Manufacturing Strategy Research Articles

Preparation of Photocurable Organic-Inorganic Hybrid Composites for Continuous Manufacturing of 3D-Patterned Abrasive.

Photocurable hybrid organic-inorganic composites were prepared via surface modification and 3D-patterned structures were fabricated by utilizing a continuous roll-to-roll manufacturing strategy. The surfaces of nanocrystals were engineered with a bifunctional ligand that is a 2-carboxyethyl acrylate, which possesses a carboxylic acid moiety at one end and an acrylate functionality moiety at the other end, yielding acrylate-functionalized nanocrystals. Micro-scale 3D patterns (protruding pyramidal shapes with each side measuring 147 μm) were continuously manufactured at a speed of 2.5 m/min via UV curing with a soft engraved mold. The surface properties of the functionalized nanocrystals and their UV curing condition were explored with Fourier transform infrared spectroscopy. The morphology of the 3D film was measured using scanning electron microscopy. A pin-on-disk tribometer measurement revealed an improved interaction between the functionalized particles and resins.

(Invited) Electron Microscopy Investigation of PEM Electrolyzer Degradation at Low Iridium Loadings

The employment of hydrogen as an energy carrier provides a promising solution toward zero carbon emission and battling climate change. To meet the DOE’s Hydrogen Shot goal of $1 per kilogram of hydrogen by 2031, proton exchange membrane (PEM) water electrolysis is a key technology of hydrogen production from renewable energy sources. Successful deployment of PEM electrolyzers requires reducing cost while simultaneously improving performance and lifetime. While cost reduction is expected from a variety of strategies including scaling up, operation optimization, and advanced manufacturing, it remains vital to reduce the use of noble-metal catalyst due to its scarcity and vulnerable supply chain subject to market disturbance. [1]Reducing the loading of iridium catalyst on the anode while maintaining electrolyzer performance and durability is of great challenge. In this talk, we discuss issues in PEM electrolyzer component development and advanced manufacturing of electrode layers related to catalyst loading reduction with an emphasize on analytical electron microscopy which provides valuable insight on materials morphology, crystalline structure, and chemical compositions. The batch-to-batch variance of catalyst morphology and elemental composition/distribution from commercial suppliers will be examined for catalyst down-selection. We then analyze the effect of reducing Ir loading on the electrode layer structure and ionomer distribution. These results are correlated with electrochemical measurements in the electrolyzer cell, which help elucidate the effect of low catalyst loading on electrolyzer performance. Morphologies of degraded low-loading anode catalyst layer and gas recombination layers will be investigated, and the dissolution and migration of iridium will be studied. We will also examine impact of different roll-to-roll manufacturing strategies for low-loading catalyst layers and on electrolyzer durability. Last but not least, we analyze MEAs subject to cation contamination and recovery procedure with electron microscopy. [2]References[1] Alia, S.M., Current Opinion in Chemical Engineering 2021, 33:100703.[2] Funding for this work was provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy, Hydrogen and Fuel Cell Technologies Office through the H2NEW Consortium. Electron microscopy research conducted as part of a user project at the Center for Nanophase Materials Sciences (CNMS), which is a US Department of Energy, Office of Science User Facility at Oak Ridge National Laboratory.

Sawtooth scanning strategy for additive manufacturing

PurposeThis study aims to improve the acceleration in the additive manufacturing (AM) process. AM tools, such as extrusion heads, jets, electric arcs, lasers and electron beams (EB), experience negligible forces. However, their speeds are limited by the positioning systems. In addition, a thin tool must travel several kilometers in tiny motions with several turns while realizing the AM part. Hence, acceleration is a more significant limiting factor than the velocity or precision for all except EB.Design/methodology/approachThe sawtooth (ST) scanning strategy presented in this paper minimizes the time by combining three motion features: zigzag scan, 45º or 135º rotation for successive layers in G00 to avoid the CNC interpolation, and modifying these movements along 45º or 135º into sawtooth to halve the turns.FindingsSawtooth effectiveness is tested using an in-house developed Sand AM (SaAM) apparatus based on the laser–powder bed fusion AM technique. For a simple rectangle layer, the sawtooth achieved a path length reduction of 0.19%–1.49% and reduced the overall time by 3.508–4.889 times, proving that sawtooth uses increased acceleration more effectively than the other three scans. The complex layer study reduced calculated time by 69.80%–139.96% and manufacturing time by 47.35%–86.85%. Sawtooth samples also exhibited less dimensional variation (0.88%) than zigzag 45° (12.94%) along the build direction.Research limitations/implicationsSawtooth is limited to flying optics AM process.Originality/valueDevelopment of scanning strategy for flying optics AM process to reduce the warpage by improving the acceleration.

