Search Results (345)

A crucial task in industrial quality control, especially in the food and agriculture sectors, is the quick and precise estimation of an object’s volume. This study combines cutting-edge machine learning and computer vision techniques to provide a comprehensive, non-destructive method for predicting orange volume. We created a reliable pipeline that employs top and side views of every orange to estimate four important dimensions using a calibrated marker. These dimensions are then fed into a machine learning model that has been fine-tuned. Our method uses a range of engineered features, such as complex surface-area-to-volume ratios and new shape-based descriptors, to go beyond basic geometric formulas. Based on a dataset of 150 unique oranges, we show that the Stacking Regressor performs significantly better than other single-model benchmarks, including the highly tuned LightGBM model, achieving an $R^2$ score of 0.971. Because of its reliance on basic physical characteristics, the method is extremely resilient to the inherent variability in fruit and may be used with a variety of produce types. Because it allows for the real-time calculation of density (mass over volume) for automated defect detection and quality grading, this solution is directly applicable to a factory sorting environment. Full article

The prevalence of infectious diseases is steadily increasing. If left untreated, they can lead to more serious health problems. Antibiotics currently available on the market are facing growing resistance, prompting the development of increasingly powerful antibacterial molecules. One alternative currently under investigation is the use of antibacterial peptides, whose mechanisms of action differ from those of conventional drugs. These peptides are produced naturally by all living organisms and can also be synthesized. However, as peptide chains become longer, synthesis and purification become increasingly complex and laborious. For decades, antimicrobial peptides have been synthesized on polymer supports using automated systems. Unfortunately, longer chains tend to fold more, preventing access of reagents within the cross-linked polymer network. Recombinant production of antimicrobial peptides has been achieved in various organisms called “cell factories,” allowing for more sustainable synthesis. Recently, microalgae have emerged as a promising and sustainable alternative for the production of antimicrobial peptides. They are inexpensive, easy to cultivate, and capable of producing biologically valuable molecules, offering a potential solution to antibiotic resistance. This work reviews the current state of these “cell factories” and examines the advantages and limitations of microalgae for the future of biopharmaceutical production. Full article

Foreign object debris (FOd) in rivet holes, machined holes, and fastener sites poses a critical risk to aerospace manufacturing, where current inspections rely on manual visual checks with flashlights and mirrors. These methods are slow, fatiguing, and prone to error. This work introduces HANNDI, a compact handheld inspection device that integrates controlled optics, illumination, and onboard deep learning for rapid and reliable inspection directly on the factory floor. The system performs focal sweeps, aligns and fuses the images into an all-in-focus representation, and applies a dual CNN pipeline based on the YOLO architecture: one network detects and localizes holes, while the other classifies debris. All training images were collected with the prototype, ensuring consistent geometry and lighting. On a withheld test set from a proprietary ≈3700 image dataset of aerospace assets, HANNDI achieved per-class precision and recall near 95%. An end-to-end demonstration on representative aircraft parts yielded an effective task time of 13.6 s per hole. To our knowledge, this is the first handheld automated optical inspection system that combines mechanical enforcement of imaging geometry, controlled illumination, and embedded CNN inference, providing a practical path toward robust factory floor deployment. Full article

As many German SMEs approach the end of their photovoltaic (PV) feed-in tariff period, the challenge of maintaining economic viability for these installations intensifies. This study addresses the integration of intermittent renewable energy sources (iRES) into production processes by proposing a method to identify and exploit industrial flexibility. A detailed case study of a German carpentry factory designed as a Net-Zero Energy Factory (NZEF) illustrates the approach, combining energy monitoring with blockchain technology to enhance transparency and traceability. Flexibility is exploited through a three-layer control system involving passive operator guidance, battery storage, and electric vehicle charging. The installation of a 40 kWh battery raises self-consumption from 50 to 70%, saving approximately EUR 4270 annually. However, this alone does not offset the investment. Blockchain-based transparency adds economic value by enabling premium pricing, potentially increasing revenue by up to 10%. This dual benefit supports the financial case for NZEFs. The framework is replicable and particularly relevant for low-automation industries, offering small and medium enterprises (SMEs) a viable pathway to decarbonization. The results align with the European Clean Industrial Deal, demonstrating how digitalization and industrial flexibility can reinforce competitiveness, sustainability, and digital trust in Europe’s transition to a resilient, low-carbon economy. Full article

