scispace - formally typeset
Search or ask a question
Journal Article•DOI•

Cloud-based design and manufacturing

Dazhong Wu1, David W. Rosen1, Lihui Wang2, Dirk Schaefer1•
Georgia Institute of Technology1, Royal Institute of Technology2
01 Feb 2015-Computer-aided Design (Elsevier)-Vol. 59, pp 1-14
TL;DR: The development of a smart delivery drone is presented as an idealized CBDM example scenario and a corresponding CBDM system architecture is proposed that incorporates CBDM-based design processes, integrated manufacturing services, information and supply chain management in a holistic sense.
Abstract: Cloud-based design manufacturing (CBDM) refers to a service-oriented networked product development model in which service consumers are enabled to configure, select, and utilize customized product realization resources and services ranging from computer-aided engineering software to reconfigurable manufacturing systems. An ongoing debate on CBDM in the research community revolves around several aspects such as definitions, key characteristics, computing architectures, communication and collaboration processes, crowdsourcing processes, information and communication infrastructure, programming models, data storage, and new business models pertaining to CBDM. One question, in particular, has often been raised: is cloud-based design and manufacturing actually a new paradigm, or is it just "old wine in new bottles"? To answer this question, we discuss and compare the existing definitions for CBDM, identify the essential characteristics of CBDM, define a systematic requirements checklist that an idealized CBDM system should satisfy, and compare CBDM to other relevant but more traditional collaborative design and distributed manufacturing systems such as web- and agent-based design and manufacturing systems. To justify the conclusion that CBDM can be considered as a new paradigm that is anticipated to drive digital manufacturing and design innovation, we present the development of a smart delivery drone as an idealized CBDM example scenario and propose a corresponding CBDM system architecture that incorporates CBDM-based design processes, integrated manufacturing services, information and supply chain management in a holistic sense. We present a new paradigm in digital manufacturing and design innovation, namely cloud-based design and manufacturing (CBDM).We identify the common key characteristics of CBDM.We define a requirement checklist that any idealized CBDM system should satisfy.We compare CBDM with other relevant but more traditional collaborative design and distributed manufacturing systems.We describe an idealized CBDM application example scenario.

Summary (5 min read)

Jump to: [1. Introduction] – [2.1 Engineering design] – [2.2 Manufacturing systems] – [3. Characteristics and requirements for cloud-based design and manufacturing systems] – [4.1 Computing architecture] – [4.2 Design communication] – [4.3 Sourcing process] – [4.4 Information and communication infrastructure] – [4.5 Programming model] – [4.6 Data storage] – [4.7 Business model] – [5. Cloud-based design and manufacturing example scenario] – [5.2 Cloud-based design] – [5.3 Cloud-based manufacturing] and [6. Conclusion]

1. Introduction

  • In its initial application field of information technology (IT), cloud computing has proven to be a disruptive technology.
  • Some of its key characteristics include agility, scalability and elasticity, on-demand computing, and self-service provisioning [2].
  • Adapted from the original cloud computing paradigm and introduced into the realm of computer-aided product development, cloud-based design and manufacturing (CBDM) is gaining significant momentum and attention from both academia and industry.
  • 3D Hubs has established an innovative business model that creates and delivers value to both 3D printing service consumers and providers.
  • Section 3 introduces key characteristics 6 of CBDM and presents a requirements checklist that CBDM systems should satisfy.

2.1 Engineering design

  • Many researchers have proposed descriptive models that abstract the engineering design process.
  • Among these models, one of the most widely known is perhaps the one proposed by Pahl and Beitz.
  • It presents a systematic engineering design approach including four core design phases: product planning and clarifying the task, conceptual design, embodiment design, and detail design [16].
  • It is argued that the first CAD system, SKETCHPAD, was developed at MIT by Ivan Sutherland in the early 1960s [21].
  • Hard to be implemented on the Internet; 1990s Distributed Strong server + thin client; Light-weighted client mechanism;.

2.2 Manufacturing systems

  • Similar to design systems, manufacturing systems have undergone a number of major transitions due to changing market demands and emerging technologies [28-29].
  • Time Systems Configuration Characteristics 1900s Assembly line Centralized Reduced labor costs;.
  • In the 1960s, to reduce manufacturing costs, TPSs, also known as just-in-time production systems, were devised.
  • Specifically, the major advantage of an FMS is that it allows for variation in both parts and assemblies; however, its implementation is usually costly.
  • With the development of the Internet, distributed manufacturing systems have been increasingly adopted by industry; two major approaches for distributed manufacturing are web- and agent-based manufacturing systems.

3. Characteristics and requirements for cloud-based design and manufacturing systems

  • According to the existing definitions for CBDM presented in Section 1, Table 4 lists some common key characteristics of CBDM and compares CBDM with other relevant distributed design and manufacturing systems.
  • As shown in Table 4, CBDM provides significantly more benefits than web- and agent-based systems.
  • Should provide social media to support communication, information and knowledge sharing in the networked design and manufacturing environment R2.
  • Should provide a multi-tenancy environment where a single software instance can serve multiple tenants R5.
  • To streamline workflow and improve business processes, a CBDM system should provide an online quoting engine to generate instant quotes based on design and manufacturing specifications.