Reinforcement learning for distributed hybrid flowshop scheduling problem with variable task splitting towards mass personalized manufacturing

Mass personalization manufacturing (MPM), an emerging production pattern, aims to improve enterprise profit in modern industries. However, the processing of heterogeneous orders from the consumers complicates such production scheduling problem. In addition, different scale tasks should adopt different splitting strategies in practical manufacturing, which makes the task splitting method more worthy of investigation. Towards MPM, this paper presents a distributed hybrid flowshop scheduling problem with variable task splitting (DHFSP-VTS) to minimize the makespan and total energy consumption simultaneously. Meanwhile, the VTS allows the tasks to be split into different sublots so they can save setup and transfer time. To solve these problems, we present an order modularization processing method that can categorize multiple types of orders into specific generation tasks, and a highly effective reinforcement learning-multiple objective evolutionary algorithm based on decomposition (RL-MOEA/D) is designed. In RL-MOEA/D, there are three features: (1) three initial rules are used for initialization based on the current splitting scheme that can increase the diversity of solutions; (2) the reinforcement learning agent uses the Q-learning mechanism to dynamically select the scheme of task splitting as action; (3) a neighborhood search strategy improves the exploitation ability and expand the solution space. To verify the effectiveness of RL-MOEA/D, the MOEA/Ds based on four splitting schemes and four RL combined meta-heuristics are compared on 18 instances. The results show that RL-MOEA/D can obtain the best optimization and stability of all the other comparison algorithms. Therefore, it’s a new technique to solve DHFSP with large-scale tasks, especially for the problem of MPM.

3D Bioprinting Photo-Crosslinkable Hydrogels for Bone and Cartilage Repair.

Three-dimensional (3D) bioprinting has become a promising strategy for bone manufacturing, with excellent control over geometry and microarchitectures of the scaffolds. The bioprinting ink for bone and cartilage engineering has thus become the key to developing 3D constructs for bone and cartilage defect repair. Maintaining the balance of cellular viability, drugs or cytokines’ function, and mechanical integrity is critical for constructing 3D bone and/or cartilage scaffolds. Photo-crosslinkable hydrogel is one of the most promising materials in tissue engineering; it can respond to light and induce structural or morphological transition. The biocompatibility, easy fabrication, as well as controllable mechanical and degradation properties of photo-crosslinkable hydrogel can meet various requirements of the bone and cartilage scaffolds, which enable it to serve as an effective bio-ink for 3D bioprinting. Here, in this review, we first introduce commonly used photo-crosslinkable hydrogel materials and additives (such as nanomaterials, functional cells, and drugs/cytokine), and then discuss the applications of the 3D bioprinted photo-crosslinkable hydrogel scaffolds for bone and cartilage engineering. Finally, we conclude the review with future perspectives about the development of 3D bioprinting photo-crosslinkable hydrogels in bone and cartilage engineering.

