Lihui Wang

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Verified

Lihui verified their affiliation via an institutional email.

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• PhD
• Chair Professor at KTH Royal Institute of Technology

Robotic welding envisioned for the future of factories will promote high-demanding and customised tasks with overall higher productivity and quality. Within the context, robotic welding parameter prediction is essential for maintaining high standards of quality, efficiency, safety, and cost-effectiveness in smart manufacturing. However, data acquis...

Industrial robots (IRs) serve as critical equipment in advanced manufacturing systems. Building high-fidelity digital twin models of IRs is essential for various applications like precision simulation, and intelligent operation and maintenance. Despite technological potentials of digital twins, existing modeling methods for industrial robot digital...

Dynamic order picking has usually demonstrated significant impacts on production efficiency in warehouse management. In the context of an automotive-part warehouse, this paper addresses a dynamic multi-tour order-picking problem based on a novel attention-aware deep reinforcement learning-based (ADRL) method. The multi-tour represents that one orde...

In the context of human-centric smart manufacturing, human-robot collaboration (HRC) systems leverage the strengths of both humans and machines to achieve more flexible and efficient manufacturing. In particular, estimating and monitoring human motion status determines when and how the robots cooperate. However, the presence of occlusion in industr...

Digital Twin (DT) of a manufacturing system mainly involving materials and machines has been widely explored in the past decades to facilitate the mass customization of modern products. Recently, the new vision of Industry 5.0 has brought human operators back to the core part of work cells. To this end, designing human-centric DT systems is vital f...

The proliferation of data silos poses a significant impediment to the advancement of machine learning applications. The traditional approach of centralized data learning is becoming increasingly impractical in certain domains, primarily due to escalating concerns over data privacy and security. Particularly in the manufacturing sector, the integrat...

This paper explores how to reduce supply-demand conflicts in supply chains by utilizing complementary products’ unique relationships and shared objectives. Also, it considers the service level concerns of consumers. Primarily employing game theory, this paper analyzes the pricing strategies of complementary products within a three-tier dual-channel...

Geometry representation, as a fundamental aspect of topology optimisation, is crucial to meet the growing demand for customised structural designs in Industry 4.0. Implicit neural representation (INR) based on neural network (NN) has emerged as a promising paradigm for geometry representation. To address the limitations of point-wise NN-based INR,...

Within Industry 5.0, human-centric smart manufacturing stands out for its adaptability and responsiveness, effortlessly combining information technology (IT) and artificial intelligence (AI) to optimize operations on both local and global levels. Methodologies combine advanced computational technologies with state-of-the-art manufacturing tools, cr...

The conventional automation approach has shown bottlenecks in the era of component assembly. What could be automated has been automated in some high-tech industrial production, leaving manual work performed by humans. To achieve ergonomic working environments and better productivity, human–robot collaboration has been adopted for this purpose throu...

This paper takes the aquatic products output of Zhejiang Province in 2018–2022 as the original data, constructs 13 relevant forecast indicators by analyzing the influencing factors of cold chain logistics demand, carries out the grey correlation calculation of the indicators, and the results obtained show that there is a strong correlation between...

Since the COVID-19 virus epidemic, the world, the governments, and citizens of various countries and regions the importance of medical care has been enhanced to varying degrees, for the demand for medicines has also risen sharply. Traditional pharmaceutical logistics, in both the mode of operation and the level of technology needed to face the surg...

In this paper, we address a multi-agent scheduling problem where multiple consumer agents compete to process jobs on parallel machines owned by multiple resource agents. Driven by self-interest and rationality, all agents aim to maximize their respective objectives. The study considers the strategic behaviors of both consumer agents and resource ag...

Following up on our previous review paper 'Review of manufacturing system design in the interplay of Industry 4.0 and Industry 5.0 (Part I): Design thinking and modeling methods' [1] , based on the proposed Thinking-Modelling-Process-Enabler (TMPE) framework of Manufacturing System Design (MSD), this paper (Part II of the two-part review) further r...

Human-robot collaboration (HRC) is set to transform the manufacturing paradigm by leveraging the strengths of human flexibility and robot precision. The recent breakthrough of Large Language Models (LLMs) and Vision-Language Models (VLMs) has motivated the preliminary explorations and adoptions of these models in the smart manufacturing field. Howe...

At present, the tool remaining useful life prediction technology is important to the effectiveness of machining, because tool life prediction plays the role of safety maintenance, cost optimization and quality assurance. However, this the technology faces many challenges in practical applications. The main problems include that when the spatial dis...

