Search Results (235)

Additive manufacturing (AM), commonly known as 3D printing, has rapidly transformed modern manufacturing, creating a growing demand for engineers with both theoretical knowledge and practical skills. Despite its increasing relevance, AM is often incorporated into engineering curricula as a supplementary tool rather than a fully integrated subject, limiting students’ understanding of fundamental material–process–performance relationships. This study presents the development, implementation, and assessment of an integrated lecture–laboratory framework for AM education at the New Jersey Institute of Technology (NJIT). Two complementary courses were developed: an undergraduate course (Introduction to 3D Printing, CHE 415) and a graduate course (Additive Manufacturing and Applications, CHE 722). The curriculum integrates instruction in AM technologies, materials, and digital workflows with hands-on design challenges, team-based projects, and structured literature reviews, enabling students to engage in the complete design-to-fabrication process. Student learning outcomes were evaluated over multiple academic years using ABET-aligned assessments, grade distributions, and student self-assessments. Results demonstrate consistently high levels of student proficiency and engagement, with strong performance in design, problem-solving, and communication skills. The courses also attracted students from diverse disciplines, underscoring the interdisciplinary nature of AM education. While limitations remain in providing hands-on exposure to a broader range of AM technologies, ongoing expansion of laboratory infrastructure is expected to address these challenges. Overall, this work demonstrates that an integrated, project-based approach effectively bridges theory and practice and provides a scalable model for incorporating AM into engineering curricula. Full article

Artificial intelligence (AI) is increasingly influencing nanopharmaceutical development by supporting the transition from empirical formulation screening toward predictive, data-driven, and translationally oriented design. Nanocarrier-based therapeutics are governed by nonlinear relationships among material composition, physicochemical attributes, manufacturing parameters, biological identity, pharmacokinetics, toxicity, and therapeutic performance. In this review, we examine how AI can contribute to nanopharmaceutical development from predictive formulation design to clinical translation. We synthesize current applications of machine learning, deep learning, physics-informed modeling, hybrid mechanistic–AI approaches, and automated optimization workflows, with emphasis on critical quality attribute modeling, multi-objective optimization, design of experiments, quality-by-design, process analytical technology, digital twins, and continuous manufacturing. We also discuss applications involving nano–bio interactions, pharmacokinetics, toxicity, immunogenicity, and precision nanomedicine. AI-based approaches can support rational nanocarrier design, identify nonlinear formulation–property relationships, guide optimization, improve process understanding, and integrate heterogeneous experimental, biological, and manufacturing datasets across diverse nanopharmaceutical platforms. These methods are particularly relevant for modeling protein corona formation, cellular uptake, intracellular trafficking, biodistribution, pharmacokinetics, toxicity, immunogenicity, and patient-specific responses. However, translational implementation remains limited by fragmented datasets, inconsistent reporting standards, limited interpretability, insufficient external validation, uncertain predictions, poorly defined applicability domains, and evolving regulatory expectations for adaptive computational models. Overall, AI should be viewed not only as an optimization tool, but also as a translational framework connecting formulation science, biological prediction, manufacturing control, and clinical implementation. Future progress will depend on standardized data infrastructures, explainable and externally validated models, uncertainty quantification, applicability-domain definition, hybrid mechanistic–AI frameworks, regulatory-ready documentation, and clinically relevant case studies. Full article

Heterogeneous fault information and a lack of real-time synchronization in CNC machine tools hinder effective Prognostics and Health Management (PHM). This paper designs and implements a digital twin-driven PHM framework for machine tools that integrates a unified machine-tool fault information dictionary and a mechanism-data dual-driven diagnostic model (ResNet-TCN). A cyber–physical platform was developed using OPC UA and RESTful APIs to ensure real-time data synchronization. Experiments on the PHM 2010 dataset demonstrate that the proposed ResNet-TCN model achieves a root mean square error (RMSE) of 5.46 μm for tool wear prediction. Its performance surpasses that of traditional LSTM models, and the proposed framework effectively eliminates information silos, providing a responsive, scalable and accurate PHM solution for smart manufacturing. Full article

