Search Results (100)

Small and medium-sized enterprises (SMEs) are expected to contribute to sustainable manufacturing, yet they often lack the resources and capabilities needed to adopt advanced automation in a structured and scalable manner. While lean robotics have been widely studied, there is still limited empirical evidence on how their integration can be systematically designed to improve sustainability-oriented performance in SME contexts. This paper examines how a scalable lean robotics system can be conceived and implemented to enhance productivity and resource efficiency in an SME packaging process. We develop a lean robotics design approach that jointly considers lean principles, collaborative industrial robotics, and Industrial Internet of Things (IIoT) monitoring. The approach is applied in a real-world case study of a “Fold Station” robotic cell, where stone paper sheets are destacked, glued, and formed into cylindrical plant protectors. Key performance indicators related to cycle time, material utilization, process stability, and manual workload are measured before and after implementation. The results show a three- to four-fold reduction in preparation time per unit, more efficient use of stone paper and adhesive, and a decrease in repetitive manual handling, thereby contributing to both economic and environmental sustainability. TRIZ (Teoriya Resheniya Izobretatelskikh Zadach, Theory of Inventive Problem Solving) is used to structure the resolution of design contradictions that arise when embedding lean principles into the robotic system and to support its scalable adaptation to different production scenarios. This study advances the understanding of lean robotics for sustainable SME production and derives practical guidelines for designing scalable, resource-efficient robotic cells. Full article

This study investigates the common assumption that segmental inertial parameters remain constant during manual lifting using a model-based experimental approach. The primary objective was to evaluate the variability in these parameters and the subsequent effects on biomechanical calculations. The research was conducted with 20 participants (10 females and 10 males) who performed lifting tasks in the two-dimensional sagittal plane under three distinct load conditions: 2.5 kg, 5.0 kg, and 7.5 kg. Angular variations of the hand, arm, and leg joints were recorded using video-based image processing techniques. These kinematic data, integrated with anthropometric measurements, were incorporated into Newton–Euler-based equations of motion to determine joint reaction forces and net joint moments. During the initial forward dynamics stage, the solvability of the problem was tested using constant mass ratios from the established literature. In the following inverse dynamics stage, genetic algorithms were utilized to overcome solution diversity and identify the variable inertial parameters responsible for the observed motion. The results indicate that changes in segment moments of inertia reached 18–37%, leading to variations of 0–19% in net joint moments. These findings highlight the critical necessity of incorporating dynamic inertial parameters into accurate biomechanical moment calculations for manual materials handling. Full article

This study explores the self-perception of small-scale artisanal food enterprises and their potential for food upcycling as a sustainable strategy to reduce food waste. The primary aim is to identify the characteristics of artisanal food production and to assess innovative uses for waste materials. Semi-structured interviews were conducted with eight enterprises from various sectors (bakeries, breweries, ice cream manufacturers, and dairies) to gain insights into the artisanal food sector and their handling of residual materials. Findings reveal a strong reliance of artisanal food businesses on traditional manufacturing methods and manual labor, resulting in high-quality, unique products. Moreover, there is notable potential for food upcycling, even though most of the enterprises already try to use most of their side streams in different ways. This study indicates that through a combination of tradition and innovation, artisanal food production can contribute to sustainability. The results provide valuable insights for practitioners and policymakers aiming to develop a definition of the food craft sector. Further research is recommended to quantify the economic and environmental benefits of upcycling strategies in artisanal contexts as well as to establish a definition of the food craft. Full article

