Social Manufacturing Research Articles

Investigation on matching of individualised requirements and shared manufacturing resources in the context of shared factory runtime

Shared Manufacturing (SharedM) empowers individuals to engage in manufacturing via order-driven, resource-sharing processes, embodying the principles of Industry 5.0 and a human-centric approach. This study tackles the challenges of requirement-resource matching to realise Production Planning and Scheduling (PP&S), stemming from conflicts in the distribution and ownership of shared Manufacturing Resources (MRs). We introduce a factory-level runtime framework alongside a self-organising redundant matching algorithm based on sample average approximation to efficiently manage shared MRs and individualised orders. Then, an industrial case study illustrates the application from the modelling of MRs and individualised requirements, to the generation of the final matching Gantt chart. The findings demonstrate that the proposed shared factory can align idle MRs with personalised consumer demands effectively. This paper presents a viable solution for implementing shared factories in industrial settings, providing valuable insights into social manufacturing, SharedM, and novel manufacturing paradigms focused on human-centric values.

THE STRATEGIC ROLE OF FPSO IN DEEPWATER EXPLORATION: INTEGRATING SOCIAL MANUFACTURING SYSTEMS WITHIN INDUSTRY 5.0

This study analyzes the role of Floating Production, Storage, and Offloading units (FPSO) in deepwater oil and gas exploration within the context of Industry 5.0, focusing on integrating social manufacturing systems. Reviewing common offshore exploration methods, the study identifies FPSOs as key catalysts for innovation, energy efficiency, and sustainability. The research concludes that combining FPSO with Industry 5.0 can optimize horizontal and vertical production processes, enhancing predictive maintenance, advanced safety, and waste management. Continuous innovation is highlighted as vital for addressing future challenges in deepwater exploration.

AI’s effect on innovation capacity in the context of industry 5.0: a scoping review

The classic literature about innovation conveys innovation strategy the leading and starting role to generate business growth due to technology development and more effective managerial practices. The advent of Artificial Intelligence (AI) however reverts this paradigm in the context of Industry 5.0. The focus is moving from “how innovation fosters AI” to “how AI fosters innovation”. Therefore, our research question can be stated as follows: What factors influence the effect of AI on Innovation Capacity in the context of Industry 5.0? To address this question we conduct a scoping review of a vast body of literature spanning engineering, human sciences, and management science. We conduct a keyword-based literature search completed by bibliographic analysis, then classify the resulting 333 works into 3 classes and 15 clusters which we critically analyze. We extract 3 hypotheses setting associations between 4 factors: company age, AI maturity, manufacturing strategy, and innovation capacity. The review uncovers several debates and research gaps left unsolved by the existing literature. In particular, it raises the debate whether the Industry5.0 promise can be achieved while Artificial General Intelligence (AGI) remains out of reach. It explores diverging possible futures driven toward social manufacturing or mass customization. Finally, it discusses alternative AI policies and their incidence on open and internal innovation. We conclude that the effect of AI on innovation capacity can be synergic, deceptive, or substitutive depending on the alignment of the uncovered factors. Moreover, we identify a set of 12 indicators enabling us to measure these factors to predict AI’s effect on innovation capacity. These findings provide researchers with a new understanding of the interplay between artificial intelligence and human intelligence. They provide practitioners with decision metrics for a successful transition to Industry 5.0.

Automatic blockchain network configuration and optimisation for credible and authorised data sharing in social manufacturing networks

The management of social manufacturing networks (SMN) faces sophisticated situations due to the customer's personalisation demands, leading to reduced trust among entities. Therefore, credible and authorised data sharing among different participants becomes vital to operate SMN well. Although blockchain has been applied to SMN to ensure credibility and authority, there is little work on how to configure and optimise a blockchain network for SMN, especially when the structure of SMN changes over time. This paper discusses an automatic blockchain network configuration and optimisation mechanism to enable credible and authorised data sharing in SMN. First, a system architecture for automatic blockchain network configuration and optimisation is built. Then, the structure of SMN and blockchain network model are established using complex network and hypergraph. Then, an automatic blockchain network configuration mechanism is studied to configure blockchain networks from certain SMN structures. Afterward, an automatic blockchain network optimisation mechanism is studied to optimise blockchain networks for SMN when dynamic events occur. Finally, the mechanisms are validated through a case study of SMN to produce air conditioners. The results show that our mechanisms can help core enterprises efficiently deploy a blockchain network for credible and authorised data sharing among different participants in the SMN.

