Search Results (276)

Carbon fiber (CF) and carbon fiber-reinforced polymers (CFRPs) are widely used in the aerospace, automotive, and energy sectors due to their high strength, stiffness, and low density. However, significant waste is generated during manufacturing and after the use of CFRPs. Traditional disposal methods like landfilling and incineration are unsustainable. CFRP machining processes, such as drilling and milling, produce fine chips and dust that are difficult to recycle due to their heterogeneity and contamination. This study investigates the oxidation behavior of CFRP drilling waste from two types of materials (tube and plate) under oxidative (non-inert) conditions. Thermogravimetric analysis (TGA) was performed from 200 °C to 800 °C to assess weight loss related to polymer degradation and carbon fiber integrity. Scanning electron microscopy (SEM) was used to analyze morphological changes and fiber damage. The optimal range for removing the polymer matrix without significant fiber degradation has been identified as 500–600 °C. At temperatures above 700 °C, notable surface and internal fiber damage occurred, along with nanostructure formation, which may pose health and environmental risks. The results show that partial fiber recovery is possible under ambient conditions, and this must be considered regarding the harmful risks to the human body if submicron particles are inhaled. This research supports sustainable CFRP recycling and fire hazard mitigation. Full article

In response to growing environmental pressures and material constraints, circular economy principles are gaining traction across manufacturing sectors. However, most existing frameworks emphasize design and supply chain considerations, with limited focus on how circularity can be operationalized within internal manufacturing systems. This paper proposes a conceptual model that embeds circular operations at the core of production strategy. Grounded in circular economy theory, operations management, and socio-technical systems thinking, the model identifies four key operational pillars: circular input management, looping process and waste valorization, product-life extension, and reverse logistics. These are supported by enabling factors—digital infrastructure, organizational culture, and leadership—and mediated by operational flexibility, which facilitates adaptive, closed-loop performance. The model aims to align internal processes with long-term sustainability outcomes, specifically resource efficiency and operational resilience. Practical implications are outlined for resource-intensive industries such as automotive, electronics, and FMCG, along with a readiness assessment framework for guiding implementation. This study offers a pathway for future empirical research and policy development by integrating circular logic into the structural and behavioral dimensions of operations. The model contributes to advancing the Sustainable Development Goals (SDGs), particularly SDG 9 and SDG 12, by positioning circularity as a regenerative operational strategy rather than a peripheral initiative. Full article

The use of aluminum honeycomb structures is fast expanding in advanced sectors such as the aeronautics, aerospace, marine, and automotive industries. However, processing these structures represents a major challenge for producing parts that meet the strict standards. To address this issue, an innovative manufacturing method using longitudinal ultrasonic vibration-assisted cutting, combined with a CDZ10 hybrid cutting tool, was developed to optimize the efficiency of traditional machining processes. To this end, a 3D numerical model was developed using the finite element method and Abaqus/Explicit 2017 software to simulate the complex interactions among the cutting tool and the thin walls of the structures. This model was validated by experimental tests, allowing the study of the influence of milling conditions such as feed rate, cutting angle, and vibration amplitude. The numerical results revealed that the hybrid technology significantly reduces the cutting force components, with a decrease ranging from 10% to 42%. In addition, it improves cutting quality by reducing plastic deformation and cell wall tearing, which prevents the formation of chips clumps on the tool edges, thus avoiding early wear of the tool. These outcomes offer new insights into optimizing industrial processes, particularly in fields with stringent precision and performance demands, like the aerospace sector. Full article

Against the backdrop of increasingly severe global climate change, the automotive industry, as a carbon-intensive sector, has found its low-carbon transformation crucial for achieving the “double carbon” goals. This paper constructs manufacturer decision-making models under an oligopolistic market scenario for the single dual-credit policy and the “dual-credit + carbon cap-and-trade” policy, revealing the nonlinear impacts of new energy vehicle (NEV) credit trading prices, carbon trading prices, and credit ratio requirements on manufacturers’ pricing, emission reduction effort levels, and profits. The results indicate the following: (1) Under the “carbon cap-and-trade + dual-credit” policy, manufacturers can balance emission reduction costs and NEV production via the carbon trading market to maximize profits, with lower emission reduction effort levels than under the single dual-credit policy. (2) A rise in credit trading prices prompts hybrid manufacturers (producing both fuel vehicles and NEVs) to increase NEV production and reduce fuel vehicle output; higher NEV credit ratio requirements raise fuel vehicle production costs and prices, suppressing consumer demand. (3) An increase in carbon trading prices raises production costs for both fuel vehicles and NEVs, leading to decreased market demand; hybrid manufacturers reduce emission reduction efforts, while others transfer costs through price hikes to boost profits. (4) Hybrid manufacturers face high carbon emission costs due to excessive actual fuel consumption, driving them to enhance emission reduction efforts and promote low-carbon technological innovation. Full article