4.1 Computing architecture

  • From a computing perspective, the difference between web- and agent-based applications and cloudbased applications is two-fold: multi-tenancy and virtualization.
  • Fig. 1 illustrates a unified computing architecture for CBDM systems that is distinguished from web- and agent-based design and manufacturing systems.
  • To improve the negotiation process between service providers and consumers as well as enhance security and privacy in CBDM systems, a cloud broker (e.g., cloud-based storage and computing brokers) can help users identify, customize, and integrate existing design and manufacturing services.
  • In addition, as shown in the virtual and physical layers in Fig. 1, virtualization can improve the efficiency and availability of computing and IT resources by re-allocating hardware dynamically to applications based on their need.

4.2 Design communication

  • As stated before, the design of any product is an inherently social, technical process.
  • Because of the use of social media in CBD settings, design communication can be improved through multiple information channels (e.g., social network sites and product review sites) in 15 which information flow can take place in multiple directions as shown in Fig. 2 (b) [41].
  • Social media allows design engineers to collaborate with customers concurrently by receiving instant feedback from customers.
  • Specifically, computer-aided design, engineering analysis, and manufacturing tools in CBDM settings will allow users in a crossdisciplinary design team to simultaneously create and modify design features of a product model.
  • Customers Marketing Analysts Designers Manufacturing Engineers Customers Marketing Analysts Designers Manufacturing Engineers (a) A linear sequence of design phases in traditional design (b) A linear sequence of design phases with more information channels in CBD 16.

4.3 Sourcing process

  • A crowdsourcing process for RFQs in CBDM systems, also known as Fig.3.
  • CBDM enables service consumers to quickly and easily locate qualified service providers who offer design and manufacturing services such as CNC machining, injection molding, casting, or 3D printing through a cloud-based sourcing platform.
  • The search engine consists of a crawler, indices, and query servers.
  • The crawler gathers manufacturing-related data (e.g., process variables, machine specifications) from databases, document servers, and other content sources, and it stores them in the index.
  • Moreover, in comparison with commercial quoting systems such as Quickparts.com [12] and MFG.com [45], the proposed cloud-based sourcing platform can not only conduct quoting for design and manufacturing services such as rapid prototyping, injection molding, and casting, but also conduct manufacturing and computing resource allocation, and scheduling activities.

4.4 Information and communication infrastructure

  • From an information and communication infrastructure perspective, CBDM employs the IoT (e.g., RFID), smart sensor, and wireless devices (e.g., smart phone) to collect real-time design- and manufacturing-related data as shown in Fig.
  • Information and communication infrastructure in CBDM systems, also known as 4. Fig.4.
  • The essence of IoT and embedded sensors is to capture events (e.g., inventory level), to represent physical objects (e.g., machine tools) in digital form, and finally to connect machines with people.
  • With the big data generated by the IoT-related Data Acquisition System Smart Phone Camera Barcode Reader RFID Reader Smart Sensor HMI Infrastructure-as-a-Service 18 devices, engineers may apply big data analytics for forecasting, proactive maintenance, and automation.
  • Such seamless connections cannot be provided in web- and agent-based design and manufacturing systems because of their limited data acquisition and computing capabilities.

4.5 Programming model

  • From a programming model perspective, MapReduce, a parallel programming model, enables CBDM systems to process large data sets which web- and agent-based manufacturing systems are not able to deal with.
  • One of the most well-known open source implementations of the MapReduce model is Hadoop.
  • Similar to other parallel programming models, Hadoop divides computationally extensive tasks into small fragments of work, and each work unit is processed on a computer node in a Hadoop cluster [46].
  • The worker nodes process the smaller sub-tasks, and send the answer back to the master node.
  • Such a parallel programming model enables CBDM to handle big data generated in design and manufacturing.

4.6 Data storage

  • From a data storage perspective, with regard to web- and agent-based design and manufacturing, product-related data are stored at designated servers, and users know where these data are as well as who is providing them.
  • With regard to CBDM, networked enterprise data are stored not only on users’ computers, but also in virtualized data centers that are generally hosted by third parties (see the virtual and physical layers in Fig. 1).
  • In other words, the users may neither exactly know who the service providers are nor where the data are stored.
  • The data may be accessed through a web service application programming interface (API) or a web browser.

