Intelligent and Sustainable Manufacturing Open Access

Enhancing Product Development Excellence through Quality Management Tools: A Comprehensive Review and Integrated Conceptual Framework

In today’s rapidly evolving and highly competitive global markets, achieving product development excellence is critical for organizations striving for sustained growth and customer-centric innovation. This study highlights the integral role of key quality management tools in enhancing product development processes, reducing defects, and driving continuous improvement. It presents a robust methodology that strategically combines Quality Function Deployment (QFD), Failure Mode and Effects Analysis (FMEA), and the DMAIC (Define, Measure, Analyze, Improve, Control) framework to significantly improve the quality, reliability, and efficiency of product development efforts. Built on core principles of customer-centricity, innovation, cross-functional collaboration, continuous improvement, and risk-based thinking, the methodology emphasizes capturing the Voice of the Customer (VoC) and identifying Critical-to-Quality (CTQ) attributes to align product outcomes with customer expectations and business objectives. Utilizing the DMAIC framework, the organization systematically drives process optimization and innovation throughout the product lifecycle Key Performance Indicators (KPIs) are established to track efficiency, quality, customer satisfaction, and time-to-market, while Agile methodologies enhance flexibility, speed, and responsiveness. The study further identifies organizational, technical, cultural, and managerial barriers to product development excellence and proposes targeted strategies to address them and ensure sustainable success. This integrated framework fosters a culture of innovation and continuous learning, enabling organizations to anticipate challenges, manage risks, and consistently deliver superior product development outcomes. While currently conceptual, the framework is slated for empirical validation through case studies, pilot projects, and simulations to verify its practical applicability across diverse development contexts.

Mechanisms of Machine Vision Feature Recognition and Quality Prediction Models in Intelligent Production Line for Broiler Carcasses

With global broiler production reaching 103 million tons in 2024—a 1.5% increase over 2023—the poultry industry continues to grow rapidly. However, traditional broiler segmentation methods struggle to meet modern demands for speed, precision, and adaptability. First, this study proposes an improved lightweight image segmentation algorithm based on YOLOv8-seg and integrates the Segment Anything Model (SAM) for semi-automatic annotation, achieving precise mask segmentation of broiler parts. Subsequently, Key geometric features (e.g., area, perimeter, axes) were extracted using image processing techniques, with enhancements from HSV color transformation, convex hull optimization, and ellipse fitting. Furthermore, Image calibration was applied to convert pixel data to physical dimensions, enabling real-sample validation. Using these features, multiple regression models—including CNNs—were developed for carcass quality prediction. Finally, by analyzing the broiler segmentation process, machine vision techniques were effectively integrated with quality grading algorithms and applied to intelligent broiler segmentation production lines, providing technical support for the intelligent and efficient processing of poultry products. The improved YOLOv8-seg model achieved mAP@0.5:box scores of 99.2% and 99.4%, and the CNN model achieved R² values of 0.974 (training) and 0.953 (validation). Compared to traditional systems, the intelligent broiler cutting line reduced failure rates by 11.38% and improved operational efficiency by over 3%, offering a reliable solution for automated poultry processing.

Modeling and Assessing Economical Feasibilities for Waste to Energy Conversion/Incineration Process in Context of Municipal Solid Waste

At the time of the study, most of the municipal waste, including solid municipal waste, in the city of St. Petersburg and in the connected larger Leningrad region is processed by landfilling. This sort of waste processing in open landfills causes environmental damage, uncontrollable landfill fires, bad and dangerous odors, nearby rivers/streams, groundwater pollution, CH₄ and CO₂ emissions, to mention a few. Additionally, landfilling is a waste of energy and material resources present in the content dumped into landfills. In this context, Waste-to-Energy (WtE) incineration is a process that we use to recover the energy the materials have back to usable form, which we use in the form of heat and electricity. Even though a lot of resources and energy are available in the (municipal solid) waste, it does not mean that recovering it would always make sense. Our study analyses and estimates the profitability of a WtE incineration plant(s) in the city of St. Petersburg and the connected Leningrad region. With the available data and following analysis, we have concluded that the WtE incineration is economically feasible in this specific region and city areas, given that the implementations follow more traditional (economically less expensive and easier) technical and process model solutions. As a note of results stability, it needs to be pointed out that the changes in estimates of gate fees, cost of electricity and heat, and so on do impact the economic feasibility a lot, and larger scale changes in the assumed revenues would have a high impact on the outcome of repeatability of the results.

