The complexity in the collaboration between humans and robots in smart manufacturing remains a significant challenge. This paper introduces an LLM-based manufacturing execution system enhancing ...Human-Robot Collaboration (HRC) in smart manufacturing. By leveraging Large Language Models (LLMs), the system provides a natural language interface for operators, integrates with Digital Twins for real-time data, and employs behavior-based control for robots. This integration facilitates intuitive interactions and rapid system programming, addressing communication complexities in HRC. The effectiveness of this approach is validated through two HRC assembly case studies, demonstrating significant improvements in collaboration and efficiency.
Information and communication technology is undergoing rapid development, and many disruptive technologies, such as cloud computing, Internet of Things, big data, and artificial intelligence, have ...emerged. These technologies are permeating the manufacturing industry and enable the fusion of physical and virtual worlds through cyber-physical systems (CPS), which mark the advent of the fourth stage of industrial production (i.e., Industry 4.0). The widespread application of CPS in manufacturing environments renders manufacturing systems increasingly smart. To advance research on the implementation of Industry 4.0, this study examines smart manufacturing systems for Industry 4.0. First, a conceptual framework of smart manufacturing systems for Industry 4.0 is presented. Second, demonstrative scenarios that pertain to smart design, smart machining, smart control, smart monitoring, and smart scheduling, are presented. Key technologies and their possible applications to Industry 4.0 smart manufacturing systems are reviewed based on these demonstrative scenarios. Finally, challenges and future perspectives are identified and discussed.
New aerospace product developments need to be done following economic and environmental drivers like costs reduction, secure a short time-to-market, reduce total carbon footprint, among others. ...Aerospace manufacturing systems must quickly adapt to new products or driver changes, reusing existing industrial resources with enough flexibility to be adjusted or reconfigured in the desired scenarios. As a result, the industrial resource reconfigurability requires to be considered from the conceptual design phase of both, product and industrial resources development, in a collaborative engineering process, following Reconfigurable Manufacturing System (RMS) principles. This paper aims to support the aerospace RMS design at conceptual phase. To this end, a systematic literature review is performed focusing on the following research questions: (a) which characteristics of aerospace industrial resources need to be considered at conceptual design phase?; (b) how the industrial resources design is interlinked with the product and processes design in current RMS design methods?; (c) how to consider aerospace products non-negligible constraints during RMS design?; (d) what is the current application of RMS in the aerospace industry?. Compared to other reviews focusing on specific aspects of the RMS design and management, this study is focused on the industrial resource role in the RMS design at the conceptual phase and highlights the application or applicability of RMS in the aerospace industry. The goal is to provide the research community with an overview of the research trend on the field, and recommendations to stimulate researchers and practitioners in developing studies in this field.
•A systematic literature review is performed to identify current research gaps in RMS design in the aerospace industry.•Methodological guidelines are followed, allowing a systematic and structured evaluation of the literature in this field.•Specific emphasis is given to the applicability of RMS design principles in aerospace during the conceptual design phase.•The goal is to provide the research trend on the field and recommendations to stimulate further studies in this topic.
Reconfigurable manufacturing systems (RMSs), which possess the advantages of both dedicated serial lines and flexible manufacturing systems, were introduced in the mid-1990s to address the challenges ...initiated by globalization. The principal goal of an RMS is to enhance the responsiveness of manufacturing systems to unforeseen changes in product demand. RMSs are cost-effective because they boost productivity, and increase the lifetime of the manufacturing system. Because of the many streams in which a product may be produced on an RMS, maintaining product precision in an RMS is a challenge. But the experience with RMS in the last 20 years indicates that product quality can be definitely maintained by inserting in-line inspection stations. In this paper, we formulate the design and operational principles for RMSs, and provide a state-of-the-art review of the design and operations methodologies of RMSs according to these principles. Finally, we propose future research directions, and deliberate on how recent intelligent manufacturing technologies may advance the design and operations of RMSs.