4.7 Business model

  • From a business model perspective, the significant difference between CBDM and web- and agentbased design and manufacturing is that CBDM involves new business models; but web- and agent-based design and manufacturing paradigms do not.
  • That is, CBDM does not simply provide new technologies; it also involves how design and manufacturing services can be delivered (e.g., IaaS, PaaS, HaaS, and SaaS), how services can be deployed (e.g., private cloud, public cloud and hybrid cloud), and how services can be paid for (i.e., pay-per-use).
  • A key driver of CBDM is the pay-per-use model that has the potential to reduce up-front investments on IT and manufacturing infrastructure for small- and mediumsized enterprises (SMEs).
  • Instead of purchasing manufacturing equipment and software licenses, CBDM users can pay a periodic subscription or utilization fee with minimal upfront costs.
  • Likewise, scalability and elasticity allow users to avoid over purchase of computing and manufacturing capacities.

5. Cloud-based design and manufacturing example scenario

  • The authors present an idealized design and manufacturing scenario in a hypothetical CBDM setting based on currently existing and potentially new cloud-based service offerings.
  • The authors present how the integration of existing and potentially new services and technologies may enhance the drone development process.
  • The authors propose the system architecture of CBDM as shown in Fig. 6 to illustrate the service models (i.e., IaaS, PaaS, HaaS, and SaaS), the existing and potentially new service providers, and the delivery drone development process as an example.
  • Google BigQuery and Salesforce.com allow the team to process these massively large datasets.
  • In the conceptual design stage, based on these design requirements, the team proposes function structures, working principles, engineering and economic constraints using PaaS and SaaS.

5.2 Cloud-based design

  • From a requirements elicitation perspective, CBD allows design engineers to conduct market research more effectively and efficiently through social media.
  • After collecting these data from social media, design engineers can elicit design requirements and customer preference using cloud-based big data analytics tools such as Google BigQuery [51].
  • Requirements elicitation based on customer reviews, also known as 24 Fig.7.
  • These data mining and visualization technologies used in CBD have the potential to significantly increase the productivity for the drone design process by allowing design engineers to search for the right design information from the right designer.
  • From a computer-aided design perspective, the traditional collaborative design process is typically expensive because it requires substantial computing resources, data consistency, transparent communication and seamless information sharing.

5.3 Cloud-based manufacturing

  • After the detail design phase is finished, the design team needs to build a prototype in a CBM setting.
  • The design team can manufacture the major mechanical components of the drone through cloud-based sourcing platforms (Quickparts, MFG.com, Alibaba.com [55], and i.materialise [56]).
  • Sourcing manufacturing tasks and electronics components to service providers not only allows the design team to save upfront investment in 3D printers and injection molding machines but also allows them to focus on design innovation.
  • Further, the formal representation of manufacturing resources enables the automatic retrieval of the required manufacturing services based on the semantic matchmaking of required and published manufacturing service specifications [58].
  • As stated before, CBM allows for rapid manufacturing capacity scalability by sourcing manufacturing tasks to global suppliers.

6. Conclusion

  • The authors discussed and compare the existing definitions for CBDM, identified common key characteristics, defined a requirements checklist that any idealized CBDM system should satisfy, and compared CBDM to other relevant but more traditional collaborative design and distributed manufacturing systems from a number of perspectives.
  • Thus far, a few prototype systems achieved some functions in the requirement checklist; however, none of the existing systems satisfies all the requirements that the authors defined.
  • Realtime tracking/monitoring data will enable CBDM systems to track and trace specific objects, to monitor and synchronize material flow in manufacturing, and eventually increase the productivity and efficiency of manufacturing supply chain.

Did you find this useful? Give us your feedback

Figures (2)

