Intelligent and Sustainable Manufacturing Open Access

Asset Management Excellence: A Roadmap for Integrating Lean Six Sigma and ISO 55001 to Achieve Operational Excellence

Asset Management Excellence (AME) has become essential for sustaining operational efficiency and long-term competitiveness in today’s digitally driven and increasingly complex industrial landscape. This study introduces an integrated roadmap that aligns Lean Six Sigma (LSS)—specifically the DMAIC methodology—with ISO 55001 standards to enhance asset reliability, optimize lifecycle performance, and support continuous improvement. The proposed model embeds principles such as lifecycle value optimization, risk-based decision-making, and sustainability. It leverages proven tools, including Failure Mode and Effects Analysis (FMEA), Root Cause Analysis (RCA), Statistical Process Control (SPC), predictive maintenance, and real-time monitoring to enable proactive, data-driven asset management. This integration supports efficiency, reduces variability, and extends asset life. Performance is measured through key indicators such as Mean Time Between Failures (MTBF), Overall Equipment Effectiveness (OEE), and lifecycle cost-efficiency. These metrics enable organizations to monitor progress, validate improvements, and ensure alignment with strategic objectives. The study also addresses common implementation challenges across financial, organizational, workforce, technological, and structural domains. It proposes targeted mitigation strategies, including phased implementation, cost-benefit analyses, stakeholder engagement, digital readiness assessments, and capacity-building programs to enhance adoption and long-term sustainability. While conceptual, the roadmap offers a practical, scalable approach to embedding LSS within asset management systems. It fosters a transition from reactive to proactive practices, enhancing resilience, sustainability, and strategic value. Future research will validate the framework through sector-specific case studies and pilot implementations.

Advancing Total Productive Maintenance in Smart Manufacturing: From Methodology to Implementation

The rapid advancement of Industry 4.0 technologies has catalyzed the development of intelligent tools and methodologies to enhance operational efficiency, reliability, and productivity across modern industrial enterprises. Total Productive Maintenance (TPM), a foundational approach in manufacturing, traditionally improves equipment reliability, reduces downtime, and drives continuous improvement through proactive employee involvement. However, in the context of Smart Manufacturing, traditional TPM reveals significant limitations—chiefly its reliance on manual data collection, reactive maintenance, and limited real-time insight. This paper explores TPM’s evolution, key innovations, and cross-industry applications while highlighting challenges in adopting Industry 4.0 technologies. It proposes a comprehensive TPM 4.0 framework integrating Lean Six Sigma’s DMAIC methodology with advanced digital tools for systematic failure mode classification, risk-based maintenance prioritization, and real-time performance optimization. Leveraging IIoT-enabled condition monitoring, Digital Twin simulations, and machine learning-driven predictive analytics, the framework supports real-time anomaly detection, cognitive diagnostics, and adaptive maintenance planning—substantially improving Overall Equipment Effectiveness (OEE), cost efficiency, and system resilience. Additionally, federated learning promotes scalable, privacy-preserving AI collaboration, while blockchain enhances data security and transparency, mitigating cybersecurity risks. By merging traditional TPM with AI-driven automation and digital sustainability, TPM 4.0 establishes a foundation for self-optimizing, cyber-resilient maintenance ecosystems, accelerating the transition to autonomous manufacturing. Although conceptual, this framework offers a practical roadmap for smart manufacturing transformation, with future validation planned through case studies and pilot projects.

CO₂ Emissions Comparisons on Cementous Sustainable Flooring Options: Modeling and Evaluation

CO₂ and greenhouse gas emissions have become a major environmental issue worldwide, and emissions have spiked faster than most could ever imagine. The issues have made it crucial to find financially feasible and long-term, use-efficient solutions that fulfill industrial needs. As society so much depends on the current industry outputs, we need to reduce emissions coming from those industrial facilities and premises where people shop and buy services and assets on a daily basis. These emissions need to be reduced on a global scale, and here, concrete as a building material comes into play as one of the most used materials, especially on industrial floors. A typical solution is a sturdy base slab with a use case-specific coating on it. The base slab is expected to last the whole life of the building, whereas the coating might be considered consumable and refurbished/fixed as a maintenance job many times before the building itself is demolished. In heavy use cases, the maintenance cycle might be fast, which reduces the usable time of the building and generates downtimes for business. The coating decisions have a major impact on the building’s lifetime emissions, which is the key focus of this study, too. Bad decisions can introduce unnecessary microplastics and nano dust particles to work environments and also generate restructuring needs of the operational activities. In the worst case, operations have to be shut down. Luckily, there are options, and emissions can be reduced in many ways. By using long-term and durable cementitious mix-based dry shake coatings, one can reduce top coating-based emissions, and by decreasing the amount of used reinforcement components in the base slab, an extra positive impact can be achieved. With a base slab, also more environmentally friendly low-carbon cement formulations can be considered, like fly ash or GGBS (ground granulated blast furnace slag) based formulas, which we discuss in detail and analyses traditional options compared to modern CEM3a and CEM3b versions. For the top coating, emissions are generated in the construction and maintenance phases. To find different options with cross implications on lifetime emissions, our study analyzes CO₂ emissions sources for several concrete mixes, which are then paired with floor-top coatings based on Cementous mix or epoxy coating. We have pinpointed the potential for reducing the building’s floor-based lifetime CO₂ emissions. The analysis is based on the impacts of the base slab and floor coating selection combinations. As a de facto comparison element, we used a 100 percent virgin Portland cement-based mix. The Portland cement was compared to CEM3a and CEM3b mixes. On the top surface of the floor, traditional epoxy base floor coating was compared to a modern dry shake-based option. In the analysis, the dry-shake-based floor showed major benefits. Emissions were drastically reduced, fewer maintenance downtimes were needed, and the general life expectancy was a lot longer for the dry shake option.

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.