The demand for distributed manufacturing systems (DMS) in the manufacturing sector has notably gained vast popularity as a suitable choice to accomplish sustainability benefits. Manufacturing ...companies are bound to face critical barriers in their pursuit of sustainability goals. However, the extent to which the DMS attributes relate to sustainable performance and impact critical barriers to sustainability is considerably unknown. To help close this gap, this article proposes a methodology to determine the relative importance of sustainability barriers, the influence of DMS on these barriers, and the relationship between DMS attributes and sustainable performance. Drawing upon a rich data pool from the Chinese manufacturing industry, the best-worst method is used to investigate the relative importance of the sustainability barriers and determine how the DMS attributes influence these barriers and relate to sustainability. The study findings show that "organizational barriers" are the most severe barriers and indicate that "reduced carbon emissions" has the highest impact on "organizational" and "sociocultural barriers" whereas public approval" has the highest impact on "organizational barriers." The results infer that "reduction of carbon emission" is the DMS strategy strongly linked to improved sustainable performance. Hence, the results can offer in-depth insight to decision-makers, practitioners, and regulatory bodies on the criticality of the barriers and the influence of DMS attributes on the sustainability barriers, and thus, improve sustainable performance for increased global competitiveness. Moreover, our study offers a solid foundation for further studies on the link between DMS and sustainable performance.
•Uniquely explores the state-of-the-art in enabling distributed and decentralized machine control and machine intelligence.•Offers unique comparatives of enabling IT & OT technology, machine ...intelligence paradigms, and future state “smart machine” models.•Draws objective answers to research questions relating to reconfigurable design, industry adoption, and enabling future state technology.•Presents a vision for next-gen Industry 4.0 manufacturing machines, which will exhibit extraordinary Smart Reconfigurable (SR*) capabilities.
This paper provides a fundamental research review of Reconfigurable Manufacturing Systems (RMS), which uniquely explores the state-of-the-art in distributed and decentralized machine control and machine intelligence. The aim of this review is to draw objective answers to two proposed research questions, relating to: (1) reconfigurable design and industry adoption; and (2) enabling present and future state technology. Key areas reviewed include: (a) RMS – fundamentals, design rational, economic benefits, needs and challenges; (b) Machine Control – modern operational technology, vertical and horizontal system integration, advanced distributed and decentralized control; (c) Machine Intelligence – distributed and decentralized paradigms, technology landscape, smart machine modelling, simulation, and smart reconfigurable synergy. Uniquely, this paper establishes a vision for next-generation Industry 4.0 manufacturing machines, which will exhibit extraordinary Smart and Reconfigurable (SR*) capabilities.
•A methodology to design an adaptive manufacturing system (AMS) is proposed.•The designed AMS is human-centred and considers workers’ needs and working conditions.•Knowledge-based rules and CBR ...algorithms allow the intelligent system adaptation.•A virtual commissioning approach is used for simulation and features’ optimization.•An industrial case study shows an example of human-centred AMS and demonstrates the benefits.
The so-called smart manufacturing systems (SMS) combine smart manufacturing technologies, cyber-physical infrastructures, and data control to realize predictive and adaptive behaviours. In this context, industrial research focused mainly on improving the manufacturing system performance, almost neglecting human factors (HF) and their relation to the production systems. However, in order to create an effective smart factory context, human performance should be included to drive smart system adaptation in efficient and effective way, also by exploiting the linkages between tangible and intangible entities offered by Industry 4.0. Furthermore, modern companies are facing another interesting trend: aging workers. The age of workers is generally growing up and, consequently, the percentage of working 45–64years old population with different needs, capabilities, and reactions, is increasing. This research focuses on the design of human-centred adaptive manufacturing systems (AMS) for the modern companies, where aging workers are more and more common. In particular, it defines a methodology to design AMS able to adapt to the aging workers’ needs considering their reduced workability, due to both physical and cognitive functional decrease, with the final aim to improve the human-machine interaction and the workers’ wellbeing. The paper finally presents an industrial case study focusing on the woodworking sector, where an existing machine has been re-designed to define a new human-centred AMS. The new machine has been engineered and prototyped by adopting cyber-physical systems (CPS) and pervasive technologies to smartly adapt the machine behaviour to the working conditions and the specific workers’ skills, tasks, and cognitive-physical abilities, with the final aim to support aging workers. The achieved benefits were expressed in terms of system usability, focusing on human-interaction quality.