Content maybe subject to copyright    Report

Citation for published version:
Wu, D, Rosen, DW, Wang, L & Schaefer, D 2015, 'Cloud-based design and manufacturing: A new paradigm in
digital manufacturing and design innovation', Computer-Aided Design, vol. 59, pp. 1-14.
https://doi.org/10.1016/j.cad.2014.07.006
DOI:
10.1016/j.cad.2014.07.006
Publication date:
2015
Document Version
Peer reviewed version
Link to publication
Publisher Rights
CC BY
Creative Commons Attribution Non-Commercial No Derivatives licence
University of Bath
Alternative formats
If you require this document in an alternative format, please contact:
openaccess@bath.ac.uk
General rights
Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners
and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.
Take down policy
If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately
and investigate your claim.
Download date: 09. Aug. 2022
Accepted Manuscript
Cloud-based design and manufacturing: A new paradigm in digital
manufacturing and design innovation
Dazhong Wu, David W. Rosen, Lihui Wang, Dirk Schaefer
PII: S0010-4485(14)00156-0
DOI: http://dx.doi.org/10.1016/j.cad.2014.07.006
Reference: JCAD 2229
To appear in: Computer-Aided Design
Received date: 8 April 2014
Accepted date: 16 July 2014
Please cite this article as: Wu D, Rosen DW, Wang L, Schaefer D. Cloud-based design and
manufacturing: A new paradigm in digital manufacturing and design innovation.
Computer-Aided Design (2014), http://dx.doi.org/10.1016/j.cad.2014.07.006
This is a PDF file of an unedited manuscript that has been accepted for publication. As a
service to our customers we are providing this early version of the manuscript. The manuscript
will undergo copyediting, typesetting, and review of the resulting proof before it is published in
its final form. Please note that during the production process errors may be discovered which
could affect the content, and all legal disclaimers that apply to the journal pertain.
1
Cloud-Based Design and Manufacturing: A New Paradigm in Digital
Manufacturing and Design Innovation
Dazhong Wu
The G.W. Woodruff School of Mechanical Engineering
Georgia Institute of Technology
Atlanta, Georgia, 30332
Email: dwu42@gatech.edu
David W. Rosen
The G.W. Woodruff School of Mechanical Engineering
Georgia Institute of Technology
Atlanta, Georgia, 30332
Email: david.rosen@me.gatech.edu
Lihui Wang
Department of Production Engineering
KTH Royal Institute of Technology
Stockholm 100 44, Sweden
Email: lihuiw@kth.se
Dirk Schaefer*
The G.W. Woodruff School of Mechanical Engineering
Georgia Institute of Technology
Atlanta, Georgia, 30332
Email: dirk.schaefer@me.gatech.edu
ABSTRACT
Cloud-based design manufacturing (CBDM) refers to a service-oriented networked product
development model in which service consumers are enabled to configure, select, and utilize customized
product realization resources and services ranging from computer-aided engineering software to
reconfigurable manufacturing systems. An ongoing debate on CBDM in the research community revolves
around several aspects such as definitions, key characteristics, computing architectures, communication
and collaboration processes, crowdsourcing processes, information and communication infrastructure,
programming models, data storage, and new business models pertaining to CBDM. One question, in
particular, has often been raised: Is cloud-based design and manufacturing actually a new paradigm, or is
it just ―old wine in new bottles‖? To answer this question, we discuss and compare the existing definitions
*Manuscript_Revised_Unmarked
Click here to view linked References
2
for CBDM, identify the essential characteristics of CBDM, define a systematic requirements checklist
that an idealized CBDM system should satisfy, and compare CBDM to other relevant but more traditional
collaborative design and distributed manufacturing systems such as web- and agent-based design and
manufacturing systems. To justify the conclusion that CBDM can be considered as a new paradigm that is
anticipated to drive digital manufacturing and design innovation, we present the development of a smart
delivery drone as an idealized CBDM example scenario and propose a corresponding CBDM system
architecture that incorporates CBDM-based design processes, integrated manufacturing services,
information and supply chain management in a holistic sense.
Keywords: Cloud-based design and manufacturing; Collaborative design; Distributed manufacturing;
Design innovation; Digital manufacturing.
1. Introduction
In its initial application field of information technology (IT), cloud computing has proven to be a
disruptive technology. It leverages existing technologies such as utility computing, parallel computing,
and virtualization [1]. Some of its key characteristics include agility, scalability and elasticity, on-demand
computing, and self-service provisioning [2]. Adapted from the original cloud computing paradigm and
introduced into the realm of computer-aided product development, cloud-based design and manufacturing
(CBDM) is gaining significant momentum and attention from both academia and industry. Cloud-based
design and manufacturing (CBDM) refers to a service-oriented networked product development model in
which service consumers are enabled to configure, select, and utilize customized product realization
resources and services ranging from CAE software to reconfigurable manufacturing systems. This is
accomplished through a synergetic integration of the four key cloud computing service models:
Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), Hardware-as-a-Service (HaaS), and
Software-as-a-Service (SaaS) [3]. In order to fully grasp the breadth, depth, and opportunities of CBDM
as an emerging paradigm for distributed and collaborative product development [25,64-65], it is advisable
3
to discuss its two counterparts: cloud-based design (CBD) and cloud-based manufacturing (CBM)
separately before shedding more light on how they may act in concert.
Cloud-Based Design (CBD) refers to a networked design model that leverages cloud computing,
service-oriented architecture (SOA), Web 2.0 (e.g., social network sites), and semantic web technologies
to support cloud-based engineering design services in distributed and collaborative environments [4,25].
Some of the important requirements of a CBD system include (1) it must be cloud computing-based; (2) it
must be ubiquitously assessable from mobile devices; and (3) it must be able to manage complex
information flow. A detailed requirements checklist for developing CBD systems will be discussed in
Section 3. While an ideal CBD system does not yet exist, some companies already develop and provide
select critical components for CBD systems. For instance, Autodesk offers a cloud-based platform,
Autodesk 123D [5], which allows users to convert photos of artifacts into 3D models, create or edit the
3D models, and generate associated prototypes with remote 3D printers accessed through the Internet. In
addition, Autodesk offers a cloud-based mobile application, AutoCAD 360 [6], which allows design
engineers to view, edit, and share AutoCAD digital files using mobile devices such as smartphones or
tablets. 100kGrarages.com [7], a social network site for connecting consumers with small and medium-
sized design companies or individual design engineers, allows a service consumer to search for capable
and qualified design service providers in a virtual community by providing consumers with each
alternative service provider’s profile page. Each profile page includes information such as specialties and
sample designs of a service provider.
Cloud-Based Manufacturing (CBM) refers to a networked manufacturing model that exploits on-
demand access to a shared collection of diversified and distributed manufacturing resources to form
temporary, reconfigurable production lines which enhance efficiency, reduce product lifecycle costs, and
allow for optimal resource allocation in response to variable-demand customer generated tasking [8-9].
Table 1 presents another two widely used definitions of CBM. Although each definition may focus on a
unique aspect of CBM, they include common elements such as networked manufacturing, ubiquitous
access, multi-tenancy and virtualization, big data and the IoT, everything-as-a-service (e.g., infrastructure-
Citations
More filters
Journal Article•DOI•
Deep learning for smart manufacturing: Methods and applications