A Novel Industrial Software Model for Social Manufacturing

Abstract Distinct from the conventional manufacturing paradigm, the functioning of the Social Manufacturing (SocialM) model relies on a vast and dispersed array of social manufacturing resources. SocialM communities/groups emerge autonomously through business interactions, resource self-organization, and production information sharing, all driven by product orders within the SocialM system. This necessitates a more open, distributed, and autonomous software model to facilitate the allocation and operation of social manufacturing resources in support of the SocialM model. Consequently, this paper introduces a novel software development model designed specifically for SocialM. This proposed model employs business interaction software as the system's primary control portal, enabling efficient management of resources and users based on the social manufacturing communities/groups. Additionally, it facilitates the administration and oversight of numerous applications by utilizing an industrial Appstore. Moreover, the paper delves into the technological roadmap for realizing the essential technologies of the software model by integrating multi-agent and Message Queuing Telemetry Transport (MQTT) technologies. A 3D printing task simulation case was employed to demonstrate the model's working principle, which served to confirm the reliability and scalability of the SocialM software, as well as the feasibility of the SocialM theory.

Collective intelligence-driven 3D printing factory for social manufacturing: implementing a testbed for industrial application

ABSTRACT The emergence of 3D printing technology has imbued the mass customization production model with novel implications. Concurrently, investigations into social manufacturing (SocialM) and collective intelligence present a fresh challenge for the 3D printing industry in their pursuit of realizing customized mass production. However, there is still a lack of investigation on the technical implementation and application scenarios of SocialM, and it hinders the development of SocialM from theory to industrial application. To mitigate this gap, firstly a five-layer framework based on collective intelligence for the configuration of design-production-service integrated 3D printing factory is established, together with the key enabling techniques that support the configuration and operation of the factory from social interaction software perspective and cyber-physical-social interconnection perspective. Secondly, the running logic of the 3D printing factory is demonstrated, and it starts from order generation to order completion. Thirdly, a testbed of the 3D printing factory is built, which contains both social interaction software and physical production hardware environments, and a production order of a 3D printed robotic arm is used to verify the feasibility of the testbed and the configuration and operation theories of SocialM. The work in this paper provides technical solutions for the industrial application of SocialM.

Bibliometric Analysis and Systematic Literature Review in Social Manufacturing

Abstract This study aims to map out social manufacturing research and the distribution of social manufacturing research, which can be used as a reference for social manufacturing research. Various studies on social manufacturing have been carried out to explore various insights on social manufacturing in different domains. This study was conducted in 2021, and explored the social manufacturing literature using bibliometric analysis methods. The approach used in the bibliometric analysis is the citation analysis to see one article cited by another article, and the co-citation analysis approach to find two or more articles cited by one article. In this study, the data source used is from the Scopus database, with social manufacturing keyword. The results show the types of analysis that have been processed include co-authorship (authors, organizations, countries), citations (authors), and co-citations (sources, cited authors). This research still has limitations, because it use the Scopus database only, so for further research, it could be added with other sources, such as Web of Science, PubMed, Crossref, etc. In addition, there are still many visualization results using VOSviewer software that could be explored further, such as the type of analysis citation-document, citation-organization, bibliographic coupling, which has not been discussed in this study.

The survey on multi-source data fusion in cyber-physical-social systems: Foundational infrastructure for industrial metaverses and industries 5.0

As the concept of Industries 5.0 develops, industrial metaverses are expected to operate in parallel with the actual industrial processes to offer “Human-Centric” Safe, Secure, Sustainable, Sensitive, Service, and Smartness “6S” manufacturing solutions. Industrial metaverses not only visualize the process of productivity in a dynamic and evolutional way, but also provide an immersive laboratory experimental environment for optimizing and remodeling the process. Besides, the customized user needs that are hidden in social media data can be discovered by social computing technologies, which introduces an input channel for building the whole social manufacturing process including industrial metaverses. This makes the fusion of multi-source data cross Cyber–Physical–Social Systems (CPSS) the foundational and key challenge. This work firstly proposes a multi-source-data-fusion-driven operational architecture for industrial metaverses on the basis of conducting a comprehensive literature review on the state-of-the-art multi-source data fusion methods. The advantages and disadvantages of each type of method are analyzed by considering the fusion mechanisms and application scenarios. Especially, we combine the strengths of deep learning and knowledge graphs in scalability and parallel computation to enable our proposed framework the ability of prescriptive optimization and evolution. This integration can address the shortcomings of deep learning in terms of explainability and fact fabrication, as well as overcoming the incompleteness and the challenges of construction and maintenance inherent in knowledge graphs. The effectiveness of the proposed architecture is validated through a parallel weaving case study. In the end, we discuss the challenges and future directions of multi-source data fusion cross CPSS for industrial metaverses and social manufacturing in Industries 5.0.

