Manufacturing Paradigm Research Articles

Foundation models assist in human–robot collaboration assembly

Human–robot collaboration (HRC) is a novel manufacturing paradigm designed to fully leverage the advantage of humans and robots, efficiently and flexibly accomplishing customized manufacturing tasks. However, existing HRC systems lack the transfer and generalization capability for environment perception and task reasoning. These limitations manifest in: (1) current methods rely on specialized models to perceive scenes; and need retraining the model when facing unseen objects. (2) current methods only address predefined tasks, and cannot support undefined task reasoning. To avoid these limitations, this paper proposes a novel HRC approach based on Foundation Models (FMs), including Large Language models (LLMs) and Vision Foundation Models (VFMs). Specifically, a LLMs-based task reasoning method is introduced, utilizing prompt learning to transfer LLMs into the domain of HRC tasks, supporting undefined task reasoning. A VFMs-based scene semantic perception method is proposed, integrating various VFMs to achieve scene perception without training. Finally, a FMs-based HRC system is developed, comprising perception, reasoning, and execution modules for more flexible and generalized HRC. The superior performances of FMs in perception and reasoning are demonstrated by extensive experiments. Furthermore, the feasibility and effectiveness of the FMs-based HRC system are validated through an part assembly case involving a satellite component model.

From Automation to Augmentation: Exploring the Role of Industry 5.0 Advanced Technologies for Developing Countries

Industry 4.0 (I4.0) has revolutionized manufacturing by bringing automation of processes through digital technologies. However, it fails to explain how machines and humans will work together in the future. Industry 5.0 (I5.0) is an attempt to move toward a manufacturing paradigm where the strength of humans and machines complements each other to move toward the production regime, which is more human-centered and finally results in a sustainable industrial environment. Therefore, I5.0 has been growing quite recently in terms of its industrial applications and academic publications. It has emerged as a newer manufacturing paradigm that integrates advanced technologies in a working ecosystem that is mostly human-centric. The ongoing I5.0 transition has changed the way industries will operate. Its implications for developing countries will be far more effective. The paper discusses that developing countries need to catch up with the developed world to move ahead for a sustainable future. The current study is aimed at using a systematic literature review of 142 Scopus-indexed articles published from 2018 to 2023 to discuss the existing opportunities for I5.0 enabling technologies that developing countries need to adopt, various barriers and challenges that may hinder their progress, and finally the future research agenda for developing countries.

Most important performance evaluation methods of production lines: A comprehensive review on historical perspective and emerging trends

Production is one of the most significant building blocks that strengthen the sustainable economy of companies and thus contribute to the countries’ welfare. Performance indicators of the production line affect planning operations and the efficiency of the supply chain to which the factory is connected. The key indicators for production line designers and performance analysts to monitor and improve include production rate, resource utilization rate, and average inventory level. The production rate is the most important indicator closely affecting an industrial plant's productivity and efficiency levels. From this perspective, accurate and fast estimation of this indicator is very critical. Production rate can be calculated by simulation, analytical technique, or artificial intelligence methods according to the production line characteristics. In this comprehensive review, the most important performance evaluation methods are discussed historically and systematically about the buffer allocation problem using the snowball sampling method. With this explicit motivation, 145 papers were reviewed and classified according to production line topology, hypothetical/real-case line, machine reliability, previous method on which the method is based, and originality and/or line characteristics. To present a comprehensive comparison, the methods considered were analyzed according to different criteria. This review provides general/in-depth qualitative and quantitative discussions and highlights insights to practitioners and scholars. In addition, the impact of recent key work on production line analysis in the field is assessed along with emerging trends, evolving manufacturing paradigms are discussed, and the challenges associated with performance analysis are addressed.