This paper presents an integrated workflow for smart manufacturing, combining CAD modeling, Digital Twin synchronization, and automated visual inspection to detect defective fuses in industrial electrical panels. The proposed system connects Onshape CAD models with a collaborative robot via the ThingWorx IoT platform and leverages computer vision with HSV color segmentation for real-time fuse validation. A custom ROI-based calibration method is implemented to address visual variation across fuse types, and a 5-s time-window validation improves detection robustness under fluctuating conditions. The system achieves a 95% accuracy rate across two fuse box types, with confidence intervals reported for statistical significance. Experimental findings indicate an approximate 85% decrease in manual intervention duration. Because of its adaptability and extensibility, the design can be implemented in a variety of assembly processes and provides a foundation for smart factory systems that are more scalable and independent. Full article

With the rapid development of smart manufacturing, the optimization of real-time monitoring in operating procedures has become a crucial issue in modern industry. Traditional disassembly and assembly (D/A) work, relying on human experience and visual inspection, lacks immediacy and a quantitative basis, further affecting operating quality and efficiency. This study aims to develop a thin-film force sensor and an inertial measurement unit (IMU)-integrated wearable device for monitoring and analyzing operators’ behavioral characteristics during D/A tasks. First, by having operators wear self-made smart gloves and 17 IMU sensors, the work tables with three different heights are equipped with a mechatronics machine for the D/A experiment. Common D/A motions are designed into the experiment. Several subjects are invited to execute the standardized operating procedure, with upper limbs used to collect data on operators’ hand gestures and movements. Then, the measured data are applied to verify the performance measure functional best path of machine D/A. The results reveal that the system could effectively identify various D/A motions as well as observe operators’ force difference and motion mode, which, through the theory of performance indicator optimization and the verification of data analysis, could provide a reference for the best path planning, D/A sequence, and work table height design in the machine D/A process. The optimal workbench height for a standing operator is 5 to 10 cm above their elbow height. Performing assembly and disassembly tasks at this optimal height can help the operator save between 14.3933% and 35.2579% of physical effort. Such outcomes could aid in D/A behavior monitoring in industry, worker training, and operational optimization, as well as expand the application to instant feedback design for automation and smartization in a smart factory. Full article

Packing food into lunch boxes requires the correct portion to be selected. Food items such as fried chicken, eggs, and sausages are straightforward to manipulate when packing. In contrast, deformable objects like spaghetti can give challenges to lunch box packing due to their fragility and tendency to break apart, and the fluctuating weight of noodles. Furthermore, achieving the correct amount is crucial for lunch box packing. This research focuses on self-learned grasping by a robotic arm to enable the ability to autonomously predict and grasp deformable objects, specifically spaghetti, to achieve the correct amount within specified ranges. We utilize deep reinforcement learning as the core learning. We developed a custom environment and policy network along a real-world scenario that was simplified as in a food factory, incorporating multi-sensors to observe the environment and pipeline to work with a real robotic arm. Through the study and experiments, our results show that the robot can grasp the spaghetti within the desired ranges, although occasional failures were caused by the nature of the deformable object. Addressing the problem under varying environmental conditions such as data augmentation can partially help model prediction. The study highlights the potential of combining deep learning with robotic manipulation for complex deformable object tasks, offering insight for applications in automated food handling and other industries. Full article

Industry 4.0 has intensified the skills gap in industrial automation education, with graduates requiring extended on boarding periods and supplementary training investments averaging USD 11,500 per engineer. This paper introduces VisFactory, a multimedia learning system that extends the cognitive theory of multimedia learning by incorporating haptic feedback as a third processing channel alongside visual and auditory modalities. The system integrates a digital twin architecture with ultra-low latency synchronization (12.3 ms) across all sensory channels, a dynamic feedback orchestration algorithm that distributes information optimally across modalities, and a tripartite student model that continuously calibrates instruction parameters. We evaluated the system through a controlled experiment with 127 engineering students randomly assigned to experimental and control groups, with assessments conducted immediately and at three-month and six-month intervals. VisFactory significantly enhanced learning outcomes across multiple dimensions: 37% reduction in time to mastery (t(125) = 11.83, p < 0.001, d = 2.11), skill acquisition increased from 28% to 85% (${\eta }_p^2=0.54$), and 28% higher knowledge retention after six months. The multimodal approach demonstrated differential effectiveness across learning tasks, with haptic feedback providing the most significant benefit for procedural skills (52% error reduction) and visual–auditory integration proving most effective for conceptual understanding (49% improvement). The adaptive modality orchestration reduced cognitive load by 43% compared to unimodal interfaces. This research advances multimedia learning theory by validating tri-modal integration effectiveness and establishing quantitative benchmarks for sensory channel synchronization. The findings provide a theoretical framework and implementation guidelines for optimizing multimedia learning environments for complex skill development in technical domains. Full article