In order to address the problems with current robotic automated assembly such as limitations of model-based methods in unstructured assembly scenarios, low training efficiency of learning-based methods, and limited performance of policy generalization, this paper proposes two modeling methodologies based on deep reinforcement learning under the ove...

Additive Manufacturing (AM) has revolutionized the production landscape by enabling on-demand customized manufacturing. However, the efficient management of dynamic AM orders poses significant challenges for production planning and scheduling. This paper addresses the dynamic scheduling problem considering batch processing, random order arrival and...

In the process of metal cutting, realizing effective monitoring of tool wear is of great significance to ensure the quality of parts machining. To address the tool wear monitoring (TWM) problem, a tool wear monitoring method based on data-driven and physical output is proposed. The method divides two Physical models (PM) into multiple stages accord...

Chatter will affect machining accuracy, production efficiency, tool wear and workers' health. In order to avoid chatter early, a grinding chatter online monitoring model based on multisensor fusion information and hybrid deep neural network is proposed. First, the grinding experiment of acoustic emission (AE), force and displacement multichannel si...

Aiming at the problem of low carbon emission reduction in the sustainable development of the logistics industry, this study adopts the research method combining game theory and dynamic system analysis. A tripartite evolutionary game model including the logistics company, the manufacturer, and the government is constructed, and the strategy choice a...

In the global pharmaceutical industry, dual-channel competition has become a common business model, where the synergy and competition between online and offline channels profoundly impact corporate strategic decision-making. This study constructs a dual-channel competition model for pharmaceutical regulatory products to explore blockchain adoption...

This study investigates the implementation of collaborative route planning between trucks and drones within rural logistics to improve distribution efficiency and service quality. The paper commences with an analysis of the unique characteristics and challenges inherent in rural logistics, emphasizing the limitations of traditional methods while hi...

In the milling process of large-scale critical parts of energy equipment, the rigidity of the tool can be lower than that of workpieces, which makes it easy to trigger tool chatter. When the vibration is large, the tool cannot act on the workpiece and cannot effectively remove the material. In severe cases, the tool will be embedded inside the work...

The integration of advanced manufacturing and the new generation of information technology promotes the development of intelligent manufacturing. In the cutting process, the condition of cutting tools is a critical factor that profoundly affects product surface quality and machining efficiency. Tool Condition Monitoring (TCM) can reduce the cost of...

Human-Robot Collaboration (HRC) aims to create environments where robots can understand workspace dynamics and actively assist humans in operations, with the human intention recognition being fundamental to efficient and safe task fulfillment. Language-based control and communication is a natural and convenient way to convey human intentions. Howev...

Human-Robot Collaboration Assembly (HRCA) provides a solution to complex product assembly tasks in modern industrial manufacturing. A key point is how to enable robots to recognize assembly tasks being executed by assemblers through autonomous visual perception technology, and plan collaborative assembly tasks according to perception results in con...

Humans often use natural language instructions to control and interact with robots for task execution. This presents a significant challenge for robots, as they have to not only comprehend and link human commands with robot actions but also have a semantic understanding of operating scenes/environments. To address this challenge, we present a visio...

With the recent vision of Industry 5.0, the cognitive capability of robots plays a crucial role in advancing proactive human–robot collaborative assembly. As a basis of the mutual empathy, the understanding of a human operator’s intention has been primarily studied through the technique of human action recognition. Existing deep learning-based meth...

Human-robot collaborative assembly can leverage the unique capabilities of humans and robots to provide a flexible and efficient way for complex tasks. In this context, robots are expected to be endowed with the perception ability to collaborate with humans. For flexible processes, robotic grasping for a desired assembly part from variable multi-pa...

Medical devices and products are a special type of manufactured object. Medical applications normally have higher requirements for quality, complexity, personalization, precision and low fault tolerance than other types of manufactured product. It is therefore especially important to develop smart systems to support all phases of medical-related ma...

The cloud-edge collaborative manufacturing system (CCMS) connects distributed factories to a cloud centre through cloud-edge collaborative communication, introducing both opportunities and challenges to conventional dynamic job scheduling. Enhancing each factory’s scheduling performance by sharing general scheduling knowledge among heterogeneous fa...

Human-Robot Collaboration (HRC) is key to achieving the flexible automation required by the mass personalization trend, especially towards human-centric intelligent manufacturing. Nevertheless, existing HRC systems suffer from poor task understanding and poor ergonomic satisfaction, which impede empathetic teamwork skills in task execution. To over...

The concurrent societal goals and environmental challenges force industries to become more human-centric, sustainable, and resilient, which is envisioned as a value-oriented Industry 5.0 paradigm. The technology-driven Industry 4.0 paradigm is in a prosperous stage. Advanced information and manufacturing technologies have shown promising benefits,...