Evaluation of the effect of forming strategy in newly introduced flexible roll forming process

In recent years, with the emergence of Industry 4.0 trends and the impact of the COVID-19 pandemic, the agile manufacturing paradigm has gained increasing significance. Substantial efforts have been directed towards introducing new methods, driven by the dual objectives of flexibility and agility. This paper presents an innovative machine that employs two rollers to apply localized deformation to sheet metal through repetitive movement along the length of the sheet. As with all incremental forming processes, the forming strategy is a critical parameter influencing both the forming force and the dimensional accuracy of the manufactured workpiece. In this research, two different forming strategies, internal and external, were implemented for manufacturing elongated 90 deg bends on 1 mm and 3 mm thick AISI 304 sheets. Both numerical and experimental analyses were performed to assess the effects of these different strategies. The results confirm that the forming force with the external strategy is 50 % and 47 % less for the 1 mm and 3 mm sheets, respectively. Moreover, the external forming strategy allows for better control of the obtained angle, but limits the minimum obtainable bending radius.

Expert opinion aggregation-based decision support for human-robot collaboration digital twin maturity assessment

Human-centered smart manufacturing is an essential direction for the future development of manufacturing. Safe and reliable smart human-robot collaboration is the foundation for realizing human-centered smart manufacturing. Digital twin-based human-robot collaboration has been proposed as a new manufacturing paradigm to devise collaborative strategies, simulate collaborative processes, and ensure worker safety. Establishing a maturity model is essential to accurately assess the capabilities of the constructed human-robot collaboration digital twin. This paper aims to contribute to the formalization and standardization of the human-robot collaboration digital twin. It constructs a novel assessment framework for the overall maturity measurement of existing digital twin-based human-robot collaboration projects. The developed human-robot collaboration digital twin maturity model includes 5 evaluation dimensions and 24 evaluation factors. Additionally, 5 maturity levels and their definitions are defined for each evaluation factor for maturity scoring. The expert opinion aggregation approach is proposed to quantify the evaluation factor metrics and ultimately to obtain a maturity level for the human-robot collaboration digital twin. The effectiveness and feasibility of the proposed method are verified through a collaborative assembly case study. This paper provides a generic method for assessing the competency level of human-robot collaboration digital twins, which can provide insights into the maturity of digital twins for practitioners in the human-robot collaboration field to develop targeted strategies for optimizing and upgrading human-robot collaboration digital twins.

Harmonising humans and technology: Exploring the dynamics of cognitive production, artificial intelligence and social communication in cybernetic systems

Agile cognitive production systems mark a manufacturing paradigm shift, propelled by the demand for accelerated product development and the adoption of digitalised production systems across extensive supply networks. Cognitive manufacturing emphasises the role of technology and automation in the learning and adaptation process. These systems independently analyse data, make real-time adjustments and optimise processes, sometimes minimising the need for human intervention. Based on a conceptual framework that draws on the diversity of living systems and cognitive processes, cybernetics provides a solid theoretical background. It explores the intricate connections between cognition, self-organising systems and the challenges arising from the autonomy of such systems. The concept of "cognition" in "agile cognitive systems" moves away from the conventional understanding of purely technical processes and towards human thought processes. This departure fosters a dynamic exchange where individual thoughts resonate in social communication. Addressing the role of artificial intelligence (AI), the article emphasises examining computers from a social science standpoint, exploring the relationship between computers and mental systems, capturing human faculties such as cognition, utterance, and understanding. The integration of AI into computer-mediated communication leads to the question how AI-equipped computers intersect with societal intelligence notions. The inherent intransparency of AI, often viewed as a black box, prompts queries about the potential black-box nature of an autonomously controlled AI factory or supply chain. In this hypothetical scenario, the idea of the supply chain as a communication network is challenged, emphasising the importance of human involvement. Research on human-centric cognitive production emphasises explainable AI and human-in-the-loop. This orientation goes beyond the technical dimensions and incorporates social science considerations, which emphasises the holistic nature of current research. In essence, research in the field of cognitive production is a comprehensive exploration of the complex interplay between human cognition, artificial intelligence and the evolving landscape of modern production systems.