Serious games are an important tool for education and training that offers interactive and powerful experience. However, a significant challenge lays with adapting a game to meet the specific needs of each player in real-time. The present paper introduces a framework to guide the development of serious games using a phased approach. The framework introduces a level of standardization for the game elements, scenarios and data descriptions, mainly to support portability, interpretability and comprehension. This standardization is achieved through semantic annotation and it is utilized by digital twins to support self-adaptation. The proposed approach describes the game environment using ontologies and specific semantic structures, while it collects and semantically tags data during players’ interactions, including performance metrics, decision-making patterns and levels of engagement. This information is then used by a digital twin for automatically adjusting the game experience using a set of rules defined by a group of domain experts. The framework thus follows a hybrid approach, combing expert knowledge with automated adaptation actions being performed to ensure meaningful educational content delivery and flexible, real-time personalization. Real-time adaptation includes modifying the game’s level of difficulty, controlling the learning ability support and maintaining a suitable level of challenge for each player based on progress. The framework is demonstrated and evaluated using two real-word examples, the first targeting at supporting the education of children with syndromes that affect their learning abilities in close collaboration with speech therapists and the second being involved with training engineers in a poultry meat factory. Preliminary, small-scale experimentation indicated that this framework promotes personalized and dynamic user experience, with improved engagement through the adjustment of gaming elements in real-time to match each player’s unique profile, actions and achievements. Using a specially prepared questionnaire the framework was evaluated by domain experts that suggested high levels of usability and game adaptation. Comparison with similar approaches via a set of properties and features indicated the superiority of the proposed framework. Full article

This work presents the design and implementation of an automated, digital, and modular system to address a real-world industrial challenge: the automation and optimization of production schedules for Computer Numerical Control (CNC) machines in a factory in Portugal. The goal is to replicate and enhance the existing manual scheduling process by integrating multiple data sources and formulating a general Mixed-Integer Linear Programming (MILP) model with constraints. This model can be solved using MILP optimization methods to produce efficient scheduling solutions that minimize machine downtime, reduce tool change frequency, and lower operator workload. The proposed system is implemented using open-source Python abstraction interfaces (Python-MIP), employing state-of-the-art of MILP optimization solvers such as CBC and HiGHS for solution validation. The system is designed to accommodate a wide range of constraints and operational factors, which can be switched on or off as needed, thereby enhancing its flexibility and decision-support capabilities. Additionally, a user-friendly graphical application is developed to facilitate the input of specific scheduling data and constraints, enabling flexible and efficient formulation of diverse scheduling scenarios. The proposed system is validated through multiple case studies, demonstrating its effectiveness in optimizing industrial CNC scheduling tasks and providing a scalable, practical tool for real-world factory operations. Full article

As power electronic modules are increasingly required to provide improved heat dissipation, aluminum nitride (AlN) stands out against other ceramic materials. At the same time, more cost-efficient production of customized products demands shorter development cycles and innovative manufacturing processes. Conventional process chains in power electronics are usually long and inflexible; thus, innovative ways to reduce process steps and faster prototyping are needed. Therefore, this study investigates the usage of additive manufacturing technology—laser-based powder bed fusion of metal powder (PBF-LB/M)—namely copper (Cu), on AlN substrates for power electronic applications. It is found that specific electrical conductivity values can be achieved up to 31 MS/m, and adhesion measured by shear testing reaches 15 MPa. In reliability testing, the newly produced samples exhibit a 25% decrease in adhesion after 250 cycles, which is comparatively moderate. This study shows the feasibility of PBF-LB/M of Cu powder on AlN, emphasizing its strengths and highlighting remaining weaknesses. Full article