Prefabricated timber construction relies on coordinated digital processes that integrate architectural design, structural engineering, and manufacturing requirements. However, current industry practices are highly fragmented, with models often reconstructed across different software platforms, and collaboration is mainly focused on exchanging files and document-based approvals. These issues lead to geometric misalignments, delayed coordination of manufacturing limitations, and inefficient design-to-manufacturing workflows. This study introduces a parametric geometry-based integration framework aimed at enhancing digital continuity throughout the design, engineering, and manufacturing stages of prefabricated timber buildings. The framework offers a rule-based parametric system where geometry is governed by specific relationships and constraints, enabling the development of discipline-specific models from a unified computational source. A model was created using Rhinoceros and Grasshopper to generate a parametric timber module and to test cross-platform compatibility with structural analysis software (Dlubal) and manufacturing detailing software (Cadwork). The results show that parameter-driven geometry can be integrated across platforms, supporting reduced primary geometry re-authoring and improved cross-platform coordination within the proof-of-concept workflow. The framework extends technical validation by shifting parametric modelling from merely a generative design tool to a comprehensive infrastructure that supports industrialised timber workflows. The proposed approach provides a practical solution to enhance design-to-manufacturing integration in prefabricated construction, while maintaining modelling flexibility specific to each discipline. Full article

AI-enabled quality control (AI-QC) tools are increasingly available to manufacturing SMEs in emerging economies, yet the firm-level conditions associated with their adoption remain underexamined. Building on the Technology–Organization–Environment (TOE) framework of Tornatzky and Fleischer, integrated with the perceived usefulness and perceived ease-of-use constructs of the Technology Acceptance Model (TAM), this study examines the determinants of AI-QC adoption intention, and its association with operational performance improvement, in 284 manufacturing SMEs from Turkey, Malaysia, and Egypt. The focal dependent construct is adoption intention rather than realized adoption. The AI-QC technologies considered are machine learning defect detection, computer vision inspection, predictive maintenance, and digital twin integration. Three complementary analytical procedures are applied to the same data: partial least squares structural equation modeling (PLS-SEM) to estimate the strength of the modeled associations, importance–performance map analysis (IPMA) to identify high-importance but low-performance predictors, and fuzzy-set qualitative comparative analysis (fsQCA) to identify combinations of conditions jointly sufficient for high adoption intention. The PLS-SEM estimates indicate positive associations for the technological, organizational, and environmental predictors, with top management support, perceived usefulness, and organizational readiness showing the largest coefficients and data security concern showing a negative association; effect magnitudes varied considerably, and several were small. The IPMA results indicate that the two most important predictors exhibit comparatively low performance scores in the sample. The fsQCA results identify three configurations associated with high adoption intention. Because the design is cross-sectional and based on self-reported, single-respondent data, the findings are interpreted as associations rather than causal effects. The paper concludes with guidance for SME managers, AI technology vendors, and industrial policymakers. Full article

Digital tools in aluminum alloy processing are computational, sensing-based, and data-driven methods used to understand, predict, monitor, optimize, and control how aluminum alloys are transformed into components. They support decisions across casting, deformation processing, heat treatment, welding, surface engineering, and additive manufacturing by linking processing conditions with geometry, microstructure, defects, properties, and service performance. In technical use, the term includes finite element method (FEM), computational fluid dynamics (CFD), CALculation of PHAse Diagrams (CALPHAD), microstructure models, machine-learning regressors, surrogate models, nondestructive digital inspection, image-analysis tools, and digital twins. These tools are most effective when they establish links among controllable processing variables, underlying metallurgical mechanisms, measurable quality indicators, and service-relevant performance outcomes. Full article

The pet food sector has progressively evolved over the past decade from conventional nutrition toward functionally targeted and sustainability-oriented systems that are increasingly parallel developments in human health. While numerous reviews have examined individual aspects of pet food innovation, an integrated perspective linking scientific research, patent activity, and global market dynamics remains limited. This review addresses this gap by systematically synthesizing peer-reviewed literature, patent landscapes, and product launch data to identify key drivers and bottlenecks shaping contemporary pet food innovation. The analysis highlights a strong concentration of research and patent activity in health-oriented functional formulations, particularly those targeting gastrointestinal health, immune modulation, and age-related conditions, while postbiotics, precision nutrition, and digital tools remain comparatively underdeveloped. Sustainability-driven ingredients and alternative proteins show growing momentum but face persistent challenges related to scalability, regulation, and sensory acceptance. The commercial success of functional pet foods depends on translating scientific findings into stable, manufacturable, and evidence-supported products. Future innovation will therefore be shaped by technologies that connect biological function with process feasibility and market readiness. This review concludes that future progress in pet food innovation will depend on integrated frameworks that align biological efficacy, technological feasibility, and market viability, thereby bridging the gap between scientific advancement and commercial implementation. Full article

