Yuqian Lu- PhD in Mechatronics Engineering
- Senior Lecturer at University of Auckland
Yuqian Lu
- PhD in Mechatronics Engineering
- Senior Lecturer at University of Auckland
Looking for talented PhD applicants seeking financial support from UoA Doctoral Scholarship and CSC Scholarship.
About
142
Publications
133,202
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Introduction
Dr. Yuqian Lu is a Senior Lecturer of Smart Manufacturing at the Department of Mechanical Engineering, The University of Auckland. He leads the Industrial Artificial Intelligence Research Group (https://yuqianlu.xyz).
Dr. Lu's research interests span manufacturing automation, autonomous systems, industrial AI, and robotics.
Current institution
Additional affiliations
February 2019 - February 2022
August 2017 - January 2019
FRAMECAD Ltd
Position
- Business Development Manager
Description
- Championing and delivering to the vision, strategy, and roadmap for the Industry 4.0 product portfolio. Leading the R&D of smart design and manufacturing systems products.
September 2016 - July 2017
FRAMECAD Ltd
Position
- Analyst
Education
October 2012 - July 2016
Publications
Publications (142)
![Figure 1: Manufacturing paradigm shifts and the drivers [13]](profile/Yuqian-Lu-5/publication/342693197/figure/fig1/AS:909681279123456@1593896326120/Manufacturing-paradigm-shifts-and-the-drivers-13_Q320.jpg)
Smart manufacturing is arriving. It promises a future of mass-producing highly personalized products via responsive autonomous manufacturing operations at a competitive cost. Of utmost importance, smart manufacturing requires end-to-end integration of intra-business and inter-business manufacturing processes and systems. Such end-to-end integration...
Smart manufacturing is characterized by self-organizing manufacturing systems and processes that can respond to dynamic changes. We envision the rapid advancement of smart machines with empathy skills will enable anthropocentric human-machine teams that can maximize human flexibility and wellness at work while maintaining the required manufacturing...
![Fig. 3. Manufacturing paradigm shifts and the drivers [1]](profile/Yuqian-Lu-5/publication/351497308/figure/fig5/AS:1022438439665665@1620779728956/Manufacturing-paradigm-shifts-and-the-drivers-1_Q320.jpg)
Mass personalization is becoming a reality. It requires responsive and flexible manufacturing operations for producing individualized products in dynamic batch sizes at scale in a cost-effective way. Therefore, manufacturing systems should timely respond to meet changing demands and conditions in the factory, in the supply network, and in customer...
The recent shift to wellbeing, sustainability, and resilience under Industry 5.0 has prompted formal discussions that manufacturing should be human-centric-placing the wellbeing of industry workers at the center of manufacturing processes, instead of system-centric-only driven by efficiency and quality improvement and cost reduction. However, there...
The integration of automation technologies has improved the efficiency of industrialised construction (IC), yet a deeper understanding of their effects on the manufacturing and assembly stages remains necessary. This paper provides a systematic review of how various automation technologies influence these key stages in IC, analysing 53 articles. It...
With the growing demand for personalized production, multi-agent technology has been introduced to facilitate rapid self-organizing production execution. The application of communication protocols and dynamic scheduling algorithms supports multi-agent negotiation and real-time scheduling decisions in response to conventional production events. To a...
Smart manufacturing does not aim for a fully unmanned approach. The role of humans in complex manufacturing processes may never be completely replaceable.
Enterprises typically possess a vast repository of design documents. Establishing a knowledge management system based on these documents can significantly enhance organizational efficiency (Xing et al. in Comput Integr Manufact Syst 12:1293–1299, 2006 [1]). Such a system effectively leverages internal knowledge resources. Standard documents represe...
The essence of production quality process control is to organize effective production, accurately identify and manage potential quality risks in the product manufacturing process. And constantly improve the driving force of self-improvement, which is a process of continuous improvement of the management system. However, due to the management guided...
In the new era of manufacturing development characterized by knowledge and information, product design is receiving more and more universal attention from countries around the world. Product design is a complex creative activity involving human creativity. With the further development of market demand, the functional requirements of products are in...