A Collaborative AR/VR Platform for Social Manufacturing

Augmented Reality (AR) and Virtual Reality (VR) technologies have significantly progressed in recent years, revolutionizing a diverse set of industries, mainly the industrial metaverse in cultural heritage and the cultural and creative sectors. In the context of social manufacturing, there's a pronounced need for platforms that foster collaboration. This paper introduces a collaborative AR/VR platform for social manufacturing, emphasizing the need for a co-creation environment. This platform supports real-time customized fabrication of 3D objects through rapid manufacturing within an AR/VR-based collaborative framework, further enhancing shared awareness in social manufacturing endeavors. The platform provides a robust toolkit for AR/VR applications that enables real-time interaction with virtual entities and seamless integration with the backend databases of online marketplaces. Designed to be universally adaptable, the toolkit is indifferent to the type of database it interacts with, and can function on a wide range of platforms including Web VR, Mobile AR, and HoloLens AR. A use case involving a reference chair model demonstrates the toolkit's capabilities, highlighting its potential to transform experiences within AR/VR landscapes. By offering a seamless fusion of digital content within physical environments, this AR/VR toolkit signifies a considerable advancement towards the intersection of digital and physical realities, paving the way of immersive experiences.

A Granular-Computing-Based Data-Sharing Decision-Making Method for Enabling Blockchain-Based Order Tracking in Social Manufacturing

A Granular-Computing-Based Data-Sharing Decision-Making Method for Enabling Blockchain-Based Order Tracking in Social Manufacturing

Modeling and Solving the Allocation Problem of Spatiotemporal Crowdsourced Logistics Tasks in Social Manufacturing

Modeling and Solving the Allocation Problem of Spatiotemporal Crowdsourced Logistics Tasks in Social Manufacturing

Socialized Incubator for Startup Companies: A New Paradigm to Construct Multilayer Industrial Ecosystem Under the Context of Social Manufacturing

Socialized Incubator for Startup Companies: A New Paradigm to Construct Multilayer Industrial Ecosystem Under the Context of Social Manufacturing

Physics-Driven Data Collection in 3-D Printing: Traversing the Realm of Social Manufacturing

Physics-Driven Data Collection in 3-D Printing: Traversing the Realm of Social Manufacturing

A Survey on Social Manufacturing: A Paradigm Shift for Smart Prosumers

The intelligent manufacturing is a complex engineering system, and the cyber–physical systems (CPSs) and the industrial Internet are the preliminary infrastructures. When cyber–physical–social systems (CPSSs) are formed by extending CPS into the social aspect, Societies 5.0 era is coming. In the Societies 5.0 era, social manufacturing (SM) is an innovative manufacturing solution for intelligent manufacturing. In this article, a survey on SM is introduced. It includes the definition and theory of SM, and comparison between SM and other manufacturing paradigms. Moreover, the key supporting technologies are presented, which can be used to realize SM, such as blockchain, 3-D printing, and big data. Then, the applications of SM to industries are illustrated. The SM has broad application prospects in the high-end customized, distributed manufacturing, and other intelligent manufacturing. Finally, the challenges and future trends are discussed.

Blockchain-based fine-grained digital twin sharing framework for social manufacturing

The lack of digitalization basis of social manufacturing resources has brought barriers to perform efficient production coordination in social manufacturing network. Digital twin can be a promising carrier for bridging this gap. However, the complexity of digital twin makes small and medium enterprises, and individual resource owners in social manufacturing hard to apply this technology. Benefiting from the convenience of sharing in social communities, this paper presents a blockchain-based fine-grained digital twin sharing framework to accelerate the popularization and iteration of digital twins in social manufacturing. A hybrid design method, called model–view–controller domain-driven design (MVC-DDD), is proposed to decompose and decouple complicated digital twin instances into small granules from software architecture perspective. Then, blockchain is introduced as the underlying infrastructure to innovate the specific sharing mechanisms using zero-knowledge proof for decentralized registering, authorizing, and extracting digital twin granules. Moreover, the sharing incentive mechanism is also explored to ensure the feasibility and sustainability for the whole sharing behaviors. Finally, different types of 3D printers are selected for implementing the referential digital twin architecture for generating the sharable granules. A series of experiments are conducted to show that our sharing mechanism has a good performance in terms of throughput, latency and network bandwidth. Additionally, the incentive mechanism is also analyzed generate the management implications to facilitate the digital twin providers with different scales for sharing.