A New Paradigm for Semiconductor Manufacturing: Integrated Synthesis, Delivery, and Consumption of Source Chemicals for IC Fabrication

The semiconductor industry is being radically impacted by the placing of greater emphasis on the development of hetero-devices and systems that will act as essential drivers for a wide spectrum of technological applications. The introduction of new materials and their integration with currently used materials are projected to replace integrated circuitry (IC) design and device scaling as the key enablers to the realization of improved device performance and larger density gains. Yet material selection has been constrained by existing fabrication process technology. To date, fabrication processes have dictated material selection by limiting chemical sources or precursors to those that match existing process tools associated with chemically based vapor phase processes and their variants, which in turn limits material compositions in ICs. The processing and integration of new materials compositions and structures will require the introduction of new deposition and etching processes, and manufacturing worthy tool designs and associated protocols that provide new methods for atomic-level control. To this end, a novel manufacturing paradigm is presented comprising a method and system for real-time, closed-loop monitoring and control of synthesis, supply, and consumption of precursors in process intensification techniques including chemical vapor deposition (CVD), atomic layer deposition (ALD), atomic layer etching (ALE), and other IC manufacturing processes. This intelligent automated manufacturing approach is consistent with a central component of the semiconductor industry’s recent adoption of Industry 4.0., including vertical integration of IC manufacturing through robotization, artificial intelligence, and cloud computing. Furthermore, the approach eliminates several redundant steps in the synthesis, handling, and disposal of source precursors and their byproducts for CVD, ALD, ALE and other chemically based manufacturing processes, and thus ultimately lowers the manufacturing cost for both conventional and new IC materials. Further, by eliminating the issues associated with precursor thermal, chemical, and pyrophoric instabilities, this new paradigm enables the deposition of a myriad of new thin-film materials and compositions for IC applications that are practically unattainable with existing precursors. Preliminary and planned demonstration examples for the generation and deposition of highly toxic and unstable source precursors are provided.

Deep learning based assembly process action recognition and progress prediction facing human-centric intelligent manufacturing

With the breakthroughs in cutting-edge technologies and the growing demand for personalized services, the approach of full automation alone is no longer sufficient to meet rapidly changing production needs. In light of this, a new human-centric manufacturing (HCM) paradigm has been put forward. Industrial human action recognition, as one of the key means of realizing HCM, has received widespread attention and research in recent years. In this paper, an end-to-end deep learning-based action recognition and progress prediction model was built, which can not only recognize action classes in real-time, but also accurately predict action completion times, to improve the efficiency and safety of HCM systems. First, a non-contact data acquisition system based on Azure Kinect sensor, MediaPipe and You-Only-Look-Once V5 (Yolo V5) algorithm was constructed, achieving comprehensive perception of human and objects in manufacturing system. Second, an Auto-encoder model was established to learn and compress high-dimensional human joint and object data into low-dimensional space to achieve efficient representation of the operation process. Finally, the human action recognition and progress prediction model was built and evaluated in a reducer assembly line, with an accuracy rate for action recognition of 99 %, and a time error for progress prediction of less than 6.9 %. This research aims to provide guidance for improving the level of intelligence in manufacturing and the application of AI technology in the industrial field.

Intelligent Manufacturing Engineers’ Knowledge Transfer and Innovation Capability: From the Perspective of Big Data Acceptance Attitude

In the face of intelligent manufacturing (or smart manufacturing) human resource shortage, the training of industrial engineers in the field of intelligent manufacturing is of great significance. In academia, the positive link between learning transfer and knowledge innovation is recognized by most scholars, while the learner’s attitude toward big data decision-making, as a cognitive perception, affects learning transfer from the learner’s experienced engineering paradigm to the intelligent manufacturing paradigm. Thus, learning transfer can be regarded as a result of the learner’s attitude, and it becomes the intermediary state between their attitude and knowledge innovation. This paper reviews prior research on knowledge transfer and develops hypotheses on the relationships between learner acceptance attitude, knowledge transfer, and knowledge innovation.