The pivotal role of Artificial Intelligence (AI) technology in smart manufacturing represents a prominent area of inquiry. Presently, an increasing array of devices, both large and small, deployed across factory floors are outfitted with sensors. These devices collect and share voluminous data while capturing myriad production actions. Utilizing AI...
As the automation and intelligent transformation of the manufacturing industry advances, more and more automated equipment and industrial intelligent robots are participating in workshop production operations, gradually replacing some of the work done by workshop workers. They are playing an increasingly important role in modern production. Althoug...
Assembly is an important part of the manufacturing process, and the quality of assembly directly affects the performance of the product. In order to improve the quality of product assembly, it is necessary to carry out a reasonable design of the assembly process first. In view of this, this chapter first analyzes the progress and current situation...
In order to realize industrial intelligence, a number of fundamental tools are needed. These include the algorithmic foundations of industrial intelligence, the basic processes for realizing industrial intelligence, and the broader industrial intelligence application scenarios.
The fault diagnosis of industrial equipment is the earliest application field of artificial intelligence technology. Pattern recognition and signal processing methods have been widely used, and excellent results have been achieved. However, the classical algorithm is also facing significant challenges: the data set is getting larger and larger, and...
This chapter first provides an overview of AR. Secondly, it introduces the AR-oriented multi-view construction and interaction method of machining process information and the model virtual-reality fusion method based on multiple information fusion. Finally, a machining process prototype system based on AR is developed and a case study is presented.
Users and manufacturers now demand higher product quality, seeking not only functional performance but also an appealing appearance, particularly in surface quality. However, surface defects are often unavoidable during manufacturing. These defects vary by product type but generally refer to areas where the surface's physical or chemical properties...
The evolution of manufacturing towards intelligent and digital processes requires innovation in machining quality control. While current research primarily addresses single-scale quality control, it overlooks comprehensive multi-scale product quality characterization. Digital twin technology emerges as a potential solution. This review examines dig...
Recognising and tracking human actions from videos is crucial for human-robot collaborative assembly (HRCA). However, traditional action segmentation methods suffer from limited scene adaptability, partly because they conceptualise actions as unified verb-object entities with complete semantics. To overcome this, we propose a compositional action s...
Self-organizing manufacturing network has emerged as a viable solution for adaptive manufacturing control within the mass personalization paradigm. This approach involves three critical elements: system modeling and control architecture, interoperable communication, and adaptive manufacturing control. However, current research often separates inter...
The advent of Industry 5.0 brought significant changes regarding manufacturing and logistics applications, prioritizing the operator's welfare at the core of its principles. Industry 5.0, complementing its predecessors, strongly emphasises the creation of production systems focused on people and human-technology interaction for system productivity,...
Human-robot collaboration (HRC) is crucial for enabling mass personalised manufacturing and human-centric manufacturing. The recent advancements in video understanding technology have enabled robots to interpret human actions from videos and discern the appropriate timing and nature of required robot assistance. However, current vision-based HRC sy...
Smart manufacturing systems are a new paradigm in Industry 4.0 driven by the emerging information and communication technology and artificial intelligence that converge to digital twin, which are able to perceive, recognize, and handle the changes in demand and production. Reconfigurable machine tools (RMTs) can promote the flexibility of smart man...
Human action recognition (HAR) is crucial for enabling seamless human-robot collaboration and enriching robot learning. In product assembly scenarios, action recognition is challenging due to the dual-handed nature of tasks, the subtle and complex human-object interactions, and the visual limitations. To achieve online action recognition for both h...
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...
Maintenance manuals are crucial information sources for maintenance and repair. Prior studies explored factual knowledge extraction from textual documents. However, maintenance knowledge in manuals is more task‐centric rather than factual knowledge and often documented in an unstructured Portable Document Format (PDF), posing challenges for knowled...
Disassembly is a decisive step in the remanufacturing process of End-of-Life (EoL) products. As an emerging semi-automatic disassembly paradigm, human-robot collaborative disassembly (HRCD) offers multiple disas-sembly methods to enhance flexibility and efficiency. However, HRCD increases the complexity of planning and determining the optimal disas...