In the digital economy era, computing power, as a novel factor of production, serves as a vital engine for driving high-quality economic development. Building upon China’s traditional 42-sector input–output table, this paper incorporates computing power networks as a new sector to construct a 43-sector dynamic input–output (IO) model. Based on this framework, a Dynamic Stochastic General Equilibrium (DSGE) analysis framework is constructed to systematically reveal the dynamic transmission mechanism of computing power within industrial linkages and capital accumulation. From an energy perspective, energy consumption is implicitly captured through carbon emissions and energy structure, which together reflect the scale, efficiency, and composition of energy use in computing power networks. The findings show that the optimal computing power allocation follows a temporal evolution pattern from the service sector to the manufacturing sector, with ICT manufacturing’s computing power quota reaching 31% by 2030. An investment inflection point occurs in 2026, aligning with the digital infrastructure cycle of China’s 14th Five-Year Plan. The “Eastern Data, Western Computing” strategy reduces unit carbon emissions from computing power by 41%. Policy simulations demonstrate that R&D tax credits generate a 2.9-fold multiplier effect through industrial linkages, boosting GDP by 2.3%. The integrated IO-DSGE framework developed in this study provides a quantitative tool for the full-cycle management of “construction–application–regulation” in computing power networks. It holds significant theoretical value and practical implications for enhancing resource allocation efficiency and promoting green, climate-friendly development. Full article

Traditional petroleum-based packaging suffers from pollution and functional limits, making it unsuitable for next-generation food systems. In contrast, bio-based smart packaging—combining renewable substrates with responsive components—transforms packaging from a passive shell into an active quality monitor and supply chain information node through three interconnected pillars: renewability, real-time responsiveness to freshness markers, and digital traceability. Market figures confirm this shift, with the smart food packaging sector projected to reach USD 48.97 billion by 2028 (CAGR 4.49% from 2023). This review covers recent progress in natural polymers (cellulose, chitosan, alginate, gelatin) and bio-based polyesters (PLA, PHA). Their multiscale structures enable tunable mechanical and barrier properties while serving as hosts for intelligent functions. Two functional directions stand out: active preservation (antimicrobial, antioxidant, gas-regulating, stimulus-controlled release) and intelligent sensing (colorimetric indicators, bio-based sensors, nano-amplified signals for real-time freshness monitoring). Beyond material functions, digital tools such as IoT and blockchain turn packaging into interactive data nodes, linking material intelligence with full traceability to enhance food safety and supply chain efficiency. Key challenges remain with long-term operational stability, production costs, scalable manufacturing, and life cycle assessments. Nevertheless, bio-based smart packaging is expected to evolve through biomimetic design, process innovation, and system-level integration toward adaptability, multifunctionality, and intelligence, ultimately supporting safer, more transparent, efficient, and sustainable food systems. Full article

Most existing analytical models for gear skiving define the cutting edge indirectly through hypothetical generating gears. This abstraction introduces an inherent geometric inconsistency between the theoretical cutting edge and the true conical rake surface of the cutter, limiting prediction accuracy and hindering measurement-driven compensation. To address this limitation, this study proposes a unified analytical and measurement-driven framework for cylindrical gear skiving that eliminates the generating-gear assumption entirely. A direct surface-based meshing formulation is developed by enforcing positional coincidence and tangential compatibility directly on the cutter’s conical rake surface, ensuring strict geometric consistency with the physical cutting mechanism. To incorporate real cutter deviations, a tension-controlled spline reconstruction method is introduced to recover smooth and curvature-stable cutting edge curves from noisy three-dimensional measurement data, overcoming the oscillation and instability commonly associated with high-order polynomial fitting. By integrating direct surface-based meshing, spline-regularized reconstruction, and CNC-oriented kinematics within a single formulation, this work establishes a complete digital chain for precision skiving cutter modeling, simulation, and compensation, providing a practical foundation for advanced tool design and manufacturing optimization. Full article

The global food system faces high pressure to sustain a growing population amid climate constraints and shifting consumer demands, making the traditional trial-and-error development methodologies inadequate. Artificial Intelligence (AI) has transitioned from a simple optimization tool into a structural enabler across the entire food chain. This review examines the integration and evolution of computational architectures in food technology between 2006 and 2026, tracing the paradigm shift from the early fuzzy logic and rule-based systems to modern deep learning and generative frameworks. This review highlights breakthroughs achieved over the last five years, demonstrating how Graph Neural Networks, Transformers, and Variational Autoencoders and other architectures are accelerating the in silico discovery of bioactive ingredients, predicting complex molecular flavors, and autonomously synthesizing optimal culinary formulations. The transition to Industry 5.0 is also explored, emphasizing the integration of collaborative robotics, process-level digital twins, and federated learning to enable autonomous manufacturing and privacy-preserving precision nutrition. Finally, this review addresses critical barriers to commercialization, including severe data fragmentation, the “Innovation Paradox” in fundamental academic research, and the urgent need for multidisciplinary teams capable of translating digital predictions into physically stable, strictly regulated food matrices. Full article