Forestry, nursery, and planting tasks involve repetitive trunk flexion, squatting, and kneeling, as well as manual handling, increasing musculoskeletal load, and the need for mobility-related safety measures. Passive exoskeletons could mitigate postural exposure and reduce the overall body workload. We conducted a preliminary study (n = 14) to test the feasibility of a protocol and estimated model- and task-specific trends during standardized simulated nursery activities in a laboratory setting. Participants simulated planting and seeding tasks (loads of 0.5–2 kg) and material handling and preparation tasks (loads of 5–15 kg) without an exoskeleton (No-EXO) and with three passive models (EXO 1–EXO 3). EXO 3 was excluded from the planting tasks for feasibility reasons. Whole-body kinematics were recorded using an IMU-based motion capture system and converted into time-based ergonomic exposure outcomes (OWAS and RULA). Physiological load was monitored via heart-rate (HR) measurements. Compared to the No-EXO condition, exoskeleton use shifted posture exposure towards lower-risk categories. The largest improvements were observed with EXO 2 and EXO 3 during material handling (OWAS: −18%/−20%; RULA action-level reduction: −25%/−39%) and with EXO 2 during planting/seeding (OWAS: −15%; RULA: −26%). HR_max did not increase across tasks or conditions and HR tended not to rise with higher workload when exoskeletons were used. Overall, the results suggest positive ergonomic and workload trends related to the model and tasks. Field validation on uneven terrain with full personal protective equipment and harness integration is needed to confirm usability and support and to define implementation requirements (fit, compatibility with PPE, and safe-use conditions). Full article

Lumbar shear forces are increasingly recognized as critical contributors to lower-back injury risk, yet most ergonomic assessment tools—most notably the Revised NIOSH Lifting Equation (RNLE)—do not directly estimate shear loading. This study develops and evaluates a family of linear mixed-effects regression models that statistically predict L4/L5 lumbar shear force exposure using traditional NIOSH lifting parameters combined with posture descriptors extracted from digital human models. A harmonized dataset of 106 peak-shear lifting postures was compiled from five controlled laboratory studies, with lumbar shear forces obtained from validated biomechanical simulations implemented in the Siemens JACK (Siemens software, Plano, TX, USA) platform. Twelve model formulations were examined, varying in fixed-effect structure and hierarchical random effects, to quantify how load magnitude, hand location, sex, and joint posture relate to simulated task-level anterior–posterior shear exposure at the lumbar spine. Across all models, load magnitude and horizontal reach emerged as the strongest and most stable predictors of shear exposure, reflecting their direct mechanical influence on anterior spinal loading. Hip and knee flexion provided substantial additional explanatory power, highlighting the role of whole-body posture strategy in modulating shear demand. Upper-limb posture and coupling quality exhibited minimal or inconsistent effects once load geometry and lower-body posture were accounted for. Random-effects analyses demonstrated that meaningful variability arises from individual movement strategies and task conditions, underscoring the necessity of mixed-effects modeling for representing hierarchical structure in lifting data. Parsimonious models incorporating subject-level random intercepts produced the most stable and interpretable coefficients while maintaining strong goodness-of-fit. Overall, the findings extend the NIOSH framework by identifying posture-dependent determinants of lumbar shear exposure and by demonstrating that simulated shear loading can be reliably predicted using ergonomically accessible task descriptors. The proposed models are intended as statistical predictors of task-level shear exposure that complement—rather than replace—comprehensive biomechanical simulations. This work provides a quantitative foundation for integrating shear-aware metrics into ergonomic risk assessment practices, supporting posture-informed screening of manual material-handling tasks in field and sensor-based applications. Full article