In intelligent welding systems, pre-welding parameter extraction is a foremost technology in upgrading robotic welding and integrating emerging technologies, e.g., digital twins, big data, and cloud manufacturing. However, current workpiece postures still rely on manual judgments based on workers' experience, which has become one of major issues hi...
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...
In the context of an increasingly automated and personalized manufacturing mode, efficient assembly sequence planning (ASP) has emerged as a critical factor for enhancing production efficiency, ensuring product quality, and satisfying diverse market demands. To address this need, our study first transforms the assembly topology and process into a w...
Future manufacturing will witness a shift in human-robot relationships toward collaboration, compassion, and coevolution. This will require seamless human-robot knowledge transfer. Differences in language and knowledge representation hinder the transfer of knowledge between humans and robots. Thus, a unified knowledge representation system that can...
Weld seam type confirmation is a key part of intelligent integrated welding to cope with adjustable schemes varying with the seam morphology. However, existing works are mainly based on qualitative joint description (QJD) and machine learning classification (MLC), which entail high costs in balancing multiple given parameters , acquiring sufficient...
Human-centric manufacturing paradigm requires the human-robot collaboration (HRC) system to place the well-being of workers at the centre of manufacturing processes. Hence, optimising human workers' fatigue during human-robot task allocation in an HRC system is crucial for human-centric manufacturing. A prerequisite for this is an objective assessm...
Understanding comprehensive assembly knowledge from videos is critical for futuristic ultra-intelligent industry. To enable technological breakthrough, we present HA-ViD - the first human assembly video dataset that features representative industrial assembly scenarios, natural procedural knowledge acquisition process, and consistent human-robot sh...
In the era of Industry 4.0, the demand fluctuation has become fiercer due to the characteristics of diversification, customisation, and uncertainty. Reconfigurability of manufacturing systems has been proven to be a useful and necessary feature when it comes to handling demand uncertainty. This feature can be achieved through the implementation of...
Robotic welding is gradually advancing towards intelligent integrated welding with integration of different seam types. In this process, the initial point positioning of weld seams is a foremost technique for ensuring a smooth subsequent welding process. However, existing studies on welding initial point positioning are not well integrated in terms...
Human-centric human-robot collaboration (HHRC) allows seamless collaboration between humans and robots to fulfill flexible manufacturing operations in a shared workspace while maximizing operator autonomy and well-being toward optimal team performance. Therefore, assessing and monitoring an operator's physical health, specifically fatigue, is param...
Mass personalization is rapidly approaching. In response, manufacturing systems should be capable of autonomously changing production plans, configurations and schedules under dynamic manufacturing environments for producing personalized products. Self-organizing manufacturing network is a promising paradigm for mass personalization. The backbone o...
In human-robot collaborative (HRC) manufacturing systems, how the collaborative robots engage in the collaborative tasks and complete the corresponding work in a timely manner according to the actual state has been a critical factor that hinders the efficiency of HRC. Inappropriate collaborative behaviors will result in a poor perceptual experience...
Digital twin technology has been gradually explored and applied in the machining process. A digital twin machining system creates high-fidelity virtual entities of physical entities to observe, analyze, and control the machining process in real-time. However, the current digital twin machining systems lack sufficient adaptability because they are u...
Equipment spot-inspection records are essential information sources supporting fault reason analysis.However,there is a lack of effective mining of root cause information in equipment spot-inspection failures to improve the reliability of preventive equipment maintenance.This paper introduces causal theory to the manufacturing field for the first t...
Cloud manufacturing provides an interactive environment for collaboration between digital twin-based manufacturing systems. However, access to the enterprise cloud by a significant number of digital twin systems would lead to bandwidth competition and severe delays. Therefore, the data exchange process needs to be improved in a more reliable and ef...
Cloud manufacturing was incepted a decade ago. Researchers have since been working on this topic to find more feasible, cost-effective, and less complex ways to implement cloud manufacturing platforms. This literature review analyzes the concept, characteristics of cloud manufacturing, and impediments to its quick adoption. In recent years, researc...