Self-X heterogeneous attributed graph embedding-based product configuration framework for cognitive mass personalization

Cognitive mass personalization (CMP) is a promising manufacturing paradigm; equipped with cognitive capabilities like reasoning, CMP satisfies changeable needs via configuring personalized products at scale. In CMP, knowledge graphs (KGs) are exploited by smart product-service systems (SPSS) to support cognitive configuration/reconfiguration processes. However, the extant KG-enabled SPSSs are built upon fixed configurations and hybrid frameworks due to lacking a graph embedding (GE) model to render cognitive configuration decisions. In fact, GE is scarcely used in SPSS configuration, because it is not only compromised by the heterogeneity of KGs entailed by content-related specifications and complex structures but also influenced by the feature randomness and feature drift problems, which are triggered by accumulative errors and inconsistent objectives due to noisy assignments and different configuration tasks, separately. To address these limitations, a Self-X Heterogeneous Attributed Graph Embedding (SXHAGE) model is proposed in a Self-X architecture, which includes 1) self-attention graph attention networks, 2) a self-adaptive autoencoder, and 3) self-optimizing training objectives, to present heterogeneous data through jointly optimizing heterogeneous attributed entities and relations. A systematic SXHAGE-based configuration framework, in which product family design and configuration recommending are enabled by graph clustering and link prediction, is developed as a continuous updating loop to proactively configure personalized products. A real-world case study, i.e., configure personalized electric clippers via a web-based sustainable configuration platform, is performed to validate the applicability of the proposed framework in the CMP context. Moreover, extensive experiments on the case study dataset demonstrate the superiority of SXHAGE over the state-of-the-art algorithms, e.g., surpassing Deep Neighbor-Aware Embedding (DNENC) by 18 % in F1-score for graph clustering and by 5 % in ROC-AUC for link prediction.

Digitally enhanced lubricant evaluation and improvement framework through developing digital characteristics (DC) for hot forging of aluminium alloys

The manufacturing sector is experiencing a never-before-seen surge in data generation, acquisition, and analytics. The promising potential of fundamental research following a data-driven approach may enable a more comprehensive understanding of forming operations and efficient optimisation of component quality. However, as a crucial component of metal forming operations, the investigation and insights from a data-centric perspective of hot forging processes is still absent. In the present study, the digital characteristics (DC) of the hot forging process was generated based on voluminous metadata extracted from experimentally verified FE simulations and localised sensors. Inherent and distinctive manufacturing nature throughout the life cycle of a hot-forged product have been revealed, spanning over the design, manufacturing, and application stages. The tribological DC was then extracted and analysed, and the data-guided interactive friction modelling was established to enable a digitally enhanced evaluation and improvement scheme of the lubricant product applied during the hot forging process. Significant potential has been demonstrated in implementing data-centric innovation techniques into traditional manufacturing paradigms to improve efficiency and process effectiveness.

S-Tune: SOT-MTJ manufacturing parameters tuning for securing the next generation of computing