Cost code verification in state-funded construction projects remains a labor-intensive and error-prone task, particularly given the structural heterogeneity of project estimates and the prevalence of malformed codes, inconsistent units of measurement (UoMs), and locally modified price components. This study evaluates a deterministic GPT-based assistant designed to automate Vietnam’s regulatory verification. The assistant was developed and iteratively refined across four Action Research cycles. Also, the system enforces strict rule sequencing and dataset grounding via Python-governed computations. Rather than relying on probabilistic or semantic reasoning, the system performs strictly deterministic checks on code validity, UoM alignment, and unit price conformity in material (MTR), labor (LBR), and machinery (MCR), given the provincial unit price books (UPBs). Deterministic equality is evaluated either on raw numerical values or on values transformed through explicitly declared, rule-governed operations, preserving auditability without introducing tolerance-based or inferential reasoning. A dedicated exact-match mechanism, which is activated only when a code is invalid, enables the recovery of typographical errors only when a project item’s full price vector well matches a normative entry. Using twenty real construction estimates (16,100 rows) and twelve controlled error-injection cases, the study demonstrates that the assistant executes verification steps with high reliability across diverse spreadsheet structures, avoiding ambiguity and maintaining full auditability. Deterministic extraction and normalization routines facilitate robust handling of displaced headers, merged cells, and non-standard labeling, while structured reporting provides line-by-line traceability aligned with professional verification workflows. Practitioner feedback confirms that the system reduces manual tracing effort, improves evaluation consistency, and supports documentation compliance with human judgment. This research contributes a framework for large language model (LLM)-orchestrated verification, demonstrating how Action Research can align AI tools with domain expectations. Furthermore, it establishes a methodology for deploying LLMs in safety-critical and regulation-driven environments. Limitations—including narrow diagnostic scope, unlisted quotation exclusion, single-province UPB compliance, and sensitivity to extreme spreadsheet irregularities—define directions for future deterministic extensions. Overall, the findings illustrate how tightly constrained LLM configurations can augment, rather than replace, professional cost verification practices in public-sector construction. Full article

High-precision calibration of inertial measurement units for automotive safety systems combines fixed automated chamber cycles with semi-manual loading, alignment, and transfer. Motion waste and ergonomic constraints can therefore dominate throughput and cycle time stability. This study redesigns a production calibration workstation using time-and-motion analysis, operator observation, and structured root-cause analysis based on the Ishikawa diagram and the five whys. Three interventions were implemented and validated with pre- and post-measurements: bundled handling that consolidates full-set transfers and reduces non-value-adding motions; a fixture and material handling redesign with a manual lifting aid to reduce physical load and enable reliable single-operator operation; and a modular workstation layout that supports the phased addition of chambers. Total cycle time decreased from 4475 s to 1230 s, a 72 percent reduction, and weekly output rose from 800 to 4500 units without additional staffing or significant automation investment. Overall equipment efficiency improved from 75.3 percent to 85.2 percent, while the quality rate remained at 98.8 percent. Full article

Objectives: The aim of this study was to present and describe a digital workflow integrating Digital Smile Design (DSD) with computer-aided design/computer-aided manufacturing (CAD/CAM) and additive manufacturing technologies for the fabrication of dental preparation guides, focusing on workflow feasibility, design reproducibility, and clinical handling. Materials and Methods: A digital workflow was implemented using intraoral scanning and Exocad DentalCAD 3.1 Elefsina software to design dental preparation guides based on digitally planned restorations. Preparation margins, insertion paths, and minimal material thickness were defined virtually. The guides were fabricated using both subtractive (PMMA milling) and additive (stereolithographic-based 3D printing) manufacturing techniques. Post-processing included chemical cleaning, support removal, additional light curing, and manual finishing. The evaluation was qualitative and descriptive, based on visual inspection, workflow performance, and guide adaptation to printed models. Results: The proposed digital workflow was associated with consistent fabrication of preparation guides and predictable transfer of the virtual design to the manufactured guides. Digital planning facilitated clear visualization of preparation margins and insertion axes, supporting controlled and minimally invasive tooth preparation. The workflow demonstrated good reproducibility and efficient communication between clinician and dental technician. No quantitative measurements or statistical analyses were performed. Conclusions: Within the limitations of this qualitative feasibility study, the integration of DSD with CAD/CAM and 3D printing technologies represents a viable digital approach for designing and fabricating dental preparation guides. The workflow shows potential for improving predictability and communication in restorative dentistry. Full article