Prioritization of lean manufacturing strategy for eliminating non-value adding activities through analytic hierarchy process

Prioritization of lean manufacturing strategy for eliminating non-value adding activities through analytic hierarchy process

Experimental investigations on the formation mechanisms of shrink lines in powder bed fusion of metals using a laser beam

The powder bed fusion of metals using a laser beam enables the tool-free fabrication of complex part geometries with merging areas and rapid cross-sectional changes. Together, these geometry features represent a structural transition leading to the formation of shrink lines. These notches on the surface of the part reduce the dimensional accuracy and the fatigue resistance. Shrink lines arise in various materials, with the dimensions of the shrink line depending on the geometric design. The formation mechanisms and influencing parameters of shrink lines have not been investigated yet. This paper demonstrates the extent of influence of the part geometry on the shrink line formation, which was quantified by varying the design of a representative structural transition. In addition, the positions of the specimens on the build platform and the scanning strategy were varied for deriving a cause-effect relationship using process monitoring. The results demonstrated that the shrink line formation was mainly caused by a local overheating at the structural transition and the global cooling behavior. The radius at the structural transition indicated the most significant impact among the investigated geometric parameters. The shrink line dimensions depended significantly on the orientation of the specimens on the build platform and the local scanning strategy applied at the height of the structural transition. The results can be used to reduce shrink lines by re-designing the part and to adjust the manufacturing strategy for structural transitions.

Development of a Quality Deterioration Index for Sustainable Quality Management in High-Tech Electronics Manufacturing

In high-tech electronics manufacturing, non-quality costs significantly impact organizational profitability and competitiveness. This case study introduces a novel Quality Deterioration Index (QDI) to systematically identify and prioritize root causes of non-quality costs within a leading electronics manufacturer. The primary objective is to integrate sustainable quality management practices that align with green sustainability objectives, such as reducing electronic waste, improving energy efficiency, and minimizing hazardous materials usage. Our comprehensive methodology encompasses a literature review, interviews, document analysis, and statistical analysis of survey data to uncover the influence of procedural, cultural, and environmental factors on quality deviations. The key findings reveal critical areas for improvement, particularly in supply chain inefficiencies, workforce challenges, and procedural gaps. By employing the QDI, we provide a structured framework that enhances both operational efficiency and environmental performance. The novelty of this research lies in its dual approach to simultaneously address economic and environmental performance, offering actionable insights for manufacturers aiming to integrate robust quality management systems with sustainability objectives. This study contributes to the ongoing dialogue on sustainable manufacturing strategies, underscoring the pivotal role of quality management in achieving both economic viability and environmental stewardship. Future research should expand this approach across various industries and global contexts to validate and refine the integration of quality management and sustainability.

How to optimize service-oriented cloud manufacturing

Within the context of Industry 4.0, service-oriented cloud manufacturing has emerged as a paradigm of significant interest, noted for enhancing manufacturing efficacy through integration, flexibility, and customization. The challenges of service selection and optimization, alongside transportation optimization, are pivotal for the effective implementation of service-oriented cloud manufacturing. This study explores an integrated problem that incorporates both sequential and parallel structures. To address this intricate issue, a binary-integer programming model is proposed, aiming to minimize the cumulative costs, including both manufacturing and transportation expenses. The validity and effectiveness of the proposed model are demonstrated through a real-world case study in mold manufacturing. Analysis of the experimental results provides managerial insights, which could inform the implementation and improvement of service-oriented cloud manufacturing strategies.

Antecedents and consequents of circular economy adoption: A meta-Analytic Investigation

This paper aims to integrate and empirically assess the antecedents and consequents of circular economy (CE) adoption to remove ambiguity existing in the literature and clarify divergent views. This study uses meta-analysis methodology to validate the research framework, considering 106 empirical studies with 210 effect sizes. Based on these studies, we establish twelve antecedents and three consequents related to CE. Antecedents are categorized in a technological-organizational-environmental framework and consequents in the sustainability outcomes. The result suggests that organisational factors are more prominent in driving CE practices, followed by environmental and technological factors. In the organisational category, the three most influencing factors are managing product returns, green manufacturing, and environmental strategy. In the environmental category, coercive pressure is the most influential factor, followed by mimetic and social pressures. Emerging I4.0 technologies are the most prominent factor in the technological category. Our study suggests that CE helps to achieve sustainable performance by significantly enabling economic, environmental, and social outcomes. This study further analyses how contextual factors such as national culture (masculinity) and economic regions influence the various relationships with CE using subgroup analysis. The moderation results show that low masculine culture and developing economies are more effectively using the I4.0 technologies to drive CE adoption than high masculine culture and developed economies. Additionally, different dimensions of sustainability are also influenced by the variations in masculinity and economic regions.