With the growing need for lightweight, durable, and high-performance structures, additively manufactured (AM) polymer composite structures have captured significant attention in the engineering community. These structures offer considerable advantages in various dynamic engineering sectors including automotive, aviation, and military. Thus, this investigation emphasizes the numerical analysis of the dynamic properties and vibration control of AM polylactic acid (PLA) composite structures reinforced with continuous glass fibers (CGFR-PLA) and carbon fibers (CCFR-PLA), with 0°–0° and 0°–90° layer orientations. The findings of this numerical study are compared and validated against earlier published experimental results. Initially, the numerical models were created using the Abaqus CAE 2024, replicating the actual experimental models. The numerical bending modal frequency of each numerical model is determined, and the 0°–0° oriented models exhibited considerably higher values compared to the corresponding 0°–90° models. Significant differences were noted between the numerical and experimental values in the higher modes, mainly due to existence of voids and misalignment in the actual models that were not considered in numerical models. Following this, a numerical amplitude frequency response (AFR) analysis was conducted to observe vibration amplitude variations as a function of frequency. The AFR numerical results demonstrated consistent trends with the experimental results despite differences between the absolute values of both scenarios. Afterwards, vibration amplitude control analysis was performed under the influence of a macro fiber composite (MFC) actuator. The findings from both numerical and experimental cases revealed that vibration control was noticeably higher in 0°–0° oriented structures compared to 0°–90° structures. Experimental models demonstrated higher vibration control effectiveness than the corresponding numerical models. Although significant differences between the numerical and experimental vibration response values were observed in each composite structure, the numerical results exhibited consistent trends with the experiments. This discrepancy is attributed to the challenge of capturing all boundary conditions of the experimental scenario and incorporating them into the numerical simulation. Full article

Additive Manufacturing (AM) techniques, such as Fused Deposition Modeling (FDM) and Stereolithography (SLA), are increasingly adopted in various high-demand sectors, including the aerospace, biomedical engineering, and automotive industries, due to their design flexibility and material adaptability. However, the tribological performance and surface integrity of parts manufactured by AM are the biggest functional deployment challenges, especially in wear susceptibility or load-carrying applications. The current review provides a comprehensive overview of the tribological challenges and surface engineering solutions inherent in FDM and SLA processes. The overview begins with a comparative overview of material systems, process mechanics, and failure modes, highlighting prevalent wear mechanisms, such as abrasion, adhesion, fatigue, and delamination. The effect of influential factors (layer thickness, raster direction, infill density, resin curing) on wear behavior and surface integrity is critically evaluated. Novel post-processing techniques, such as vapor smoothing, thermal annealing, laser polishing, and thin-film coating, are discussed for their potential to endow surface durability and reduce friction coefficients. Hybrid manufacturing potential, where subtractive operations (e.g., rolling, peening) are integrated with AM, is highlighted as a path to functionally graded, high-performance surfaces. Further, the review highlights the growing use of finite element modeling, digital twins, and machine learning algorithms for predictive control of tribological performance at AM parts. Through material-level innovations, process optimization, and surface treatment techniques integration, the article provides actionable guidelines for researchers and engineers aiming at performance improvement of FDM and SLA-manufactured parts. Future directions, such as smart tribological, sustainable materials, and AI-based process design, are highlighted to drive the transition of AM from prototyping to end-use applications in high-demand industries. Full article