Embodied intelligence has always been regarded as the ultimate form of artificial intelligence (AI) and an ideal concept for smart manufacturing. With the development of AI foundation models, remarkable generalization capabilities have been achieved in various fields, such as natural language processing and computer vision. In the era of AI foundat...

The purpose of this special issue of IISE Transactions on Design and Manufacturing is to create a repository of state-of-the-art industrial engineering research on AI and Machine Learning (ML) for Manufacturing. An open call for papers on IISE Transactions was announced in 2021. Fourteen papers, to be published in two issues, were selected among 36...

Humans often use natural language instructions to control and interact with robots for task execution. This poses a big challenge to robots that need to not only parse and understand human instructions but also realise semantic understanding of an unknown environment and its constituent elements. To address this challenge, this study presents a vis...

Human-centricity, sustainability, and resilience are becoming core values in modern manufacturing, with human–robot collaboration (HRC) in high demand for flexible automation. However, human–robotic swarms are typically designed to target one specific procedure and cannot fully share their autonomy. The Metaverse, characterized by socialized avatar...

The manufacturing landscape has witnessed a paradigm shift towards multi-variety and small-batch production for the customized and personalized product (CPP). But this paradigm poses significant challenges for the cloud manufacturing system: 1) wired production machines cannot support the ultraflexible resource allocation for the CPP job; 2) the sc...

Autonomous robots that understand human instructions can significantly enhance the efficiency in human-robot assembly operations where robotic support is needed to handle unknown objects and/or provide on-demand assistance. This paper introduces a vision AI-based method for human-robot collaborative (HRC) assembly, enabled by a large language model...

Human-Robot Collaboration (HRC) has emerged as a pivot in contemporary human-centric smart manufacturing scenarios. However, the fulfilment of HRC tasks in unstructured scenes brings many challenges to be overcome. In this work, mixed reality head-mounted display is modelled as an effective data collection, communication, and state representation i...

The development of any industry cannot be done without social expectations. The industrial metaverse arises from customers' emphasis on their value, their desire for immersive experiences, and their vision for untram-meled economic transactions. This paper first introduces the definition, propositions, and metrics of the industrial metaverse toward...

Monitoring bearing failures in production equipment can effectively prevent finished product quality issues and unplanned factory downtime, thereby reducing supply chain uncertainties and risk. Therefore, monitoring bearing failures in production equipment is important for improving supply chain sustainability. Due to the generalization limitations...

This chapter starts with an introduction to the terminology of existing humancentric systems and then provides detailed statements on human roles within manufacturing systems. The remainder of this chapter is organized as follows. Section 6.2 gives the terminologies and definitions of human-centric systems within various cases. Section 6.3 reviews...

This chapter is arranged as follows: Section 10.2 will delve deeper into the latest advances in data denoising, data annotation, and data balancing facilitated by DL techniques. Section 10.3 will present the manufacturing applications of these methodologies, followed by a discussion on the remaining challenges and opportunities in Section 10.4, and...

This chapter is structured as follows. In Section 3.2 the definitions and the structure of Industry 5.0 are discussed. Then, in Section 3.3, the technological background is presented in accordance with the technological evolution from Industry 4.0 onward. Consequently, in Section 3.4, the challenges toward the transition from the ongoing Industry 4...

Human intention prediction is vital for the efficiency of human-robot collaboration (HRC) and is usually modeled based on data-driven methods. However, due to the complexity and diverse nature of HRC, data collection for human intention prediction suffers from low sampling efficiency which restricts the application of HRC in manufacturing. Differen...

Proactive Human–Robot Collaboration Toward Human-Centric Smart Manufacturing is driven by an appreciation of manufacturing scenarios where human and robotic agents can understand each other's actions and conduct mutual-cognitive, predictable, and self-organizing teamwork. Modern factories’ smart manufacturing transformation and the evolution of rel...

The COVID-19 outbreak has posed significant challenges to end-to-end global supply chain visibility and transparency, with city lockdowns, factory shutdowns, flight cancellations, cross-border closures, and other uncertainties, disruptions, and disturbances. To address these challenges, reliable and accurate spatial-temporal information of physical...

This paper addresses a multi-agent scheduling problem with uniform parallel machines owned by a resource agent and competing jobs with dynamic arrival times that belong to different consumer agents. All agents are self-interested and rational with the aim of maximizing their own objectives, resulting in intense resource competition among consumer a...

The integration of human-robot collaboration yields substantial benefits, particularly in terms of enhancing flexibility and efficiency within a range of mass-personalized manufacturing tasks, for example, small-batch customized product inspection and assembly/disassembly. Meanwhile, as human-robot collaboration lands broader in manufacturing, the...