Hardware-based acceleration approaches for Machine Learning (ML) workloads have been embracing the significant potential of post-CMOS switching devices to attain reduced footprint and/or energy-efficient execution relative to transistor-based GPU and/or TPU-based accelerator architectures. Meanwhile, the promulgation of fabless IC chip manufacturing paradigms has heightened the hardware security concerns inherent in such approaches. Namely, unauthorized access to various supply chain stages may expose significant vulnerabilities resulting in malfunctions including subtle adversarial outcomes via the malicious generation of differentially-corrupted outputs. Whereas the Spin-Orbit Torque Magnetic Tunnel Junction (SOT-MTJ) is a leading spintronic device for use in ML accelerators, as well as holding security tokens, their manufacturing-only security exposures are identified and evaluated herein. Results indicate a novel vulnerability profile whereby an adversary without access to the circuit netlist could differentially-influence the machine learning application’s behavior. Specifically, ML recognition outputs can be significantly swayed via a global modification of oxide thickness (Tox) resulting in bit-flips of the weights in the crossbar array, thus corrupting the recognition of selected digits in MNIST dataset differentially creating an opportunity for an adversary. With just 0.05% of bits in crossbar having a flipped resistance state, digits “4” and “5” show the highest overall error rates, and digit “9” exhibit the lowest impact, with recognition accuracy of digits “2,” “3,” and “8” unaffected by changing the oxide thickness of SOT-MTJs uniformly from 0.75 nm to 1.2 nm without modifying the netlist nor even having access to the circuit design itself. Exposures and mitigation approaches to such novel and potentially damaging manufacturing-side intrusions are identified, postulated, and quantitatively assessed.

Production routing problem in shared manufacturing: Robust chance-constrained formulation and multi-diversification based matheuristic

Building on the concept of the sharing economy, which capitalizes on the advancements in information and manufacturing technologies, the shared manufacturing paradigm has tremendous potential for enhancing the performance of production–distribution systems and has become prevalent in the today’s manufacturing industry. Despite the extensive research on the production routing problem, which integrates decisions of production, inventory, and distribution routing, there is a notable absence of investigation within the context of shared manufacturing. This paper aims to bridge the above-mentioned research gap and develop valuable decision-support tools for managers, assisting efficient management of production–distribution systems in shared manufacturing environments. To achieve this, this paper introduces a general model for the production routing problem with shared manufacturing resources. The problem is formulated as a robust chance-constrained model to address uncertainties arising in customer demand and the demand and supply of shared manufacturing resources. Subsequently, a computationally tractable formulation is derived, for which a matheuristic that incorporates three diversification techniques is proposed to solve. Extensive computational experiments are conducted to validate the feasibility and effectiveness of both the model and the matheuristic, as well as to evaluate the benefits of manufacturing resource sharing. The experimental results demonstrate that our matheuristic outperforms state-of-the-art solution approaches. Moreover, manufacturing resource sharing can lead to significant reductions in operational costs and improvements in service levels. Finally, the advantages are found to increase as manufacturing resource unit purchasing costs decrease, demand fluctuations decrease, and production interruption severity increases.

Multi-agent deep reinforcement learning for dynamic reconfigurable shop scheduling considering batch processing and worker cooperation

Reconfigurable manufacturing system is considered as a promising next-generation manufacturing paradigm. However, limited equipment and complex product processes add additional coupled scheduling problems, including resource allocation, batch processing and worker cooperation. Meanwhile, dynamic events bring uncertainty. Traditional scheduling methods are difficult to obtain good solutions quickly. To this end, this paper proposes a multi-agent deep reinforcement learning (DRL) based method for dynamic reconfigurable shop scheduling problem considering batch processing and worker cooperation to minimize the total tardiness cost. Specifically, a dual-agent DRL-based scheduling framework is first designed. Then, a multi-agent DRL-based training algorithm is developed, where two high-quality end-to-end action spaces are designed using rule adjustment, and an estimated tardiness cost driven reward function is proposed for order-level scheduling problem. Moreover, a multi-resource allocation heuristics is designed for the reasonable assignment of equipment and workers, and a batch processing rule is designed to determine the action of manufacturing cell based on workshop state. Finally, a strategy is proposed for handling new order arrivals, equipment breakdown and job reworks. Experimental results on 140 instances show that the proposed method is superior to scheduling rules, genetic programming, and two popular DRL-based methods, and can effectively deal with various disturbance events. Furthermore, a real-world assembly and debugging workshop case is studied to show that the proposed method is applicable to solve the complex reconfigurable shop scheduling problems.