Purely electric and hybrid vehicles are emerging as the transport sector’s response to meet climate goals, aiming to mitigate global warming. As the adoption of transport electrification increases, the importance of recycling components of the electric propulsion system at the end of their life grows, particularly the battery pack, which significantly contributes to the vehicle’s final cost and generates environmental impacts and CO₂ during production. This work presents an overview of the recycling processes for lithium-ion automotive batteries, emphasizing the developing Brazilian scenario and more established international scenarios. In Brazil, companies and research centers are investing in recycling and using reused cathode material to manufacture new batteries through the hydrometallurgical process. On the international front, pyrometallurgy and physical recycling are being applied, and other methods, such as direct processes and biohydrometallurgy, are also under study. Regardless of the recycling method, the main challenge is scaling prototype processes to meet current and future battery demand, driven by the growth of electric and hybrid vehicles, pursuing both environmental gains through reduced mining and CO₂ emissions and economic viability to make recycling profitable and support global electrification. Full article

Lean manufacturing (LM) is one of the strongest tools used by manufacturing companies to improve and optimize their processes, making them more performant and agile. The current industrial revolution, or Industry 4.0, aims to give new momentum to manufacturing systems through various technologies, of which Blockchain is one of them. This technology has gained significant attention for its ability to enhance transparency, traceability, and data security within manufacturing and supply chain operations, representing a valuable opportunity to enhance lean manufacturing tools. Firstly, this paper presents what lean manufacturing is. After that, it explores how Industry 4.0 technologies influence LM. Then, it examines the impact of blockchain on LM, paving the way for lean 4.0 by presenting a case study concerning the Kanaban method in the automotive sector. Finally, the summary and future research direction will be presented. Full article

Currently, the need for resource efficiency and CO₂ reduction is growing in industrial production, particularly in the automotive sector. To address this, the industry is focusing on lightweight components that reduce weight without compromising mechanical properties, which are essential for passenger safety. Hybrid designs offer an effective solution by combining weight reduction with improved mechanical performance and functional integration. This study focuses on a one-step manufacturing process that integrates forming and bonding of hybrid systems using compression molding. This approach reduces production time and costs compared to traditional methods. Conventional Post-Mold Assembly (PMA) processes require two separate steps to combine fiber-reinforced plastic (FRP) structures with metal components. In contrast, the novel In-Mold Assembly (IMA) process developed in this study combines forming and bonding in a single step. In the IMA process, glass-mat-reinforced thermoplastic (GMT) is simultaneously formed and bonded between two metal belts during compression molding. The GMT core provides stiffening and load transmission between the metal belts, which handle tensile and compressive stresses. This method allows to produce hybrid structures with optimized material distribution for load-bearing and functional performance. The process was validated by producing a lightweight hybrid brake pedal. Demonstrating its potential for efficient and sustainable automotive production, the developed hybrid brake pedal achieved a 35% weight reduction compared to the steel reference while maintaining mechanical performance under quasi-static loading Full article

Low-code development platforms (LCDPs) have emerged as transformative tools for accelerating digital transformation across industries by enabling rapid application development with minimal hand-coding. This paper synthesizes existing research and industry practices to explore the adoption, benefits, challenges, and future directions of low-code technologies in key sectors: automotive, equipment manufacturing, aerospace, electronics, and energy. Drawing on academic literature, industry reports, and case studies, this review highlights how low-code bridges the gap between IT and domain experts while addressing sector-specific demands. The study emphasizes the significant impact of LCDPs on operational efficiency, innovation acceleration, and the democratization of software development. However, it also identifies critical challenges related to customization, interoperability, security, and usability. The paper concludes with a discussion of emerging trends, including enhanced AI/ML integration, edge computing, open-source ecosystems, and sector-specific platform evolution, which are poised to shape the future of low-code development. Ultimately, this research underscores the potential of low-code platforms to drive sustainable digital transformation while addressing the complex needs of modern industries. Full article