As an emerging manufacturing paradigm, Industry 5.0 emphasizes human-centric intelligent manufacturing. XR technology (a general term of virtual reality, augmented reality and mixed reality) brings unprecedented opportunities for assembly in such manufacturing paradigm. We provide a comprehensive review, in-depth analysis , and prospect on XR in pr...

Aiming at the existing peg-in-hole assembly method problems of dependence on accurate contact state models, difficulties in data acquisition, low sampling efficiency, and poor security, a simulation research method for robot peg-in-hole assembly strategy based on DRL is proposed. A simulation environment of robot peg-in-hole assembly based on ROS-G...

The increasing complexity of industrial systems demands more effective and intelligent maintenance approaches to address manufacturing defects arising from faults in multiple asset modules. Traditional digital twin (DT) systems, however, face limitations in interoperability, knowledge sharing, and causal inference. As such, cognitive digital twins...

In the context of the development of the new period, the pharmaceutical supply chain market is gradually changing from a seller’s market to a buyer’s market. The closer the pharmaceutical supply chain market is to the consumer, the greater the market’s pricing power, so the market power of the pharmaceutical manufacturer and the retailer has change...

We consider a broadband over-the-air computation empowered model aggregation approach for wireless federated learning (FL) systems and propose to leverage an intelligent reflecting surface (IRS) to combat wireless fading and noise. We first investigate the conventional node-selection based framework, where a few edge nodes are dropped in model aggr...

Smart manufacturing has been transforming toward industrial digitalization integrated with various advanced technologies. Metaverse has been evolving as a next-generation paradigm of a digital space extended and augmented by reality. In the metaverse, users are interconnected for various virtual activities. In consideration of advanced possibilitie...

Tool Condition Monitoring (TCM) technology in machining is crucial for maintaining safety and optimizing costs. However, its practical application faces two significant challenges: difficulties in data collection and a decline in generalization performance across different monitoring tasks. To this end, a hybrid feature boundary-enhanced meta-learn...

Digital twin (DT) models are computational models that can effectively represent different assets and processes in the manufacturing environment. Moreover, the DT models can support intelligent automation by integrating with the digital foundation and the data analytics provided by the cyber-physical system (CPS) in an industrial environment. To pr...

Human-robot collaborative assembly (HRCA) combines the flexibility and adaptability of humans with the efficiency and reliability of robots during collaborative assembly operations, which facilitates complex product assembly in the mass personalisation paradigm. The cognitive ability of robots to recognise and predict human actions and make respons...

Accurately predicting the tool remaining useful life (RUL) is critical for maximizing tool utilization and saving machining costs. Various physical model-based or data-driven prediction methods have been developed and successfully applied in different machining operations. However, many uncertain factors affect tool RUL during the cutting process,...

Manufacturing systems envisioned for factories of the future will promote human-centricity for close collaboration in a shared working environment towards better overall productivity within the context of Industry 5.0. Robust and accurate recognition and prediction of human intentions are crucial to reliable and safe collaborative operations betwee...

The residual stress field of structural components significantly influences their comprehensive performance and service life. Due to the lack of effective representation means and inference methods, existing methods are confined to inspecting local residual stress rather than the entire residual stress field, rendering the inference of complex resi...

With the continuous development of human-centric, resilient, and sustainable manufacturing towards Industry 5.0, Artificial Intelligence (AI) has gradually unveiled new opportunities for additional functionalities, new features, and tendencies in the industrial landscape. On the other hand, the technology-driven Industry 4.0 paradigm is still in fu...

Human-Robot Interaction (HRI) has escalated in notability in recent years, and multimodal communication and control strategies are necessitated to guarantee a secure, efficient, and intelligent HRI experience. In spite of the considerable focus on multimodal HRI, comprehensive disquisitions delineating various modalities and intricately analyzing t...

Smart manufacturing (SM) enhances the competitiveness of manufacturing companies by promoting automation and overall equipment effectiveness (OEE), targeting to produce 100% qualified products fully automatically. One of the key challenges to the SM initiatives is the continuous demand fluctuations in the specification and quantity, especially when...

The fast development of AI-based approaches for image recognition has driven the availability of fast and reliable tools for identifying the human body in captured videos (both 2D and 3D). This has increased the feasibility and effectiveness of approaches for human pose estimation in industrial environments. This essay will cover different approach...

Modelling and Simulation (M&S) are critical capabilities for Cloud Computing. M&S products and services are valuable resources that have to be easily accessible and available on demand in a cost-effective way to users; they provide the required level of agility so that capabilities can be integrated quickly and easily. To address new design and man...