Smart manufacturing is arriving [1]. It promises a future of highly
responsive manufacturing operations with advanced sensing, reasoning, and decision-making capabilities towards mass personalization [2]. Statistical AI, e.g., machine learning technologies, has shown great potential in making manufacturing smart [3]. However, Statistical AI’s appro...
Rapid on-demand manufacturing resource sharing within and between factories are critical to achieving responsive autonomous manufacturing collaborations towards mass personalization. To this end, cloud manufacturing technologies allow resource owners/service providers to virtualize and encapsulate their resources as services accessible over the Int...
In Industry 4.0, the emergence of new information technology and advanced manufacturing technology (e.g., digital twin, and robot) promotes the flexibility and smartness of manufacturing systems to deal with production task fluctuation. Digital twin-driven manufacturing system with human-robot collaboration is a typical paradigm of intelligent manu...
In railway engineering, monitoring the health condition of rail track structures is crucial to prevent abnormal vibration issues of the wheel–rail system. To address the problem of low efficiency of traditional nondestructive testing methods, this work investigates the feasibility of the computer vision-aided health condition monitoring approach fo...
As industry rapidly shifts towards mass personali-sation, the need for a decentralised multi-agent system capable of dynamic flexible job shop scheduling (FJSP) is evident. Traditional heuristic and meta-heuristic scheduling methods cannot achieve satisfactory results and have limited application to static environments. Recent Reinforcement Learnin...
International Journal of Computer Integrated Manufacturing
Rapidly evolving global initiatives have highlighted a manufacturing future that is connected, smart, resilient, human-centric, and sustainable for rapidly producing high-value-added products and services defined by end-users. Manufacturing systems, therefore, have to change: (1) new manuf...
When dealing with the fault diagnosis of different rotating machines (gear or bearing), different working conditions (such as rotating speed), different signals (acoustic signal or vibration signal), it is usually necessary to establish different models, which is, however, time-consuming and laborious. At the same time, the models have poor general...
Cloud manufacturing represents a service-oriented manufacturing paradigm that allows ubiquitous and on-demand access to various customisable manufacturing services in the cloud. While a vast amount of research in cloud manufacturing has focused on high-level decision-making tasks, such as service composition and scheduling, the link between field-l...
The process knowledge base is the key module in intelligent process design, it determines the intelligence degree of the design system and affects the quality of product design. However, traditional process knowledge base construction is non-automated, time consuming and requires much manual work, which is not sufficient to meet the demands of the...
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Goal:Machine Learning (ML) has recently become a power-engine transforming various manufacturing research and applications. In the era of Smart Manufacturing and I4.0, the abundance of smart sensors and industrial internet of things has made manufacturing systems a data-rich environment. ML techniques play a significant role in uncovering fine-grai...
Goal: Digital twin technology can build virtual replicas of physical entities to observe. analyze and control the physical processes and systems. It opens the potential of developing ultra-intelligent automation systems via embedding complex models. expensive deep learning algorithms and ubiquitous communication. However, open questions exist for h...
The time series data in the manufacturing process reflects the sequential state of the manufacturing system, and the fusion of temporal features into the industrial knowledge graph will undoubtedly significantly improve the knowledge process efficiency of the manufacturing system. This paper proposes a semantic-aware event link reasoning over an in...
The (I, R, S) policy is a well-known inventory replenishment strategy, where inventory is raised to an order-up-to-level S at the end of each review interval I, if it falls below a reorder-point R. Determining the optimal values for these parameters by mathematical analysis methods are difficult, especially in sectors with complex and uncertain pur...
Small and Medium-sized Enterprises (SMEs) hold a significant proportion in the economy and more than half of employment worldwide. Nowadays, many SMEs are embarking on a digital transformation journey by embracing and deploying the Fourth Industrial Revolution (a.k.a. Industry 4.0) technologies with a vision of the “Factory of the Future”. Factory...
Digital twin technology can build virtual replicas of physical entities to observe, analyze, and control the machining process. The virtual model always simplifies the physical entity as limited by the current technical level, so that the digital twin model cannot fully reflect the physical entity with high-fidelity, leading to a particular error r...