Quadratic Unconstrained Binary Optimization (QUBO) is the problem of finding binary variable assignments that minimize a given quadratic objective function. Many combinatorial optimization problems can be transformed into equivalent QUBO formulations, and significant research efforts have been devoted to developing efficient QUBO solvers. This paper presents a method for converting a real-world, industrial-scale scheduling problem arising in the heat treatment process of metal parts into an equivalent QUBO formulation. In current operations, two experienced operators manually spend about two hours each day to create a 24 h schedule for two furnaces, considering various constraints and processing priorities. We have developed a C++-based scheduling system that automatically converts this problem into a QUBO instance and solves it using QUBO++, a high-performance toolkit we have recently developed and released. The experimental results on actual scheduling instances show that QUBO++ consistently produces high-quality solutions in a short time, leveraging the computational merits of the QUBO formulation. The system reduces the labor equivalent to two hours of expert work per day, indicating that automated scheduling via QUBO++ is a promising approach for improving efficiency and accuracy in factory operations. Full article

Significant improvements in the forest-based industrial sector are expected due to increased digitalization; however, examples of practical implementations remain limited. This study explores the use of an automated algorithm to estimate truckload volumes based on 3D point cloud data acquired using two different LiDAR scanning platforms. This research compares the performance of a professional mobile laser scanning (MLS GeoSLAM) platform and a smartphone-based iPhone LiDAR system. A total of 48 truckloads were measured using a combination of manual, factory-based, and digital approaches. Accuracy was evaluated using standard error metrics, including the mean absolute error (MAE) and root mean square error (RMSE), with manual or factory references used as benchmarks. The results showed a strong correlation and no significant differences between the algorithmic and manual measurements when using the MLS platform (MAE = 2.06 m³; RMSE = 2.46 m³). For the iPhone platform, the results showed higher deviations and significant overestimation compared to the factory reference (MAE = 3.29 m³; RMSE = 3.60 m³). Despite these differences, the iPhone platform offers real-time acquisition and low-cost deployment. These findings highlight the trade-offs between precision and operational efficiency and support the adoption of automated measurement tools in timber supply chains. Full article

Microalgae, with their unparalleled capabilities for sunlight-driven growth, CO₂ fixation, and synthesis of diverse high-value compounds, represent sustainable cell factories for a circular bioeconomy. However, industrial deployment has been hindered by biological constraints and the inadequacy of conventional genetic tools. The advent of CRISPR-Cas systems initially provided precise gene editing via targeted DNA cleavage. This review argues that the true transformative potential lies in moving decisively beyond cutting to harness CRISPR as a versatile synthetic biology “Swiss Army Knife”. We synthesize the rapid evolution of CRISPR-derived tools—including transcriptional modulators (CRISPRa/i), epigenome editors, base/prime editors, multiplexed systems, and biosensor-integrated logic gates—and their revolutionary applications in microalgal engineering. These tools enable tunable gene expression, stable epigenetic reprogramming, DSB-free nucleotide-level precision editing, coordinated rewiring of complex metabolic networks, and dynamic, autonomous control in response to environmental cues. We critically evaluate their deployment to enhance photosynthesis, boost lipid/biofuel production, engineer high-value compound pathways (carotenoids, PUFAs, proteins), improve stress resilience, and optimize carbon utilization. Persistent challenges—species-specific tool optimization, delivery efficiency, genetic stability, scalability, and biosafety—are analyzed, alongside emerging solutions and future directions integrating AI, automation, and multi-omics. The strategic integration of this CRISPR toolkit unlocks the potential to engineer robust, high-productivity microalgal cell factories, finally realizing their promise as sustainable platforms for next-generation biomanufacturing. Full article

In the industrial production of polytetrafluoroethylene (PTFE), accurately recognizing the liquid state within the coagulation vessel is critical to achieving better product quality and higher production efficiency. However, the complex and subtle changes in the coagulation process pose significant challenges for traditional sensing methods, calling for more reliable visual approaches that can handle varying scales and dynamic state changes. This study proposes a multi-branch deep learning framework for classifying the liquid state of PTFE emulsions based on high-resolution images captured in real-world factory conditions. The framework incorporates multi-scale feature extraction through a three-branch network and introduces a frequency–channel attention module to enhance feature discrimination. To address optimization challenges across branches, contrastive learning is employed for deep supervision, encouraging consistent and informative feature learning. The experimental results show that the proposed method significantly improves classification accuracy, achieving a mean F1-score of 94.3% across key production states. This work demonstrates the potential of deep learning-based visual classification methods for improving automation and reliability in industrial production. Full article