The NPRR research project “From waste to manufacturing” developed an AI-powered digital tool to support the transition towards a circular built environment in Italy. The tool integrates a web platform enabling data exchange about materials with recycled content between designers, manufacturers and waste recyclers, with a CAD plug-in for real-time sustainability assessment. As such, the tool fosters the use of recycled materials and allows a reduction in embodied energy. AI, trained through scenario-based learning and stakeholder participation, assists designers in sourcing recycled materials and processing data. With further training by LCA experts, it could interpret Environmental Product Declarations to guide material selection in line with international regulations. Full article

Everyday heritage objects are often overlooked despite their cultural significance and vulnerability to surface degradation caused by environmental exposure, material ageing, and human interaction. This review examines how surface characterization, digital documentation, additive manufacturing, and extended reality (XR) technologies can be integrated to support the conservation, replication, and inclusive dissemination of such assets. The study synthesizes recent advances in non-destructive surface analysis methods, including spectroscopic and imaging techniques, alongside 3D scanning approaches capable of capturing both geometry and surface condition. These data are linked to additive manufacturing workflows for producing accurate and durable replicas, with particular attention to surface fidelity and material selection. The review further explores how tactile replicas and multimodal interpretation strategies can enhance accessibility for visually impaired users, addressing limitations of visually dominant heritage practices. XR technologies are discussed as complementary tools for interpretation and remote access. The findings highlight that combining surface-focused conservation with digital and fabrication technologies enables more resilient, accessible, and sustainable heritage management. Future research should focus on standardizing inclusive design approaches and improving the integration of surface data into digital and physical reproduction pipelines. Full article

This review paper presents a comprehensive and system-oriented analysis of advanced non-destructive testing (NDT) technologies for metal additive manufacturing (AM), including X-ray computed tomography (XCT), ultrasonic testing (UT), infrared thermography, acoustic emission (AE), and electromagnetic techniques. While the existing literature often focuses on the physical principles of individual NDT methods, this work addresses a critical knowledge gap by analyzing NDT as a digitally integrated “quality intelligence layer” rather than a standalone post-process inspection tool. The primary motivation is to bridge the disconnect between raw inspection data and cyber–physical production systems. Particular focus is given to NDT data analytics and digitalization, where machine learning (ML) and digital twin (DT) integration are discussed as fundamental enablers of intelligent manufacturing. The review systematically examines image and signal processing pipelines required for quantitative defect characterization, highlighting challenges related to voxel resolution, signal-to-noise ratio, anisotropic microstructures, and operator dependency. It further analyzes supervised learning, deep learning, and multi-sensor data fusion approaches for automated defect classification and predictive quality assessment. Furthermore, the role of digital twins in coupling in situ monitoring data, ex situ NDT results, and physics-based models is discussed as a transformative pathway toward closed-loop process control and evidence-based certification. By synthesizing NDT science with digital manufacturing architectures, this review contributes a unique framework for transitioning from traditional inspection-centric quality control to a predictive, adaptive, and digital twin-enabled quality assurance paradigm. The work concludes by identifying key research gaps in data standardization and computational scalability, providing a strategic roadmap for the future of smart AM production. Full article

The increasing digitalization of manufacturing systems and emphasis on sustainable development are transforming maintenance from a purely operational function into a strategic driver of customer value in the digital economy. However, the relationship between maintenance maturity and consumer-perceived sustainability remains largely unexplored. This study addresses the following research questions: (RQ1) How does maintenance maturity influence consumer-perceived sustainability and trust? (RQ2) How can operational resilience be linked to customer perception through a structured modeling approach? (RQ3) Which maintenance strategy provides the highest combined operational and sustainability value? To address these questions, the Integrated Maintenance Maturity Model with a Customer-Centric Sustainability Layer (IMMM–CCSL) is proposed. The framework links maintenance maturity with consumer sustainability perception using a structured fuzzy-based aggregation approach. Five consumer-oriented dimensions are considered: product lifecycle extension, service continuity and trust, consumer maintenance experience, perceived ecological performance, and post-sale engagement. A composite Customer Sustainability Index (CCSI) is introduced to quantify the relationship between maintenance maturity and consumer perception. The model is applied in an illustrative case study comparing reactive, preventive, predictive, and AI-enhanced maintenance strategies. The results indicate that CCSL values range from 0.709 to 0.749, while the overall CCSI equals 0.729, suggesting a consistently high level of consumer-perceived sustainability associated with higher maintenance maturity. Predictive maintenance demonstrates the highest contribution to both operational reliability and perceived sustainability outcomes within the analyzed case. Overall, the IMMM–CCSL framework offers a structured, interpretable tool for aligning maintenance strategy with sustainable production and consumption objectives, supporting managers and policymakers in translating technical capabilities into measurable consumer sustainability outcomes. The findings should be interpreted as exploratory and case-specific, given the illustrative nature of the study. Full article