Industrial tabular information extraction and its semantic fusion with text (ITIESF) is of great significance in converting and fusing industrial unstructured data into structured knowledge to guide cognitive intelligence analysis in the manufacturing industry. A novel end-to-end ITIESF approach is proposed to integrate tabular information and cons...
![Fig. 1. Reference Architecture Model Industrie 4.0 (RAMI4.0) [16].](profile/Xun-Xu-15/publication/355391185/figure/fig1/AS:1080491734237189@1634620712200/Reference-Architecture-Model-Industrie-40-RAMI40-16_Q320.jpg)
![Fig. 2. Core values of Industry 5.0 [9].](profile/Xun-Xu-15/publication/355391185/figure/fig2/AS:1080491734249473@1634620712254/Core-values-of-Industry-50-9_Q320.jpg)
Industry 4.0, an initiative from Germany, has become a globally adopted term in the past decade. Many countries have introduced similar strategic initiatives, and a considerable research effort has been spent on developing and implementing some of the Industry 4.0 technologies. At the ten-year mark of the introduction of Industry 4.0, the European...
Metal products are susceptible to factors such as cutting force, clamping force and heat in the machining process, resulting in product quality problems, such as geometric deformation and surface defects. The real-time observation and control of product quality are integral to optimizing machining process. Digital twin technologies can be used to m...
Dynamic personalized orders demand and uncertain manufacturing resource availability have become the research hotspots of intelligent resource optimization allocation. Currently, the data generated from the manufacturing industry are rapidly expanding. Such data are multi-source, heterogeneous and multi-scale. Transforming the data into knowledge t...
Automatic process decision-making is a key module in intelligent process design(IPD), which determines the intelligence degree of IPD and affects the quality of product design. The traditional process decision-making method fails to solve the problem of knowledge expression, especially the integration of enterprise manufacturing resources and proce...
Unstructured manufacturing environments with flexible production procedures will continue to require worker involvement due to the difficulty of automation. In the context of human-centricity, human physical well-being during manufacturing operations, in particular, muscular fatigue, must be reliably assessed and optimized on the shop floor. Howeve...
Accurate anomaly detection is critical to the early detection of potential failures of industrial systems and proactive maintenance schedule management. There are some existing challenges to achieve efficient and reliable anomaly detection of an automation system: (1) transmitting large amounts of data collected from the system to data processing c...
![Development of machining monitoring: (a) [29] the development of the...](profile/Bao-Jinsong/publication/349798676/figure/fig1/AS:997863081394184@1614920506127/Development-of-machining-monitoring-a-29-the-development-of-the-manufacturing_Q320.jpg)
The change of size, surface roughness, residual stress, and so on profoundly influence the final machining quality of complex mechanical products. Digital twin machining technology can ensure machining quality by observing the machining process in real time. However, the current digital twin systems mainly adopt the display method of virtual-real s...
Mass personalization is arriving. It requires smart manufacturing capabilities to responsively produce personalized products with dynamic batch sizes in a cost-effective way. However, current manufacturing system automation technologies are rigid and inflexible in response to ever-changing production demands and unforeseen internal system status. A...
The vision-based welding status recognition (WSR) provides a basis for online welding quality control. Due to the severe arc and fume interference in the welding area and limited computational resources at the welding edge nodes, it becomes a challenge to mine the most discriminative feature contained in welding images by using a lightweight model....
In the wake of COVID-19, the production demand of medical equipment is increasing rapidly. This type of products is mainly assembled by hand or fixed program with complex and flexible structure. However, the low efficiency and adaptability in current assembly mode are unable to meet the assembly requirements. So in this paper, a new framework of hu...
Questions
Question (1)
There has been a surge in digital twin related research over the past 2-3 years, however, the term has become a buzzy word that academics and industry overuse. In particular, a couple of tech giants have used Digital Twin for marketing their existing products&services. This chaotic situation has made it challenging to understand the core research questions on digital twin.
Is digital twin a concept that only targets engineering application innovation with existing technologies?
OR, there are unique research questions for digital twin itself?