Carbon Micro/Nano Machining toward Miniaturized Device: Structural Engineering, Large-Scale Fabrication, and Performance Optimization.

With the rapid development of micro/nano machining, there is an elevated demand for high-performance microdevices withhigh reliability and low cost. Due to their outstanding electrochemical, optical, electrical, and mechanical performance, carbon materials are extensively utilized in constructing microdevices for energy storage, sensing, and optoelectronics. Carbon micro/nano machining is fundamental in carbon-based intelligent microelectronics, multifunctional integrated microsystems, high-reliability portable/wearable consumer electronics, and portable medical diagnostic systems. Despite numerous reviews on carbon materials, a comprehensive overview is lacking that systematically encapsulates the development of high-performance microdevices based on carbon micro/nano structures, from structural design to manufacturing strategies and specific applications. This review focuses on the latest progress in carbon micro/nano machining toward miniaturized device, including structural engineering, large-scale fabrication, and performance optimization. Especially, the review targets an in-depth evaluation of carbon-based micro energy storage devices, microsensors, microactuators, miniaturized photoresponsive and electromagnetic interference shielding devices. Moreover, it highlights the challenges and opportunities in the large-scale manufacturing of carbon-based microdevices, aiming to spark further exciting research directions and application prospectives.

Continuous Manufacturing of Bioinspired Bone‐Periosteum Integrated Scaffold to Promote Bone Regeneration

AbstractThe scaffold that bioinspired natural bone‐periosteum is ideal for the repair of bone defects, while the achievement of a gradient scaffold with an integrated and stable interface remains challenging. Herein, a bioinspired bone‐periosteum integrated collagen‐based scaffold is developed, in which the top layer is electrospun collagen‐dense scaffold as bioinspired periosteum (BP) to prevent invasion of reticular fiber tissue and the bottom layer is in situ mineralized collagen scaffold (IMCS) to promote osteogenic differentiation. Owing to the proposed continuous manufacturing of successive 3D printing and electrospinning, the integrated scaffold (BP‐IMCS) comprised of BP and IMCS demonstrates excellent structural stability, ten times higher than that of direct‐combination scaffolds. Besides, in vivo implantation results confirmed that BP‐IMCS significantly improves new bone formation up to 32.47%, better than an individual layer, due to its co‐work of mineral ions and bioinspired structure. Therefore, this study offers a continuous manufacturing strategy to realize the integrated and interface‐stable bone‐periosteum structure, providing new solutions for heterostructure tissue fabrication.

Advancements in diabetic foot insoles: a comprehensive review of design, manufacturing, and performance evaluation.

The escalating prevalence of diabetes has accentuated the significance of addressing the associated diabetic foot problem as a major public health concern. Effectively offloading plantar pressure stands out as a crucial factor in preventing diabetic foot complications. This review comprehensively examines the design, manufacturing, and evaluation strategies employed in the development of diabetic foot insoles. Furthermore, it offers innovative insights and guidance for enhancing their performance and facilitating clinical applications. Insoles designed with total contact customization, utilizing softer and highly absorbent materials, as well as incorporating elliptical porous structures or triply periodic minimal surface structures, prove to be more adept at preventing diabetic foot complications. Fused Deposition Modeling is commonly employed for manufacturing; however, due to limitations in printing complex structures, Selective Laser Sintering is recommended for intricate insole designs. Preceding clinical implementation, in silico and in vitro testing methodologies play a crucial role in thoroughly evaluating the pressure-offloading efficacy of these insoles. Future research directions include advancing inverse design through machine learning, exploring topology optimization for lightweight solutions, integrating flexible sensor configurations, and innovating new skin-like materials tailored for diabetic foot insoles. These endeavors aim to further propel the development and effectiveness of diabetic foot management strategies. Future research avenues should explore inverse design methodologies based on machine learning, topology optimization for lightweight structures, the integration of flexible sensors, and the development of novel skin-like materials specifically tailored for diabetic foot insoles. Advancements in these areas hold promise for further enhancing the effectiveness and applicability of diabetic foot prevention measures.