Prediction of an Additive Manufacturing Defect Based on Deep Learning

Selective laser melting (SLM) is one of the additive manufacturing (AM) methods and the most studied laser-based AM process for metals and alloys. The optimization of the laser process parameters of SLM and the prediction of defects such as cracks, keyholes, and lack of fusion (LOF) are significant for improving the product quality of SLM. Deep learning (DL) has the potential to analyze complex processes and predict anomalies; however, much data is generally needed for training a DL model. Experimental studies on AM, such as SLM, often use the design of experiments (DOE) to reduce the number of experiments and save costs and time. Therefore, much experimental data is not prepared to create the DL model. This paper deals with creating a DL model on a small experimental data set with unbalanced data and predicting the defect LOF of SLM using the created DL model. Data analytics is performed based on four DL methods, including the Elman neural network, the Jordan neural network, the deep neural network (DNN) with weights initialized by the deep belief network (DBN), and the regular DNN based on the algorithms ‘rprop+’ and ‘sag’. It is shown that the regular DNN based on the ‘sag’ algorithm, after the z-score standardization of the small data set, helps create an accurate DL model and achieve good analytics and prediction results. The three other DL methods in this paper do not work well on the small data set (with unbalanced data) in the defect prediction.

Technical and Eco-Efficiency Implications of the Use of Basalt Fibre in Hybrid Composites

The use of hybrid composites can be environmentally friendlier than the traditional materials since renewable resources, both natural and synthetic fibres can be incorporated into the composites, resulting in lighter weight, enhanced resource efficiency, durability, and biodegradability, which could potentially make them sustainable materials for structural applications. Basalt fibre being treated with hydrochloric acid exhibits superior adhesion with the epoxy matrix, improving overall strength and stiffness. Thus, the aim of this paper is to determine the eco-efficiency of two types of hybrid composites: glass/basalt and carbon/basalt fibre-reinforced under flexural loading. The flexural strengths of these composites were obtained through a Finite Element Analysis (FEA) model using Ansys workbench. These simulation-based flexural strengths form the basis for the quadratic regression model to establish a relationship between the different flexural strengths and fibre volume fractions combinations. Given the required flexural strength between 900 and 1300 MPa, the optimal candidates/layups were identified with the aid of the model. An environmental study following a life cycle assessment (LCA) and eco-efficiency framework of unidirectional glass/basalt and carbon/basalt fibre-reinforced hybrid composites with varying fibre volume fractions is presented in this paper to select the eco-efficient composites. In the case of glass/basalt fibre-reinforced hybrid composites, the designs with the highest eco-efficiency for 900 and 1200 MPa are [BG₃]_S with more glass fibre and [G₇B] with more glass fibre, respectively, due to having lower costs and environmental impacts. For carbon/basalt fibre-reinforced composites, the stacking sequence [B₈] was deemed to be the most eco-efficient. Finally, epoxy has the highest economic and environmental cost. Therefore, composite designs with high glass fibre content are considered eco-efficient since they have a lower epoxy content.

Sustainable Machining for Difficult-to-Cutting Materials

Wide-Bandgap Semiconductors: A Critical Analysis of GaN, SiC, AlGaN, Diamond, and Ga₂O₃ Synthesis Methods, Challenges, and Prospective Technological Innovations

The increasing demand for high-performance Wide-Bandgap (WBG) semiconductors, including GaN, SiC, and emerging Ultrawide-Bandgap (UWBG) materials such as Ga₂O₃ and diamond, has driven significant advancements in epitaxial growth techniques. However, achieving scalability, defect-free growth, and sustainability remains a major challenge. This review systematically evaluates Molecular Beam Epitaxy (MBE), Metal-Organic Chemical Vapor Deposition (MOCVD), Hydride Vapor Phase Epitaxy (HVPE), and other novel growth and hybrid growth techniques, emphasizing energy efficiency, defect control, and environmental impact. Industry 4.0-driven AI-based process optimization and closed-loop recycling have emerged as transformative strategies, reducing waste and improving manufacturing efficiency. Key findings reveal that HVPE enables rapid defect-free GaN fabrication, Hot-Filament CVD enhances SiC growth with superior thermal properties, and Atomic Layer Epitaxy (ALE) achieves sub-nanometer precision crucial for next-generation quantum and RF applications. Despite these advancements, p-type doping in UWBG materials, substrate compatibility, and thermal management remain unresolved challenges. Future research must focus on scalable eco-friendly epitaxy, novel doping mechanisms, and policy-driven sustainability efforts. This review provides a comprehensive roadmap for sustainable WBG semiconductor manufacturing, bridging materials innovation, energy efficiency, and industrial adoption to support the next generation of power electronics and optoelectronics.