Artificial intelligence enabled smart design and manufacturing of advanced materials: The endless Frontier in AI+ era

AbstractFuture‐oriented Science & Technology (S&T) Strategies trigger the innovative developments of advanced materials, providing an envision to the significant progress of leading‐/cutting‐edge science, engineering, and technologies for the next few decades. Motivated by Made in China 2025 and New Material Power Strategy by 2035, several key viewpoints about automated research workflows for accelerated discovery and smart manufacturing of advanced materials in terms of AI for Science and main respective of big data, database, standards, and ecosystems are discussed. Referring to classical toolkits at various spatial and temporal scales, AI‐based toolkits and AI‐enabled computations for material design are compared, highlighting the dominant role of the AI agent paradigm. Our recent developed ProME platform together with its functions is introduced briefly. A case study of AI agent assistant welding is presented, which is consisted of the large language model, auto‐coding via AI agent, image processing, image mosaic, and machine learning for welding defect detection. Finally, more duties are called to educate the next generation workforce with creative minds and skills. It is believed that the transformation of knowledge‐enabled data‐driven integrated computational material engineering era to AI+ era promotes the transformation of smart design and manufacturing paradigm from “designing the materials” to “designing with materials.”

Advancements in Hybrid Additive Manufacturing: Integrating SLM and LMD for High-Performance Applications

Additive manufacturing (AM) technologies enable near-net-shape designs and demand-oriented material usage, which significantly minimizes waste. This points to a substantial opportunity for further optimization in material savings and process design. The current study delves into the advancement of sustainable manufacturing practices in the automotive industry, emphasizing the crucial role of lightweight construction concepts and AM technologies in enhancing resource efficiency and reducing greenhouse gas emissions. By exploring the integration of novel AM techniques such as selective laser melting (SLM) and laser metal deposition (LMD), the study aims to overcome existing limitations like slow build-up rates and limited component resolution. The study’s core objective revolves around the development and validation of a continuous process chain that synergizes different AM routes. In the current study, the continuous process chain for DMG MORI Lasertec 65 3D’s LMD system and the DMG MORI Lasertec 30 3D’s was demonstrated using 316L and 1.2709 steel materials. This integrated approach is designed to significantly curtail process times and minimize component costs, thus suggesting an industry-oriented process chain for future manufacturing paradigms. Additionally, the research investigates the production and material behavior of components under varying manufacturing processes, material combinations, and boundary layer materials. The culmination of this study is the validation of the proposed process route through a technology demonstrator, assessing its scalability and setting a benchmark for resource-efficient manufacturing in the automotive sector.

Triaxial mechanical characterization of ultrasoft 3D support bath-based bioprinted tubular GelMA constructs

Support bath-based extrusion bioprinting is an emerging additive manufacturing paradigm that allows for the creation of geometrically complex three-dimensional tissue-mimicking constructs. Although this approach enables arbitrary geometries to be printed, preserving shape and mechanical integrity after the construct is extracted from the support bath is one of the main challenges today. In the present study, we systematically tested the effect of critical printing parameters on construct integrity and stiffness. Specifically, we varied the concentration and temperature of the bioink and support bath material, hydrogel crosslinking time, and cell density of the bioink. While previous studies on hydrogel materials quantify construct properties based on a single loading mode-such as uniaxial testing or rheological experiments, for example—we used a multiaxial mechanical testing protocol to assess the printed construct’s response in tension, compression, and shear. For each sample, we determined a single set of material parameters by simultaneously fitting all three modes in our inverse finite element framework. In support of future modeling work, we analyzed three different constitutive models with respect to their ability to match the construct’s mechanical response. We observed that GelMA concentration, temperature, and cell density have a statistically significant effect on the construct stiffness; support bath temperature and UV crosslinking time have a weak effect on construct stiffness; and support bath concentration appears to have no direct effect on the measured construct properties. Our construct stiffness were found to vary between 0.07 and 2.2 kPa depending on printing conditions, and showed noticeable tension–compression asymmetry as well as pronounced nonlinear behavior when loaded under shear.