A cost modelling methodology based on machine learning for engineered-to-order products

Recent scientific studies are targeted at applying and assessing the effectiveness of Machine Learning (ML) approaches for cost estimation during the preliminary design phases. To train ML prediction models, comprehensive and structured datasets of historical data are required. This solution is inapplicable when such information is unavailable or sparse due to the lack of structured datasets. For engineered-to-order products, the number of historical records is often limited and strongly influenced by different purchasing or manufacturing strategies, thus requiring complex normalisation of such data.This method overcomes the above limitations by presenting an ML-based cost modelling methodology for the conceptual design that is applicable even when historical data are insufficient to train the prediction algorithms. The training dataset is generated through an analytical and automatic software tool for manufacturing cost estimation. Such a tool, starting from a 3D model of a product, can quickly and autonomously assess the related cost in different scenarios. An extensive and structured training dataset can be easily generated. The proposed methodology was based on CRISP-DM (Cross Industry Standard Process for Data Mining).Cost engineers of an Oil & Gas company used the method to develop parametric cost models for discs and spacers of an axial compressor. The solution guarantees lower error (7% vs 9%) and significant time-saving (minutes instead of hours) than estimations based on other approaches. Cost models are more comprehensive (capable of analysing different scenarios), explainable (not conceived as a black box), and self-learning (can be updated by extending the training dataset).

Political Economy of Manufacturing: Conventional and Nonconventional Practices in the Global Context

This paper examines the intricate interplay between political economy and manufacturing practices, encompassing both conventional and nonconventional methods in a global context. It explores how political and economic factors shape manufacturing strategies, with a focus on technological innovation, labor dynamics, sustainability, and regulatory frameworks. Comparative case studies highlight disparities between developed and developing economies, revealing distinct approaches to industrial policies and their impacts on economic outcomes. The findings underscore the importance of balancing traditional manufacturing strengths with advancements in Industry 4.0 technologies to enhance competitiveness and sustainability. Policy recommendations advocate for strategic government interventions that promote innovation, support workforce adaptation, and foster international collaboration in manufacturing sectors.

Laser Interference Additive Manufacturing: Mask Bundle Shape Bionic Shark Skin Structure.

Here, we explored a new manufacturing strategy that uses the mask laser interference additive manufacturing (MLIAM) technique, which combines the respective strengths of laser interference lithography and mask lithography to efficiently fabricate across-scales three-dimensional bionic shark skin structures with superhydrophobicity and adhesive reduction. The phenomena and mechanisms of the MLIAM curing process were revealed and analyzed, showing the feasibility and flexibility. In terms of structural performance, the adhesive force on the surface can be tuned based on the growth direction of the bionic shark skin structures, where the maximum rate of the adhesive reduction reaches about 65%. Furthermore, the evolution of the directional diffusion for the water droplet, which is based on the change of the contact angle, was clearly observed, and the mechanism was also discussed by the models. Moreover, no-loss transportations were achieved successfully using the gradient adhesive force and superhydrophobicity on the surface by tuning the growth direction and modifying by fluorinated silane. Finally, this work gives a strategy for fabricating across-scale structures on micro- and nanometers, which have potential application in bioengineering, diversional targeting, and condenser surface.

3D volume construction methodology for cold spray additive manufacturing

Cold spraying has proved as an attractive and rapidly developing solid-state material deposition process that allows for fast formation of high quality, large 3D volume objects. Low risks of undesirable heat effects lead to increased interest in cold spraying based rapid additive manufacturing. However, by continuous powder spraying and high-pressure gas operation, cold spray additive manufacturing in terms of shape building is sensitive to operating parameters and imposes high requirements on the control of process conditions and locally needed kinematics. Every step of the manufacturing process therefore needs to be meticulously conceived and planned, especially the toolpath planning and implementation. This is not only essential to meet basic performance requirements, but also needed for the desired geometric accuracy. To address these needs, this study presents a new implementation method for cold spray additive manufacturing to improve manufacturing accuracy and flexibility. The workflow and principles of the proposed method are explained and strategies are presented for building up various component types, including rotational geometries as well as freestanding parts. The developed algorithms for 3D applications can be well integrated into the cold spray additive manufacturing process for toolpath planning and path parameter determination. Reliable and accurate robot programs were developed to make the process easy to repeat and to follow. Toolpath planning and robot programming are supported and performed by applying virtual cells to simulate the automation process for the real scenario. Path parameter adjustment and kinematic optimization can then be conducted according to the feedback from the simulation result. Applied benchmarking tests prove acceptable shape accuracy and demonstrate that the current method can enhance the capabilities of cold spray additive manufacturing for near-net shape construction. This implies that careful planning and manufacturing strategies should enable overcoming the challenges associated with cold spray additive manufacturing.