Sustainable Manufacturing and Applications of Wide-Bandgap Semiconductors—A Review

Wide-bandgap (WBG) semiconductors such as silicon carbide (SiC) and gallium nitride (GaN) are revolutionizing high-power electronics due to their superior thermal conductivity, breakdown voltage, and energy efficiency. These materials are critical in electric vehicles, renewable energy systems, and high-frequency applications like 5G infrastructure. However, their production processes are resource-intensive and present significant environmental challenges. This review evaluates recent advancements in sustainable WBG semiconductor manufacturing, focusing on low-energy epitaxial growth, closed-loop recycling, and the mitigation of toxic by-products. Additionally, it highlights the role of Industry 4.0 innovations, such as AI-driven process optimization and IoT-based resource management, in enhancing sustainability. The review identifies research gaps in cost reduction, alternative WBG materials like Gallium Oxide (Ga₂O₃) and Diamond, and scalable green manufacturing solutions. It underscores the necessity for industry-wide collaboration and regulatory frameworks to drive the adoption of eco-friendly semiconductor fabrication. The findings of this study provide a roadmap for advancing sustainability in WBG semiconductor production, ensuring their long-term viability in the transition toward energy-efficient technologies.

A Shear Stress Model for Magnetorheological Fluid with High Volume Fraction

Shear stress prediction in high-concentration magnetorheological fluids (MRFs) faces limitations due to the oversimplified magnetic dipole interactions and neglect of multibody effects in classical single-chain models, particularly under conditions (30–40 vol.%) where stress prediction errors start escalating nonlinearly. To address this gap, based on the classic single-chain model, this study proposed a new revised calculation method that integrates three novel components: (1) a distance-weighted dipole interaction model incorporating material-specific correction factors, (2) dynamic chain reconstruction mechanisms accounting for magnetic aggregation under shear deformation, and (3) transverse field overlap parameters quantifying anisotropic field distributions. Validated against Lord Corp.’s MRF-132DG, the proposed approach reduces shear stress prediction root-mean-square error (RMSE) by 71.7% (from 27.40 kPa to 7.76 kPa). It rectifies the R-square metric from −0.9236 to 0.8457, outperforming existing models in high-concentration regimes. The work resolves the bottleneck of modeling chain-to-network transition behaviors through Monte Carlo simulations with energy barrier analysis, revealing how localized dipole rearrangement governs macroscopic rheological responses. The methodology’s adaptability to pre-saturation magnetization stages further enables systematic evaluation of multi-dipole interaction thresholds critical for high-performance MRF engineering applications.

A Conceptual Design of Industrial Asset Maintenance System by Autonomous Agents Enhanced with ChatGPT

This article introduces OPRA (Observation-Prompt-Response-Action) and its multi-agent extension, COPRA (Collaborative OPRA), as frameworks offering alternatives to traditional agent architectures in intelligent manufacturing systems. Designed for adaptive decision-making in dynamic environments, OPRA enables agents to request external knowledge—such as insights from large language models—to bridge gaps in understanding and guide optimal actions in real-time. When predefined rules or operational guidelines are absent, especially in contexts marked by uncertainty, complexity, or novelty, the OPRA framework empowers agents to query external knowledge systems (e.g., ChatGPT), supporting decisions that traditional algorithms or static rules cannot adequately address. COPRA extends this approach to multi-agent scenarios, where agents collaboratively share insights from prompt-driven responses to achieve coordinated, efficient actions. These frameworks offer enhanced flexibility and responsiveness, which are critical for complex, partially observable manufacturing tasks. By integrating real-time knowledge, they reduce the need for extensive training data and improve operational resilience, making them a promising approach to sustainable manufacturing. Our study highlights the added value OPRA provides over traditional agent architectures, particularly in its ability to adapt on-the-fly through knowledge-driven prompts and reduce complexity by relying on external expertise. Motivational scenarios are discussed to demonstrate OPRA’s potential in critical areas such as predictive maintenance.