[...]

Jinjiang Wang1, Yulin Ma1, Laibin Zhang1, Robert X. Gao2, Dazhong Wu3 •
China University of Petroleum1, Case Western Reserve University2, University of Central Florida3
01 Jul 2018-Journal of Manufacturing Systems
TL;DR: A comprehensive survey of commonly used deep learning algorithms and discusses their applications toward making manufacturing “smart”, including computational methods based on deep learning that aim to improve system performance in manufacturing.

1,025 citations

Journal Article•DOI•
Smart manufacturing: Past research, present findings, and future directions

[...]

Hyoung Seok Kang1, Ju Yeon Lee2, SangSu Choi1, Hyun Kim3, Jun Hee Park3, Ji Yeon Son3, Bo Hyun Kim2, Sang Do Noh1 •
Sungkyunkwan University1, KITECH2, Electronics and Telecommunications Research Institute3
23 Jan 2016-International Journal of Precision Engineering and Manufacturing-Green Technology
TL;DR: In this article, the authors surveyed and analyzed various articles related to Smart Manufacturing, identified the past and present levels, and predicted the future, and the major key technologies related to smart manufacturing were identified through the analysis of the policies and technology roadmaps of Germany, the U.S., and Korea that have government-driven leading movements for Smart Manufacturing.
Abstract: Today, the manufacturing industry is aiming to improve competitiveness through the convergence with cutting-edge ICT technologies in order to secure a new growth engine. Smart Manufacturing, which is the fourth revolution in the manufacturing industry and is also considered as a new paradigm, is the collection of cutting-edge technologies that support effective and accurate engineering decision-making in real time through the introduction of various ICT technologies and the convergence with the existing manufacturing technologies. This paper surveyed and analyzed various articles related to Smart Manufacturing, identified the past and present levels, and predicted the future. For these purposes, 1) the major key technologies related to Smart Manufacturing were identified through the analysis of the policies and technology roadmaps of Germany, the U.S., and Korea that have government-driven leading movements for Smart Manufacturing, 2) the related articles on the overall Smart Manufacturing concept, the key system structure, or each key technology were investigated, and, finally, 3) the Smart Manufacturing-related trends were identified and the future was predicted by conducting various analyses on the application areas and technology development levels that have been addressed in each article.

949 citations

Journal Article•DOI•
Blockchain Platform for Industrial Internet of Things

[...]

Arshdeep Bahga, Vijay K. Madisetti
10 Oct 2016-Journal of Software Engineering and Applications
TL;DR: With the use of Blockchain technology, the BPIIoT platform enables peers in a decentralized, trustless, peer-to-peer network to interact with each other without the need for a trusted intermediary.
Abstract: Internet of Things (IoT) are being adopted for industrial and manufacturing applications such as manufacturing automation, remote machine diagnostics, prognostic health management of industrial machines and supply chain management Cloud-Based Manufacturing is a recent on-demand model of manufacturing that is leveraging IoT technologies While Cloud-Based Manufacturing enables on-demand access to manufacturing resources, a trusted intermediary is required for transactions between the users who wish to avail manufacturing services We present a decentralized, peer-to-peer platform called BPIIoT for Industrial Internet of Things based on the Block chain technology With the use of Blockchain technology, the BPIIoT platform enables peers in a decentralized, trustless, peer-to-peer network to interact with each other without the need for a trusted intermediary

559 citations

Cites background from "Cloud-based design and manufacturin..."

  • ...BPIIoT also enhances existing CBM platforms by: 1) enabling consumer-to-machine and machine-to-machine transactions without a trusted intermediary; 2) integrating legacy shop floor equipment into the cloud environment; 3) automating machine maintenance and diagnostics tasks; 4) providing a distributed, secure and shared ledger of all transactions, assets and inventory records....

    [...]

  • ...A recent on-demand model of manufacturing that is leveraging IoT technologies is called Cloud-Based Manufacturing (CBM) [2]....

    [...]

  • ...The BPIIoT platform will act as a key-enabler for cloud-based manufacturing, enhancing the functionality of existing CBM platforms, especially towards integrating legacy shop floor equipment into the cloud environment....