Galvanized steel sheets are vital anti-corrosion materials, yet their surface quality is prone to defects that impact performance. Manual inspection is inefficient, while conventional machine vision struggles with complex, small-scale defects in industrial settings. Although deep learning offers promising solutions, standard object detection models like YOLOv5 (which is short for ‘You Only Look Once’) exhibit limitations in handling the subtle textures, scale variations, and reflective surfaces characteristic of galvanized sheet defects. To address these challenges, this paper proposes S-C-B-YOLO, an enhanced detection model based on YOLOv5. First, a Squeeze-and-Excitation (SE) attention mechanism is integrated into the deep layers of the backbone network to adaptively recalibrate channel-wise features, improving focus on defect-relevant information. Second, a Transformer block is combined with a C3 module to form a C3TR module, enhancing the model’s ability to capture global contextual relationships for irregular defects. Finally, the original path aggregation network (PANet) is replaced with a bidirectional feature pyramid network (Bi-FPN) to facilitate more efficient multi-scale feature fusion, significantly boosting sensitivity to small defects. Extensive experiments on a dedicated galvanized sheet defect dataset show that S-C-B-YOLO achieves a mean average precision (mAP@0.5) of 92.6% and an inference speed of 62 FPS, outperforming several baseline models including YOLOv3, YOLOv7, and Faster R-CNN. The proposed model demonstrates a favorable balance between accuracy and speed, offering a robust and practical solution for automated, real-time defect inspection in galvanized steel production. Full article

Material handling in special cable manufacturing remains highly inefficient, with manual logistics accounting for nearly 90% of product cycle time. Existing scheduling methods commonly rely on oversimplified assumptions and fail to integrate machine processing with autonomous mobile robot (AMR) transportation constraints, limiting practical applicability. This study proposes a comprehensive scheduling framework that explicitly incorporates AMR movement dynamics—covering empty-load travel and loaded transportation—into flexible job shop scheduling. A dual-objective model is formulated to minimize makespan and total equipment load, providing a more realistic evaluation of workshop performance. To solve this model, an enhanced Sparrow Search Algorithm (SSA) is developed, featuring Pareto dominance sorting, harmonic mean crowding, an external elite archive, and adaptive discoverer–follower scaling to improve convergence stability and avoid premature stagnation. Using real production data from a cable workshop, the proposed method achieves a 15.0% reduction in completion time and a 36.3% reduction in equipment load compared with the traditional SSA. The results demonstrate that the integrated model and improved algorithm offer an effective solution for AMR-constrained multi-objective workshop scheduling. Full article

Manual material handling is one of the leading causes of work-related low-back disorders, and an accurate assessment of the biomechanical risk is essential to support prevention strategies. Despite workers’ interest in wearable sensor networks for quantifying exposure metrics, these systems still present several limitations, including potential interference with natural movements and workplaces, and concerns about durability and cost-effectiveness. For these reasons, alternative motion capture methods are being explored. Among them, completely markerless (ML) technologies are being increasingly applied in ergonomics. This study aimed to compare a wearable sensor network and an ML system in the evaluation of lifting tasks, focusing on the variables and multipliers used to compute the recommended weight limit (RWL) and the lifting index (LI) according to the revised NIOSH lifting equation. We hypothesized that ML systems equipped with multiple cameras may provide reliable and consistent estimations of these kinematic variables, thereby improving risk assessments. We also assumed that these ML approaches could represent valuable input for training AI algorithms capable of automatically classifying the biomechanical risk level. Twenty-eight workers performed standardized lifts under three risk conditions. The results showed significant differences between wearable sensor networks and ML systems for most measures, except at a low risk (LI = 1). Nevertheless, ML consistently showed a closer agreement with reference benchmarks and a lower variability. In terms of the automatic classification performance, ML–based kinematic variables yielded accuracy levels comparable to those obtained with the wearable system. These findings highlight the potential of ML approaches to deliver accurate, repeatable, and cost-effective biomechanical risk assessments, particularly in demanding lifting tasks. Full article

In pharmaceutical and biomedical industries, manual handling of dangerous chemicals is a leading cause of hazardous exposure to chemicals, toxic burning, and chemical contamination. To counteract these risks, we proposed a gesture-controlled bionic hand system to mimic human finger movements for safe and contactless chemical handling. This innovation system uses an ESP32 microcontroller to decode the hand gestures that are detected by the system using computer vision via an integrated camera. A PWM servo driver converts these movements to motor commands such that accurate movements of the fingers can be achieved. Teflon and other corrosion-proof materials are utilized in the 3D printing of the bionic hand in order to withstand corrosive conditions. This new, low-cost, and non-surgical approach replaces the EMG sensors, gives real-time control, and enhances industrial and laboratory process safety. The project is a major milestone in the application of robotics and AI for automation and risk reduction in dangerous environments. Full article