China’s leading development of a complete battery value chain for electric vehicles (EVs) is restructuring the global automotive sector. In contrast with the normal point of view, which emphasizes the role of industrial policy, this article argues that the competitive advantage of China’s EV battery industry lies in firms’ core competency and political economic geography. Based on first-hand empirical material and data obtained from years of fieldwork carried out at an EV battery cluster in south China, this paper identifies the Chinese EV battery industry’s core competency and details how it is built up from below. The current core competency of Chinese battery firms is their mass manufacturing capability, which allows them to supply vehicle manufacturers (OEMs) with lithium-ion batteries of stable and consistent quality at competitive prices. This competency is acquired by firms through technological learning at the workshop level while making use of the experiences they have accumulated while mass producing batteries for consumer electronics sectors. Furthermore, the rapid learning and accumulation of knowledge of battery manufacturing on a large scale is also facilitated by the local industrial cluster environment where firms are embedded. Supported and promoted by local government policies, Chinese EV battery clusters are composed of firms from different segments of a complete battery value chain. The findings have significant implications for battery and car makers in global competition as well as for national and local governments which aim to promote EV battery development. Full article

An important step toward automation and digitization in Industry 4.0 is the automobile sector’s use of smart manufacturing integrated systems (SMISs). Although this change increases productivity and competitiveness, it also creates new hazards for workplace safety. Key issues include ergonomic and cognitive strain from greater human–machine interactions, particularly with collaborative robots (cobots), and cybersecurity threats from the IIoT and cyber–physical systems. This paper looks at these hazards and stresses the value of safety precautions like predictive maintenance, traceability, and real-time monitoring. This case study investigates how the integration of smart manufacturing integrated systems (SMISs) and cyber–physical systems (CPSs) within Industry 4.0 frameworks enhances workplace safety in the automotive sector. Through a comprehensive case study of the final assembly line, this research explores how these technologies contribute to predictive maintenance, real-time monitoring, and human–machine collaboration, leading to significant reductions in ergonomic and cybersecurity risks. Full article

This study aims to present an integrated approach to customer experience, which was developed considering the identification and application of essential factors from the product life cycle. The study was conducted in the automotive industry and may be transferable to other products with high complexity and medium–long in-service use. The main goal is to identify the determining factors and perform a regression analysis of the effect of attribute-level performance on overall customer satisfaction through the customer’s entire journey during the product development phase. This study is based on a generic example that is meant to capture trends influencing customer satisfaction in the launch of a new product vehicle, focusing on factors that influence each stage of the process, from planning–exploration, design and development, and manufacturing and validation to performance measurement and after-sales assistance. Based on multiple surveys that were used as the main instruments for measuring the level of customer satisfaction at defined touchpoints, the product life cycle was followed through several stages: prospecting survey, upstream survey, launch preparation survey, post-launch investigation, life cycle survey, and after-sales support. Three meta-factors were identified—design, price, and durability—for which the ordinal regression demonstrated that they are significant predictors of customer experience in general. The approach may be transferable to other sectors by identifying relevant attributes and adapting tools for measuring customer satisfaction, customer experience, and consumer concerns, which act as key vectors influencing the product life cycle and, by extension, business sustainability. Full article

In the context of Al-Kharj city, which is steadily advancing as an industrial and manufacturing hub within Saudi Arabia, this study has significant relevance. The city’s focus on metal forming, fabrication, and materials engineering makes it crucial to optimize processes such as hot deformation of metallic alloys for various sectors, including aerospace, automotive, oil and gas, and structural applications. By assessing and comparing phenomenological and physical material models for nickel, aluminum, titanium, and iron-based alloys, this study aids Al-Kharj industries in advancing their process simulation and predictive performance. Thus, this study aims to evaluate the proposed phenomenological and physically based constitutive models for Ni-, Al-, Ti-, and Fe-based alloys to enhance the accuracy of high-temperature deformation simulations. Phenomenological models investigated include the Johnson–Cook (JC), Fields and Backofen (FB), and Khan–Huang–Liang (KHL) formulations, while the Zerilli–Armstrong (ZA) model represents the physical category. Additionally, various modifications to these models are explored. Model parameters are calibrated using the Levenberg–Marquardt algorithm to minimize mean square error. Performance is assessed through key statistical metrics, including the correlation coefficient (R), average absolute relative error (AARE), and root mean square error (RMSE). Of the 32 models analyzed, a modified version of the JC model delivers the highest accuracy across all alloys. Furthermore, four other modifications, one each for the JC and ZA models and two for the FB model, exhibit superior predictive capability for specific alloys. This makes this study valuable not just academically, but also as a practical resource to boost Al-Kharj’s industrial competitiveness and innovation capacity. Full article