Social Manufacturing Paradigm Research Articles

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.

Fundamentals and new achievements in feature-based modeling, a review

Feature-based modeling approach has been widely utilized in various applications of product and process engineering. Initially, it was introduced for geometry representation in computer-aided design (CAD) utilities. With further developments in computer-aided engineering, process planning, and manufacturing, feature-based modeling gained higher importance and role for the integration of these systems. Despite considerable efforts to gain domain-neutral definition for features, they have been mostly defined for specific applications that restrict their reusability for different systems. Recent progress in the Internet of Things (IoT), Artificial Intelligence (AI), Big Data, in addition to additive and hybrid manufacturing techniques, has realized a new manufacturing paradigm called Social Manufacturing which needs significant cross-domain interactions and intensifies the demand for feature interoperability. In this paper, after presenting a brief history of the feature-based modeling approach and its development challenges, we focus on finding the approaches to tackle feature interoperability issues and investigate the role of features in Model-Based Definition (MBD) and the Social Manufacturing paradigm. Also, future research pathways to develop the feature-based modeling approach is presented.

Shared factory: A new production node for social manufacturing in the context of sharing economy

Manufacturing industry is heading towards socialization, interconnection and platformization. Motivated by the infiltration of sharing economy usage in manufacturing, this article addresses a new factory model – shared factory, and provides a theoretical architecture and some actual cases for manufacturing sharing. Concepts related to three kinds of shared factories which deal, respectively, with sharing production-orders, manufacturing-resources and manufacturing-capabilities are defined accordingly. These three kinds of shared factory modes can be used for building correspondent sharing manufacturing ecosystems. On the basis of sharing economic analysis, we identify feasible key-enabled technologies for configuring and running a shared factory. At the same time, opportunities and challenges of enabling the shared factory are also analysed in detail. In fact, shared factory, as a new production node, enhances the sharing nature of social manufacturing paradigm, fits the needs of light assets and gives us a new chance to use socialized manufacturing resources. It can be drawn that implementing a shared factory would reach a ‘win-win’ way through production value-added transformation and social innovation.

Makerchain: A blockchain with chemical signature for self-organizing process in social manufacturing

The growth of personalized demands requires socialized resources to timely self-organize themselves with crowd intelligence for co-creating open architecture products. This social manufacturing paradigm drives an increased demand for makers to track the authenticity and quality of products. A new decentralized blockchain-driven model, named Makerchain, is presented to handle the cyber-credit of social manufacturing among various makers. An anti-counterfeiting method composed of chemical signature is proposed to represent unique features of personalized products. Twinning unique signature data to blockchain and other functional databases is realized and anticipated to make manufacturing service transactions among makers more trustworthy. Based on an automated execution mechanism of smart contracts among makers, a decentralized manufacturing network can be enabled for automating transactions among makers, as well as third-party verification of product lifecycle through a trail of historic events. A Makerchain Decentralized Application (DApp) is presented to demonstrate the proposed approach through which clustered makers can self-organizing themselves around personalized demands.

Analysis of personalized production organizing and operating mechanism in a social manufacturing environment

Current manufacturing industry has witnessed the trends of socialization, personalization, and servitization, which prompts the appearance of social manufacturing paradigm. This article addresses the personalized production organizing and operating mechanism in social manufacturing. Socialized manufacturing resources spring up in fine-grained markets and self-organize into social communities to participate in different phases of product lifecycle. A socialized production network integrating different socialized manufacturing resources/social communities is organized to create personalized products. The framework and key aspects for socialized production network operating are discussed. The discussion on personalized production organizing and operating mechanism will reveal the future manufacturing philosophy.

A deep learning approach for relationship extraction from interaction context in social manufacturing paradigm

There is an increasing unstructured text data produced in cross-enterprise social interaction media, forming a social interaction context that contains massive manufacturing relationships, which can be potentially used as decision support information for cross-enterprise manufacturing demand-capability matchmaking. How to enable decision-makers to capture these relationships remains a challenge. The text-based context contains high levels of noise and irrelevant information, causing both highly complexity and sparsity. Under this circumstance, instead of exploiting man-made features carefully optimized for the relationship extraction task, a deep learning model based on an improved stacked denoising auto-encoder on sentence-level features is proposed to extract manufacturing relationships among various named entities (e.g., enterprises, products, demands, and capabilities) underlying the text-based context. Experiment results show that the proposed approach can achieve a comparable performance with the state-of-the-art learning models, as well as a good practicality of its’ web-based implementation in social manufacturing interaction context. The ultimate goal of this study is to facilitate knowledge transferring and sharing in the context of enterprise social interaction, thereby supporting the integration of the resources and capabilities among different enterprises.

Mining and Matching Relationships From Interaction Contexts in a Social Manufacturing Paradigm

There is an increasing use of social interaction contexts in the cross-enterprise manufacturing problem solving. To transform these massive and unstructured data into decision-support information for cross-enterprise manufacturing demand-capability matching, we present automated solutions to two phases: (1) extracting relationships based on a semi-supervised learning approach to derive formalized heterogeneous manufacturing network from the unstructured text-based context that contains high levels of noise and irrelevant information and (2) matching group-level relationships among the entities in the established manufacturing network. The extracting phase formulates network data using multiattributed graph that can encode various entities and relationships. The matching phase is based on probabilistic multiattributed graph matching, and implemented using distributed message passing algorithm. We developed a prototype system to verify the proposed model, which is also flexible to new domains of contexts and scale to large datasets. The ultimate goal of this paper is to facilitate knowledge transferring and sharing in the context of cross-enterprise social interaction, thereby supporting the integration of the resources and capabilities among different enterprise.