Ergonomics plays a significant role in the industrial workplaces by optimizing process efficiency while considering well-being of operators or workers. In the present case, an ergonomic study was conducted in an automotive manufacturing firm. There were discomfort- and fatigue-related complaints from workers engaged in the assembly and manual material handling operations. A thorough investigation was conducted using Body Parts Symptoms Survey (BPSS) and RULA, after discussing with the workers and observing the operations. Immediate ergonomics interventions and related issues have been identified and a new workstation with improved design was fabricated to minimize the ergonomic issues. Workers reported improvement in comfort level upon working on ergonomically sound workstations. Regular ergonomic assessments and continuous improvement with modification in workstations and processing techniques have been recommended to maintain a safe and productive workplace. Full article

Human Activity Recognition (HAR) is widely used for healthcare, but few works focus on Manual Material Handling (MMH) activities, despite their diffusion and impact on the workers’ health. We propose four Deep Learning algorithms for HAR in MMH: Bidirectional Long Short-Term Memory (BiLSTM), Sparse Denoising Autoencoder (Sp-DAE), Recurrent Sp-DAE, and Recurrent Convolutional Neural Network (RCNN). We explored different hyperparameter combinations to maximize the classification performance (F1-score,) using wearable sensors’ data gathered from 14 subjects. We investigated the best three-parameter combinations for each network using the full dataset to select the two best-performing networks, which were then compared using 14 datasets with increasing subject numerosity, 70–30% split, and Leave-One-Subject-Out (LOSO) validation, to evaluate whether they may perform better with a larger dataset. The benchmarking network DeepConvLSTM was tested on the full dataset. BiLSTM performs best in classification and complexity (95.7% 70–30% split; 90.3% LOSO). RCNN performed similarly (95.9%; 89.2%) with a positive trend with subject numerosity. DeepConvLSTM achieves similar classification performance (95.2%; 90.3%) but requires $\times 57.1$ and $\times 31.3$ more Multiply and ACcumulate (MAC) and $\times 100.8$ and $\times 28.3$ more Multiplication and Addition (MA) operations, which measure the complexity of the network’s inference process, than BiLSTM and RCNN, respectively. The BILSTM and RCNN perform close to DeepConvLSTM while being computationally lighter, fostering their use in embedded systems. Such lighter algorithms can be readily used in the automatic ergonomic and biomechanical risk assessment systems, enabling personalization of risk assessment and easing the adoption of safety measures in industrial practices involving MMH. Full article

Electronics manufacturing workers engaged in material handling are susceptible to occupational back pain. This cross-sectional study aimed to develop a semi-quantitative risk assessment matrix and evaluate ergonomic risk factors contributing to back pain among workers in this industry. A total of 354 electronics assembly workers participated in the study. Data collection involved the use of the Musculoskeletal Disorders (MSDs) Severity and Frequency Questionnaire (MSFQ), the Rapid Upper Limb Assessment (RULA), and workstation lighting intensity measurements. The risk assessment matrix for back pain prediction was applied, and associated factors were analyzed using multiple logistic regression. Results indicated that lighting intensity at 76.52% of inspection stations was below the standard requirements. Furthermore, 57.63% of workstations exhibited high- to very high-risk postures, necessitating ergonomic intervention. The risk matrix predicted that 62.44% of workers were at moderate to very high risk of occupational back pain. Statistical analysis identified manual lifting (OR_adj = 2.48; 95% CI = 1.13–5.44), shift work (OR_adj = 2.21; 95% CI = 1.11–4.40), and inappropriate workstation design (OR_adj = 3.45; 95% CI = 1.42–8.42) as significant contributors to elevated back pain risk. These findings underscore the importance of ergonomic interventions and the application of a semi-quantitative risk assessment matrix for the prevention of occupational back pain in industrial workers. Full article