    [...]

  • ...CBM enables ubiquitous, convenient, on-demand network access to a shared pool of configurable manufacturing resources that can be rapidly provisioned and released with minimal How to cite this paper: Bahga, A. and Madisetti, V.K. (2016) Blockchain Platform for Industrial Internet of Things....

    [...]

  • ...Figure 3 shows how BPIIoT platform can enhance the functionality of Cloud-based Manufacturing (CBM) platforms, by providing a decentralized, trustless, peer-to-peer network for manufacturing applications....

    [...]

Journal Article•DOI•
Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives

[...]

Pai Zheng1, Honghui wang1, Zhiqian Sang1, Runyang Zhong1, Yongkui Liu1, Chao Liu1, Khamdi Mubarok1, Shiqiang Yu1, Xun Xu1 •
University of Auckland1
16 Mar 2018-Frontiers in Mechanical Engineering
TL;DR: This study examines smart manufacturing systems for Industry 4.0 using a conceptual framework and demonstrative scenarios that pertain to smart design, smart machining, smart control, smart monitoring, and smart scheduling.
Abstract: 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.

558 citations

Journal Article•DOI•
Towards the online computer-aided design of catalytic pockets

[...]

Laura Falivene1, Zhen Cao1, Andrea Petta2, Luigi Serra2, Albert Poater1, Romina Oliva, Vittorio Scarano2, Luigi Cavallo1 •
King Abdullah University of Science and Technology1, University of Salerno2
02 Sep 2019-Nature Chemistry
TL;DR: Topographic steric maps are introduced that provide a three-dimensional image of the catalytic pocket—the region of the catalyst where the substrate binds and reacts—enabling it to be visualized and also reshaped by changing various parameters.
Abstract: The engineering of catalysts with desirable properties can be accelerated by computer-aided design. To achieve this aim, features of molecular catalysts can be condensed into numerical descriptors that can then be used to correlate reactivity and structure. Based on such descriptors, we have introduced topographic steric maps that provide a three-dimensional image of the catalytic pocket-the region of the catalyst where the substrate binds and reacts-enabling it to be visualized and also reshaped by changing various parameters. These topographic steric maps, especially when used in conjunction with density functional theory calculations, enable catalyst structural modifications to be explored quickly, making the online design of new catalysts accessible to the wide chemical community. In this Perspective, we discuss the application of topographic steric maps either to rationalize the behaviour of known catalysts-from synthetic molecular species to metalloenzymes-or to design improved catalysts.

548 citations

References
More filters
Journal Article•DOI•
MapReduce: simplified data processing on large clusters

[...]

Jeffrey Dean1, Sanjay Ghemawat1•
Google1
06 Dec 2004
TL;DR: This paper presents the implementation of MapReduce, a programming model and an associated implementation for processing and generating large data sets that runs on a large cluster of commodity machines and is highly scalable.
Abstract: MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. Many real world tasks are expressible in this model, as shown in the paper. Programs written in this functional style are automatically parallelized and executed on a large cluster of commodity machines. The run-time system takes care of the details of partitioning the input data, scheduling the program's execution across a set of machines, handling machine failures, and managing the required inter-machine communication. This allows programmers without any experience with parallel and distributed systems to easily utilize the resources of a large distributed system. Our implementation of MapReduce runs on a large cluster of commodity machines and is highly scalable: a typical MapReduce computation processes many terabytes of data on thousands of machines. Programmers find the system easy to use: hundreds of MapReduce programs have been implemented and upwards of one thousand MapReduce jobs are executed on Google's clusters every day.

20,309 citations

Journal Article•DOI•
MapReduce: simplified data processing on large clusters

[...]

Jeffrey Dean1, Sanjay Ghemawat1•
Google1
01 Jan 2008-Communications of The ACM
TL;DR: This presentation explains how the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks.
Abstract: MapReduce is a programming model and an associated implementation for processing and generating large datasets that is amenable to a broad variety of real-world tasks. Users specify the computation in terms of a map and a reduce function, and the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks. Programmers find the system easy to use: more than ten thousand distinct MapReduce programs have been implemented internally at Google over the past four years, and an average of one hundred thousand MapReduce jobs are executed on Google's clusters every day, processing a total of more than twenty petabytes of data per day.

17,663 citations

Book•
Product Design and Development

[...]

