These results confirm that MS imaging provides complementary information beyond traditional RGB images, contributing to improved classification performance. Although the dataset size remains a limitation, the findings indicate that MS imaging has significant potential for enhancing skin lesion diagnosis, paving the way for further advancements as larger datasets become available. Full article

The purpose of this research was to evaluate the quality of small features fabricated by the fused filament fabrication (FFF) method. The samples containing circular and square cross-sections through holes with different dimensions, lengths, and orientation angles were printed from ABS (acrylonitrile butadiene styrene) filament. The adopted optical inspection method allowed us to conduct observations of individual features and their measurements. The image processing software was used to determine the accuracy of the dimensions and shape of different cross-sections. Feret’s diameters were used for the evaluation of shape accuracy by comparing them with theoretical dimensions assumed in a 3D CAD model. Considering the relationship between the real and theoretical dimensions of different features, general empirical equations for predicting the equivalent dimensions were developed. The proposed method of the quality evaluation of small features can be easily implemented and widely applied to other features, especially internal holes with different cross-sections made using various additive manufacturing methods. Full article

The increasing adoption of 2D/3D laser line sensors in industrial and research applications necessitates accurate and efficient simulation tools for tasks such as surface inspection, dimensional verification, and quality control. This paper presents a novel algorithm developed in MATLAB for simulating the measurements of any 2D/3D laser line sensor on STL CAD models. The algorithm uses a modified fast-ray triangular intersection method, addressing challenges such as overlapping triangles in assembly models and incorporating sensor resolution to ensure realistic simulations. Quantitative analysis shows a significant reduction in computation time, enhancing the practical utility of the algorithm. The simulation results exhibit a mean deviation of 0.42 mm when compared to real-world measurements. Notably, the algorithm effectively handles complex geometric features, such as holes and grooves, and offers flexibility in generating point cloud data in both local and global coordinate systems. This work not only reduces the need for physical prototyping, thereby contributing to sustainability, but also supports AI training by generating accurate synthetic data. Future work should aim to further optimize the simulation speed and explore noise modeling to enhance the realism of simulated measurements. Full article

Segmentation of nuclei in histology images is key in analyzing and quantifying morphology changes of nuclei features and tissue structures. Conventional diagnosis, segmenting, and detection methods have relied heavily on the manual-visual inspection of histology images. These methods are only effective on clearly visible cancerous lesions on histology images thus limited in their performance due to the complexity of tissue structures in histology images. Hence, early detection of breast cancer is key for treatment and profits from Computer-Aided-Diagnostic (CAD) systems introduced to efficiently and automatically segment and detect nuclei cells in pathology. This paper proposes, an automatic watershed segmentation method of cancerous lesions in unsupervised human breast histology images. Firstly, this approach pre-processes data through various augmentation methods to increase the size of dataset images, then a stain normalization technique is applied to these augmented images to isolate nuclei features from tissue structures. Secondly, data enhancement techniques namely; erosion, dilation, and distance transform are used to highlight foreground and background pixels while removing unwanted regions from the highlighted nuclei objects on the image. Consequently, the connected components method groups these highlighted pixel components with similar intensity values and, assigns them to their relevant labeled component binary mask. Once all binary masked groups have been determined, a deep-learning recurrent neural network from the Keras architecture uses this information to automatically segment nuclei objects with cancerous lesions and their edges on the image via watershed filling. This segmentation method is evaluated on an unsupervised, augmented human breast cancer histology dataset of 11,151 images. This proposed method produced a significant evaluation result of $98\%$ F1-accuracy score. Full article

Background: Intraoral scanning technology has opened new perspectives in dental practice, and combined with CAD/CAM technology, contributes significantly to fabricating high-quality prosthetic restorations. Our in vitro study aims to assess the accuracy of digital models obtained from one laboratory and two less commonly used intraoral scanners by conducting 3D measurements on the digital models obtained. Methods: An articulated simulator cast was used. Forty-eight scans were performed before and after tooth preparation with each scanner. The Zeiss Inspect software (Version: 2023.3.0.969) was used for measurements in sagittal and transversal planes. The obtained values were compared to reference values resulting from manual measurements. Results: Digital impressions provided discrepancies compared to the reference model. The lowest differences at the A2-L2 (the diagonal dimension of the models from the distal fossa of the second right maxillary molar and the maximum oral convexity of the artificial gingiva at the first left premolar) and the A1-B1 (transversal dimension of the model in the posterior area, from the right second molar’s occlusal central fossa to the left second molar central fossa) distances were obtained for the upper models, and at the a1-b1 distance for all the lower models, except the non-prepared models scanned with the intraoral scanners (the discrepancies were not statistically significant). The discrepancies increased with the distance from the starting point of the scan. Conclusion: The number and position of prepared teeth can influence the accuracy of the scans. Distortions can appear in the case of multiple preparations. The scanning protocol and calibration must be optimized for the highest accuracy. Furthermore, in vivo studies are necessary to evaluate the clinical applicability of these findings. Full article