Modeling and IAHA Solution for Task Scheduling Problem of Processing Crowdsourcing in the Context of Social Manufacturing

The paper addresses the discrete characteristics of the processing crowdsourcing task scheduling problem in the context of social manufacturing, divides it into two subproblems of social manufacturing unit selecting and subtask sorting, establishes its mixed-integer programming with the objective of minimizing the maximum completion time, and proposes an improved artificial hummingbird algorithm (IAHA) for solving it. The IAHA uses initialization rules of global selection, local selection, and random selection to improve the quality of the initial population, the Levy flight to improve guided foraging and territorial foraging, the simplex search strategy to improve migration foraging to enhance the merit-seeking ability, and the greedy decoding method to improve the quality of the solution and reduce solution time. For the IAHA, orthogonal tests are designed to obtain the optimal combination of parameters, and comparative tests are made with variants of the AHA and other algorithms on the benchmark case and a simulated crowdsourcing case. The experimental results show that the IAHA can obtain superior solutions in many cases with economy and effectiveness.

A dynamic distributed edge-cloud manufacturing with improved ADMM algorithms for mass personalization production

The primary feature of Industry 4.0 is MPP (mass personalization production), which requires that consumers’ individual requests are met in large-scale production. Under MPP, there is a multitude of subtasks decomposed from production tasks that are derived from individualized requests, and allocating these subtasks properly brings high economic benefits. However, existing approaches to achieve MPP, such as cloud manufacturing and social manufacturing, generally can not provide customers with deep participation in the entire production cycle, or respond to consumers’ modification needs by a triggered mechanism. Besides, some methods are of centralized architecture, which is vulnerable to single point error and with large cloud load that is not conducive to quickly responding to consumers’ dynamic demand changes. Therefore, this paper proposes a dynamic edge-cloud manufacturing mode for MPP, which can make subtask allocation with high economic benefit through distributed computing and implementing modifications of alternating direction method of multiplier (ADMM) algorithm. Also, it proposes an original improved ADMM algorithm, named Relaxation-Based ADMM algorithm, to increase the optimization speed in large-scale cases. The experimental results show that the proposed method generally obtains a superior solution under a certain iteration count.

Social Manufacturing On Integrated Production System: A Systematic Literature Review

Abstract Currently, the development of information technology in all fields is very rapid, including in the manufacturing sector, thus making the social environment of manufacturing change a lot. Traditional manufacturing research focuses on applying technology, such as production models, supplier arrangements, automation, advanced machines in the manufacturing industry, etc. Global competition and increasingly diverse customer demands are a phenomenon that occurs today. Social manufacturing is a new distributed, collaborative, and intelligent manufacturing, that every consumer can participate in the entire process of product design, manufacture, and even marketing. This study aims to examine the development of social manufacturing systems and the latest research in social manufacturing. The method used is to review the latest papers related to social manufacturing that provide a new paradigm for the manufacturing industry, then map based on the concepts, frameworks, and technologies used. The results of this review paper are in the form of mapping research papers taken from the database of a reputable international journal, Scopus. The mapping includes data on the author, co-author, terms on social manufacturing paper, year of publication, type of document and publisher. This systematic literature review can be one of the references for basic research on social manufacturing.

An Efficient Product-Customization Framework Based on Multimodal Data under the Social Manufacturing Paradigm

With improvements in social productivity and technology, along with the popularity of the Internet, consumer demands are becoming increasingly personalized and diversified, promoting the transformation from mass customization to social manufacturing (SM). How to achieve efficient product customization remains a challenge. Massive multi-modal data, such as text and images, are generated during the manufacturing process. Based on the data, we can use large-scale pre-trained deep learning models and neural radiation field (NeRF) techniques to generate user-friendly 3D contents for 3D Printing. Furthermore, by the cloud computing technology, we can achieve more efficient SM operations. In this paper, we propose an efficient product-customization framework that can provide new ideas for the design, implementation, and optimization of collaborative production, and can provide insights for the upgrading of manufacturing industries.

Making, Hacking, Coding: Fablabs as Intermediary Platforms for Modes of Social Manufacturing

The article suggests that the emergence of FabLabs and other collaborative spaces of innovation imply new modes of manufacturing, based on a more social, inclusive, and open approach than current industrial mass-production models. We argue that FabLabs are platforms of social manufacturing, allowing different combinations of interactions between industries and individuals, through their activities around making (producing goods from raw materials), hacking (re-using/combining produced goods), and coding (producing and re-using digital goods). The article also defines four different modes of social manufacturing depending on the technologies used (open or proprietary) and the location of manufacturing (local or industrial). The article contributes to the literature on collaborative spaces by explaining prospective scenarios of development in relationship to new modes of production. It also complements the literature by contextualizing the physical spaces where social manufacturing takes place.JEL codes: D20, L23, L60