Karl T. Ulrich, Steven D. Eppinger
01 Jan 1995
TL;DR: This paper presents a meta-modelling framework for modeling uncertainty in the value of money and the net present value technique, and some examples show how this framework can be applied to product development economics.
Abstract: Chapter 1 Introduction Chapter 2 Development Processes and Organizations Chapter 3 Product Planning Chapter 4 Identifying Customer Needs Chapter 5 Product Specifications Appendix Concept -Scoring Matrix Example Chapter 6 Concept Generation Chapter 7 Concept Selection Appendix A Concept-Screening Matrix Example Appendix B Concept-Scoring Matrix Example Chapter 8 Concept Testing Appendix Estimating Market Sizes Chapter 9 Product Architecture Chapter 10 Industrial Design Chapter 11 Design for Manufacturing Appendix A Material Costs Appendix B Component Manufacturing Costs Appendix C Assembly Costs Appendix D Cost Structures Chapter 12 Prototyping Chapter 13 Robust Design Appendix Orthogonal Arrays Chapter 14 Patents and Intellectual Property Appendix A Trademarks Appendix B Advice to Individual Inventors Chapter 15 Product Development Economics Appendix A Time Value of Money and the Net Present Value Technique Appendix B Modeling Uncertain Cash Flow Using Net Present Value Analysis Chapter 16 Managing Projects Appendix Design Structure Matrix Example

6,707 citations

"Cloud-based design and manufacturin..." refers background in this paper

  • ...Time Configuration Characteristics 1960s Centralized Standalone system; Operate on large and expensive computers; Generate 2D drawings with a light pen on a CRT monitor; 1970s Centralized Standalone system; Operate on affordable personal desktop computers; Perform 3D solid modeling; 1980s Distributed Thin server + strong client; 8 Heavy-weighted client mechanism; Hard to be implemented on the Internet; 1990s Distributed Strong server + thin client; Light-weighted client mechanism; Adopt the application service provider (ASP) model Easy to be implemented on the Internet; Beyond 2010s Distributed Cloud computing-based; Virtualization; Multi-tenancy; Social media; Ubiquitous access; Software-as-a-Service; Pay-per-use;...

    [...]

  • ...Time Systems Configuration Characteristics 1900s Assembly line Centralized Reduced labor costs; Increased production rate; 1960s Toyota production systems Centralized Reduced waste of over production; Reduced waiting time; Reduced defective products; Continuous improvement; 1980s Flexible manufacturing systems Centralized Reduced inventories; Improved productivity; Increased system reliability; Increased variety of parts; Improved machine utilization; Improved response to engineering changes; 1990s Reconfigurable manufacturing systems Centralized Increased responsiveness to market changes; Reduced time required for product changeover; Reduced lead time for launching new manufacturing systems; Rapid integration of new technology; 9 2000s Web-based and agent-based manufacturing systems Distributed Improved information sharing; Improved resource reuse; Improved computational performance; Remote monitoring and control; Beyond 2010s Cloud-based manufacturing systems Distributed Rapid capacity scalability; Reduced time-to-market; Reduced costs; Ubiquitous computing environment; Pooled manufacturing resources; Improved information sharing; Improved resource reuse; Improved machine utilization; For example, Henry Ford created the first assembly line, in which interchangeable parts can be added to a product in a sequential manner to produce finished products more efficiently and cost-effectively....

    [...]

  • ...Time Systems Configuration Characteristics 1900s Assembly line Centralized Reduced labor costs; Increased production rate; 1960s Toyota production systems Centralized Reduced waste of over production; Reduced waiting time; Reduced defective products; Continuous improvement; 1980s Flexible…...

    [...]

Book•
Big data: The next frontier for innovation, competition, and productivity

[...]

James Manyika
13 May 2011
TL;DR: The amount of data in the authors' world has been exploding, and analyzing large data sets will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus, according to research by MGI and McKinsey.
Abstract: The amount of data in our world has been exploding, and analyzing large data sets—so-called big data— will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus, according to research by MGI and McKinsey's Business Technology Office. Leaders in every sector will have to grapple with the implications of big data, not just a few data-oriented managers. The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things will fuel exponential growth in data for the foreseeable future.

4,700 citations

"Cloud-based design and manufacturin..." refers background in this paper

  • ...economic variables and determine what key measurable manufacturing parameters most influence customer satisfaction [68]....

    [...]

Proceedings Article•DOI•
Cloud Computing and Grid Computing 360-Degree Compared

[...]

Ian Foster1, Yong Zhao2, Ioan Raicu1, Shiyong Lu3•
University of Chicago1, Microsoft2, Wayne State University3
01 Nov 2008
TL;DR: In this article, the authors compare and contrast cloud computing with grid computing from various angles and give insights into the essential characteristics of both the two technologies, and compare the advantages of grid computing and cloud computing.
Abstract: Cloud computing has become another buzzword after Web 2.0. However, there are dozens of different definitions for cloud computing and there seems to be no consensus on what a cloud is. On the other hand, cloud computing is not a completely new concept; it has intricate connection to the relatively new but thirteen-year established grid computing paradigm, and other relevant technologies such as utility computing, cluster computing, and distributed systems in general. This paper strives to compare and contrast cloud computing with grid computing from various angles and give insights into the essential characteristics of both.

3,132 citations

Related Papers (5)
From cloud computing to cloud manufacturing

[...]

01 Feb 2012-Robotics and Computer-integrated Manufacturing
Xun Xu
Cloud manufacturing: Strategic vision and state-of-the-art☆

[...]