Robotic machining is a potential method for machining large-scale components (LSCs) due to its low cost and high flexibility. However, the low stiffness of robots and complex machining process of LSCs result in a lack of alignment between the physical process and digital models, making it difficult to realize the robotic machining of LSCs. The rece...

In recent years, brain-based technologies that capitalise on human abilities to facilitate human-system/robot interactions have been actively explored, especially in brain robotics. Brain-computer interfaces, as applications of this conception, have set a path to convert neural activities recorded by sensors from the human scalp via electroencephal...

Human-centricity, a core value of Industry 5.0, places humans in the center of production. It leads to the prioritization of human needs, spanning from health and safety to self-actualization and personal growth. The concept of the Human Digital Twin (HDT) is proposed as a critical method to realize human-centricity in smart manufacturing systems t...

Tool breakage monitoring (TBM) during milling operations is crucial for ensuring workpiece quality and minimizing economic losses. Under the premise of sufficient training data with a balanced distribution, TBM methods based on statistical analysis and artificial intelligence enable accurate recognition of tool breakage conditions. However, conside...

Intelligent models for tool wear condition monitoring (TWCM) have been extensively researched. However, in industrial scenarios, limited acquired monitoring signals and variations of machining parameters lead to insufficient training samples and data distribution shifts for the models. To address the issues, this research presents a novel residual...

Robotic autonomous assembly is critical in intelligent manufacturing and has always been a research hotspot. Most previous approaches rely on prior knowledge, such as geometric parameters and pose information of the assembled parts, which are hard to estimate in unstructured environments. This paper proposes a residual reinforcement learning (RL) p...

Rapidly evolving global initiatives highlighted a manufacturing future that is connected, smart, resilient, human-centric, and sustainable for producing high-value-added products and services. Manufacturing systems, therefore, must change: (1) new manufacturing control strategies are required to enable flexible production of heterogeneous manufactu...

Human-robot collaborative disassembly (HRCD) has gained much interest in the disassembly tasks of end-of-life products, integrating both robot’s high efficiency in repetitive works and human’s flexibility with higher cognition. Explicit human-object perceptions are significant but remain little reported in the literature for adaptive robot decision...

Journal of Engineering Design, Special Issue https://www.tandfonline.com/action/newsAndOffers?journalCode=cjen20 To contribute to this issue, submissions in the following topics, but are not limited to, are highly encouraged: Advances in cognitive intelligence fundamentals/theories Knowledge extraction/representation for cognitive intelligence Kno...

Cloud manufacturing is a manufacturing paradigm that integrates wide-area distributed manufacturing resources for distributed services over the Internet. Scheduling is a critical technique that determines the overall performance of a cloud manufacturing system. Robots are an important type of manufacturing resource in cloud manufacturing. Schedulin...

Humans can instinctively predict whether a given grasp will be successful through visual and rich haptic feedback. Towards the next generation of smart robotic manufacturing, robots must be equipped with similar capabilities to cope with grasping unknown objects in unstructured environments. However, most existing data-driven methods take global vi...

Intelligent algorithms can empower the development of smart manufacturing, since they can provide optimal solutions for detection, analysis, prediction and optimization. In recent ten years, publications on intelligent algorithms in smart manufacturing have increased sharply, showing superior performance in solving problems such as shop-floor sched...

Class imbalance (CI) is a well-known problem in data science. Nowadays, it is affecting the data modeling of many of the real-world processes that are being digitized. The manufacturing industry turns out to be highly affected by this problem, especially in fault inspection, prediction or monitoring processes, and in all those processes where the p...

The training of future experts and operators in manufacturing engineering relies on understanding procedural processes that require applied practice. Yet, current manufacturing education and training overwhelmingly continues to depend on traditional pedagogical methods that segregate theoretical studies and practical training. While educational ins...

During cutting operations, tool condition monitoring (TCM) is essential for maintaining safety and cost optimization, especially in the accelerated tool wear phase. Due to the safety constraints of the actual production environment and the tool's properties, the data for each wear stage is usually unbalanced, and these unbalances lead to difficulti...

Head-mounted display (HMD) augmented reality (AR) has attracted more and more attention in manufacturing activities, as it enables operators to access visual guidance in front of their view directly while freeing human’s two hands. Nevertheless, HMD AR has not been widely adopted in manufacturing fields as humans expected since the release of Googl...

With the rapid development of intelligent manufacturing, fault diagnostic methods based on deep learning have achieved impressive results. However, most methods require plentiful annotated samples and are based on the assumption that data from the source and target domains has the same distribution. These two conditions are difficult to satisfy in...