The layout of fixed-position assembly islands (FPAI) is widely used for producing fragile or bulky products. With the increasing customised demand and unique operation patterns, manufacturing ...practitioners are facing challenges on flexible and efficient production arrangement to meet customer demand, which lead to inappropriate assembly islands configuration, frequent setups and long waiting times in FPAI. Industry 4.0 comes with the promise of improved flexibility and efficiency in manufacturing. In the context of Industry 4.0, this paper proposes a 5-layer APICS (
a
ssembly layer,
p
erception layer,
i
nteraction layer,
c
ognition layer, and
s
ervice layer) roadmap for transformation and implementation of Assembly 4.0. Following the 5-layer APICS roadmap, a Graduation Intelligent Manufacturing System (GiMS) is presented as the pioneering implementation in FPAI. A graduation-inspired assembly system is designed for FPAI at assembly layer. Internet of Things (IoT) and industrial wearable technologies are deployed for perception, connection, and collaboration among various manufacturing resources at perception and interaction layer. A self-configuration model is proposed at cognition layer for autonomously configuring optimal assembly islands and corresponding production activities to meet customer demand. Cloud-based services are developed for managers and onsite operators to facilitate their decision-making and daily operations at service layer. Finally, a demonstrative case is conducted to verify the feasibility of the proposed methods.
•The paper reviews offline prediction, online detection, suppression.•Both regenerative chatter and mode coupling chatter are discussed.•Chatter active control is classified according to actuators ...and control algorithms.•The requirement of real-time becomes important in chatter monitoring system.•An integrated chatter monitoring system for thin wall machining is a challenge.
Machining chatter has been studied by scholars over the past decades, since chatter has a significant impact on surface quality and productivity. Researchers have carried out extensive research on offline chatter prediction, online chatter detection, and chatter suppression. However, these studies have not been comprehensively reviewed. Especially with the development of intelligent manufacturing (IM), some new requirements for research on chatter have already been put forward. Hence, it is crucial to conduct a systematic review of chatter, focusing on regenerative chatter and mode coupling chatter. This paper presents a critical review from the three areas and a detailed summary of each section can be found at the end of this section. Furthermore, the current research issues in three areas and overall are proposed. In response to these problems, four directions for future research are presented: (1) integrating the chatter prediction, detection and suppression units into a smart machine tool or smart spindle; (2) high speed and real-time wireless transmission with high sample rate; (3) advanced real-time data processing and decision-making methods; (4) integrated chatter monitoring system involving the thin-walled parts with the complex surface.
•Methods to construct high-fidelity digital twin for automation systems is introduced.•Network of interfaces enabling communications among system components is built.•Manufacturing intelligence is ...realized by training Deep Reinforcement Learning.•A smart dynamic scheduler is developed for continuous process optimization.
Filling the gaps between virtual and physical systems will open new doors in Smart Manufacturing. This work proposes a data-driven approach to utilize digital transformation methods to automate smart manufacturing systems. This is fundamentally enabled by using a digital twin to represent manufacturing cells, simulate system behaviors, predict process faults, and adaptively control manipulated variables. First, the manufacturing cell is accommodated to environments such as computer-aided applications, industrial Product Lifecycle Management solutions, and control platforms for automation systems. Second, a network of interfaces between the environments is designed and implemented to enable communication between the digital world and physical manufacturing plant, so that near-synchronous controls can be achieved. Third, capabilities of some members in the family of Deep Reinforcement Learning (DRL) are discussed with manufacturing features within the context of Smart Manufacturing. Trained results for Deep Q Learning algorithms are finally presented in this work as a case study to incorporate DRL-based artificial intelligence to the industrial control process. As a result, developed control methodology, named Digital Engine, is expected to acquire process knowledges, schedule manufacturing tasks, identify optimal actions, and demonstrate control robustness. The authors show that integrating a smart agent into the industrial platforms further expands the usage of the system-level digital twin, where intelligent control algorithms are trained and verified upfront before deployed to the physical world for implementation. Moreover, DRL approach to automated manufacturing control problems under facile optimization environments will be a novel combination between data science and manufacturing industries.
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