01 Oct 2013-Journal of Manufacturing Systems
Dazhong Wu, Matthew J. Greer, David W. Rosen, Dirk Schaefer 
Cloud manufacturing: a computing and service-oriented manufacturing model:

[...]

22 Aug 2011
Fei Tao, Lin Zhang, V C Venkatesh, Y Luo, Ying Cheng 
Cloud manufacturing: a new manufacturing paradigm

[...]

01 Mar 2014-Enterprise Information Systems
Lin Zhang, Yongliang Luo, Fei Tao, Bo Hu Li, Lei Ren, Xuesong Zhang, Hua Guo, Ying Cheng, Anrui Hu, Yongkui Liu 
Cloud manufacturing:a new service-oriented networked manufacturing model

[...]

01 Jan 2010-Computer Integrated Manufacturing Systems
Chai Xu-dong
Frequently Asked Questions (19)
Q1. What are the contributions mentioned in the paper "Cloud-based design and manufacturing: a new paradigm in digital manufacturing and design innovation" ?

To answer this question, the authors discuss and compare the existing definitions * Manuscript_Revised_Unmarked Click here to view linked References 

In addition, IoT is another key enabling technology to improve manufacturing automation, supply chain management, remote maintenance and diagnostics in the future development and implementation of CBDM. In addition, future advances in wireless sensor networks, pervasive remote tracking/monitoring, and standardization of communications protocols will allow for effective and efficient machine to machine, machine to infrastructure, machine to environment, human to human, and human to machine communications from anywhere at any time. Specifically, because IoT is characterized by ubiquitous computing ( e. g., embedded smart sensors and actuators ) and pervasive sensing technologies ( e. g., RadioFrequency Identification tags ), it has the potential to automate manufacturing processes by 33 connecting humans, machines, manufacturing processes, and design- and manufacturing-related massive data sets. For example, realtime tracking/monitoring data will enable CBDM systems to track and trace specific objects, to monitor and synchronize material flow in manufacturing, and eventually increase the productivity and efficiency of manufacturing supply chain. 

To provide an interface such as social media and crowdsourcing platforms between service providers and consumers, the web portal of CBDM systems is developed using Web 2.0 technology and associated application software. 

the cloud-based supply chain management module allows for manufacturing capacity scalability planning and control by simulating the material flow in the CBDM process and optimizing supplier selection. 

TPSs are characterized by a number of principles that assist in eliminating waste by reducing waiting time, inventory, and the number of defective products. 

The graph theory and data mining tools in SNA allow for visualizing information flow in the drone design network, detecting groups of design engineers with common design interests and activities while design activities are being conducted. 

they can use business-targeted market research platforms such as HootSuite [47], Epinions [48], and Salesforce.com [49] to collect customer feedback and responses on existing and new features of drones. 

the major advantage of an FMS is that it allows for variation in both parts and assemblies; however, its implementation is usually costly. 

From a design communication perspective, cloud-based information management tools allow for enhanced information flow management that can significantly improve design productivity. 

In five years or so, the FAA will address current and future policies, regulations, technologies, and procedures related to the commercial use of drones in the United States. 

Quickparts enables design engineers to upload their CAD files of the drone design created by CATIA and SolidWorks, to perform geometric and printability analysis, and finally to receive a list of qualified service providers instantly. 

The advantages of cloud-based data storage are: (1) cloud-based data storage provides users with ubiquitous access to a broad range of data stored in the networked servers via a web service19interface; (2) data storage can easily scale up and down as needed on a self-service basis; (3) users are only charged for the storage they actually use in the cloud. 

the machine-readable knowledge representation scheme, called web service description language (WSDL), and universal description discovery and integration (UDDI) allow manufacturing service providers to publish their manufacturing services in a machine-readable language. 

From a rapid prototyping perspective, CBM allows the design team to build the prototype more efficiently and cost effectively without large upfront investment in manufacturing equipment. 

Should provide cloud-based distributed file systems that allow users to have ubiquitousaccess to design- and manufacturing-related dataR3. 

Adapted from the original cloud computing paradigm and introduced into the realm of computer-aided product development, cloud-based design and manufacturing (CBDM) is gaining significant momentum and attention from both academia and industry. 

To bridge the gap between currently existing technologies, services, infrastructures and their vision of CBDM, it is worthwhile to discuss how future and emerging technologies such as cyber-physical systems (CPS), the internet of things (IoT), and big data can help achieve and improve CBDM: CPS is expected to play a major role in the design and development of future CBDM systems. 

In addition, as shown in the virtual and physical layers in Fig. 1, virtualization can improve the efficiency and availability of computing and IT resources by re-allocating hardware dynamically to applications based on their need. 

manufacturing capacity can be rapidly scaled up when needed, because the team can almost always find a list of qualified service providers whose manufacturing capacity is not fully utilized using the aforementioned cloud-based global sourcing platforms.