Cerium oxide nanoparticles formation and aggregation dynamics

The conventional synthesis of cerium oxide nanoparticles (CeNPs) relies on the use of trivalent cerium and an oxidizer to ensure a thorough hydrolysis. This study explores CeNPs formation via the hydrolysis of tetravalent cerium, specifically through the spontaneous reaction of ceric ammonium nitrate (Ce(NH4)2(NO3)6) under varied aging conditions, including adjustments in pH, concentration, and heating strategy. This research adopts a thermo-hydrolysis method to synthesize CeNPs with small primary particles (2–3 nm) to loose dendritic clusters (5–8 nm). Additionally, when the starting pH of the solution decreases below 0.8, it favors the formation of clusters with larger diameters (15–20 nm). Analyses using dynamic light scattering (DLS), high-resolution transmission electron microscopy (HRTEM), and X-ray photoelectron spectroscopy (XPS) indicate that CeNPs particle size in acidic environments is predominantly determined by the extent of primary particle aggregation, influenced by nitrate adsorption. Overall, our comprehensive analysis of the CeNPs evolution provides fundamental insights for the development of a size/structure controllable synthetic strategy for nanoparticle manufacturing.

Inosine pranobex-derived coordination complexes for self-adjuvant, self-carrier, and self-assembled vaccines in cancer immunotherapy

Inosine pranobex (InP), an immunomodulatory drug approved for clinical use worldwide, is a potential adjuvant for cancer vaccines with a low clinical translation barrier. However, the administration of soluble InP agents in free format is associated with drawbacks of a short half-life period and limited efficacy. In this study, a rapid self-assembly approach was proposed for the synthesis of cancer vaccines due to the coordination interaction of ferric ion and acedoben (Ace) in InP. The self-assembled cancer vaccines can be readily adjusted from the microscale to the nanoscale, with shapes including rods, fibers, and spheres. Herein, Fe-Ace coordination complexes not only serve as carriers for tumor antigens, but also enhance antigen-specific antitumor immunity due to their intrinsic adjuvant properties. Nanofibrous self-assembled cancer vaccines based on Fe-Ace coordination complexes more effectively induced an antitumor immune response, exerted a remarkable therapeutic effect, and inhibited the tumor growth at distant site in vivo, compared to free InP and other self-assembled cancer vaccines. The presented work provides a promising strategy for the rapid manufacture of InP-derived self-adjuvant, self-carrier, and self-assembled vaccines for cancer immunotherapy.

Selection and Production of a Suitable Support for Palladium Membranes

The present work focuses on establishing anactual manufacturing strategy that is suitable for the viableproduction of a substrate support for a palladiummembrane; one that will lower production costs and beutilised to gain a competitive edge within the industry.Porous stainless steel supports for palladium membraneswere produced using various types of stainless steel powdersand methods. Observations indicate that SS powder 410Ltype Chengdu Huarui with particle sizes 1-5 μm, sintered inthe HPT induction furnace at temperatures between 930-950°C for a period of 8 minutes, in the presence of argonflow and vacuum, creates a first-class Pd membranesubstrate. Notwithstanding that this result is the mosteconomical method. The results of the study were based onthe: roughness test, first bubble point test, and scanningelectron microscope (SEM). The Chengdu Huarui powdertype 410 L – 1-5 μm, which achieved the best results,produced a roughness test result of Ra 0.614 μm, and abubble point pore size is 1.992 μm.