Mapping the hot stamping process through developing distinctive digital characteristics

Structural components produced through hot stamping of lightweight materials, such as aluminium alloys, play a pivotal role in mass reduction, leading to decreased CO2 emissions and enhanced fuel efficiency, especially in applications such as electric vehicles, high-speed trains, and aircraft. Concurrently, the hot stamping process is experiencing an exponential increase in data generation, stemming from ongoing production, research, and development activities. Yet, translating the inherent values of these voluminous metadata into scientific innovations and industrial breakthroughs requires the emerging expertise by consolidating the knowledge of hot stamping and data science. Here, the authors have conceptualised and developed the digital characteristics (DC) for manufacturing processes. The DC serves as the ‘DNA’ of every manufacturing process by encompassing its inherent and distinctive natures spanning over the design, manufacturing and application phases of the manufactured products. Focusing on the hot stamping process, the authors have developed the unique DC from voluminous hot stamping data derived from experimentally validated simulations and sensing networks. Results demonstrate that the DC revealed the distinct evolutionary thermo-mechanical characteristics of the hot stamping process in terms of representative geometric features, which facilitates the fundamental scientific understanding and unlocks the potential on implementing data-centric scientific innovations in advanced manufacturing paradigms.

Reverse logistics platform decisions integrating crowdsourced contracting: A three-level interactive optimization approach for product design considering remanufacturing

Currently most original equipment manufacturers are reluctant to remanufacture their end-of-life products due to lack of adequate infrastructure for remanufacturing profitable products. To solve the problem, we propose a three-level interactive decision-making framework for product design considering remanufacturing by establishing reverse logistics platform integrating crowdsourced contracting. The reverse logistics platform can incorporate a large population of manufacturer and consumer crowds in an extended manufacturing network. The crowdsourced manufacturing paradigm can maximize the utilization of external resources, thereby enhancing overall remanufacturing efficiency. We propose a three-level mixed 0–1 programming model based on above framework with a Stackelberg game to handle the complex decision-making process. A combination of analytical solutions and a nested genetic algorithm is developed to implement the model. A laptop case study is presented. To verify the effectiveness of our algorithm, we compare the results of the three algorithms in solving the model proposed in this paper. Furthermore, applying our proposed model to three additional cases of varying levels of complexity further validates the applicability of the model for our problem over traditional integrated optimization models and two-stage optimization models. Sensitivity analyses are conducted on three key parameters of the model, providing valuable insights for both industry and academia.

Micronano Synergetic Three-Dimensional Bioelectronics: A Revolutionary Breakthrough Platform for Cardiac Electrophysiology.

Cardiovascular diseases (CVDs) are the leading cause of mortality and therefore pose a significant threat to human health. Cardiac electrophysiology plays a crucial role in the investigation and treatment of CVDs, including arrhythmia. The long-term and accurate detection of electrophysiological activity in cardiomyocytes is essential for advancing cardiology and pharmacology. Regarding the electrophysiological study of cardiac cells, many micronano bioelectric devices and systems have been developed. Such bioelectronic devices possess unique geometric structures of electrodes that enhance quality of electrophysiological signal recording. Though planar multielectrode/multitransistors are widely used for simultaneous multichannel measurement of cell electrophysiological signals, their use for extracellular electrophysiological recording exhibits low signal strength and quality. However, the integration of three-dimensional (3D) multielectrode/multitransistor arrays that use advanced penetration strategies can achieve high-quality intracellular signal recording. This review provides an overview of the manufacturing, geometric structure, and penetration paradigms of 3D micronano devices, as well as their applications for precise drug screening and biomimetic disease modeling. Furthermore, this review also summarizes the current challenges and outlines future directions for the preparation and application of micronano bioelectronic devices, with an aim to promote the development of intracellular electrophysiological platforms and thereby meet the demands of emerging clinical applications.