Automated industrial Visual Inspection Systems (VIS) are predominantly designed for specific use cases, resulting in constrained adaptability, high setup requirements, substantial capital investments, and significant knowledge barriers. This paper explores the business potential of recent alternative architectures proposed in the literature for the visual inspection of individual products or complex assemblies within highly variable production environments, utilizing next-generation VIS. These advanced VIS exhibit significant technical (hardware and software) enhancements, such as increased flexibility, reconfigurability, Computer Aided Design (CAD)-based integration, self-X capabilities, and autonomy, as well as economic improvements, including cost-effectiveness, non-invasiveness, and plug-and-produce capabilities. The new trends in VIS have the potential to revolutionize business models by enabling as-a-service approaches and facilitating a paradigm shift towards more sustainable manufacturing and human-centric practices. We extend the discussion to examine how these technological innovations, which reduce the need for extensive coding skills and lengthy reconfiguration activities for operators, can be implemented as a shared resource within a circular lifecycle. This analysis includes detailing the underlying business model that supports shared utilization among different stakeholders, promoting a circular economy in manufacturing by leveraging the capabilities of next-generation VIS. Such an approach not only enhances the sustainability of manufacturing processes but also democratizes access to state-of-the-art inspection technologies, thereby expanding the possibilities for autonomous manufacturing ecosystems. Full article

In recent years, CAD/CAM technology has allowed indirect ceramic restorations to become a part of everyday chairside clinical practice. Therefore, the impact of different cavity-preparation designs on the fracture resistance of CAD/CAM fabricated ceramics was assessed in this study. Three designs of cuspal covering (none, palatal, and entire) and two widths of the occlusal isthmus (75% and 100% of the intercuspal distance) were used for the preparation of inlays and onlays to form six groups (n = 10/group). Moreover, thermomechanical cyclic loading was applied to every tooth under a chewing simulator. A universal testing machine was used to measure each group’s fracture resistance. The tested specimens were inspected for any signs of fractures and cracks to categorize failure patterns. Thereby, the values of fracture strength showed that there were statistically nonsignificant differences between the tested groups (p < 0.05). However, a significant difference (p = 0.01) was found between group 1 (inlays) (1950 ± 405) and group 6 (onlays) (3900 ± 770). Type III or type IV fracture modes were seen in the majority of the specimens. In conclusion, inlays and onlays made of zirconia using CAD/CAM technology were deemed reliable for restoring premolars, irrespective of the cavity-preparation design, except for inlays with a 75% intercuspal distance. Full article

This study aimed to analyze the effect of the height of the proximal axial wall of the prepared tooth and the distance between the adjacent tooth and the prepared tooth on the scan accuracy of intraoral scanners. Ten working casts with maxillary first molars prepared to receive zirconia crowns were randomly obtained from a dental clinic. Each of the 10 casts was scanned using two intraoral scanners (i700; MEDIT and CS3600; Carestream; computer-aided design [CAD] test model, CTM; N = 15 per working cast) 15 times per scanner. Individual dies of the prepared teeth were fabricated, and high-precision scan data were acquired using a laboratory scanner (CAD reference model, CRM; N = 1). CTMs were aligned relative to the prepared tooth of CRMs by using three-dimensional inspection software (Ver 2018.1.0; Control X; 3D Systems). Data were statistically analyzed using an independent t-test and one-way analysis of variance for between-group comparisons (α = 0.05). The inaccuracy in the proximal regions (mesial or distal) of the prepared tooth was higher than that in the buccal and lingual regions (p < 0.05). The scan accuracy was not correlated with the variables when the distance between the adjacent tooth and the prepared tooth was ≥2.0 mm and the height of the proximal axial wall of the prepared tooth was <3.0 mm (p > 0.05). Therefore, an excellent scan accuracy can be obtained using an intraoral scanner when the distance between the adjacent tooth and the prepared tooth is ≥2.0 mm and the proximal axial wall height of the prepared tooth is <3.0 mm. Full article