The smart manufacturing and Industry 4.0 paradigms are transforming factories into highly complex IT systems, generating massive amounts of data. The modeling and optimization of smart industrial processes to support decision-making has consequently become a new domain of application, with its own unique and peculiar issues, for some of the most prominent emerging technologies across the entire computing stack, from hardware and systems design to application-level software.
At the lowest hierarchical level, the massive data collection that forms the foundation of digital industries is enabled by increasingly complex and heterogeneous cyber-physical systems (CPSs). Owing to that, the design of software for CPSs is receiving renewed interest from research and industry in relation to challenges such as designing efficient yet robust virtual platform environments for heterogeneous computing systems, and managing variability and dependencies handling with an unprecedented degree of customization.
At a higher level, digital twins (DTs) of industrial processes and products, fed with the data collected by CPSs, are widely considered fundamental tools for smart manufacturing. Nonetheless, many facets of advanced DTs design in this field are still unexplored, as well as many of their potential applications. The sophistication of DTs with new features, such as the modeling of non-functional aspects (e.g., energy consumption, communication, safety, and fault tolerance) require refining or rethinking the underlying computing models. More broadly, fundamental challenges are also brought upon computation models and platforms by the realization of cognitive DTs, possessing properties such as attention, perception, and memory, and foreseen as key enablers for realizing the Industry 4.0 vision.
Machine-learning (ML) algorithms constitute the core components of cognitive digital twins. Moreover, they are at the basis of many other key applications found in smart factories, including in situ quality inspection, product binning, predictive and prescriptive maintenance, and logistics. In recent years, this has spurred a lot of research interest on computational and algorithmic aspects of AI and ML, aimed at eliminating the shortcomings of these algorithms in industrial scenarios, e.g., making them effective despite scarce and imbalanced distributions of labeled data and robust enough to handle complex and variegated inputs, for instance through hybrid conventional/ML solutions.
This invited thematic section on “Applications of Emerging Computing Technologies in Smart Manufacturing and Industry 4.0” covers all the aforementioned aspects, from CPS designs to novel AI pipelines. This thematic section only accepts submissions upon invitation, and aims at gathering papers that extend, improve, and enrich the contributions presented at the recent 2021 Design, Automation, and Test in Europe (DATE) Conference, in the sessions of the Special Day on “Cyber-Physical Systems for I4.0 and Smart Industrial Processes.” Topics include, but are not limited to:
- CPSs to enable the Industry 4.0 paradigm,
- Computing models and architectures for the implementation of DTs of industrial plants and manufacturing systems,
- Safety-critical computing systems,
- Architectures for cognitive computing and applications to intelligent production systems, and
- Custom computing architectures for AI and ML used in smart manufacturing systems and their simulators.
Schedule
- Submission deadline: April 30, 2021
- First decision (accept/reject/revise, tentative): June 15, 2021
- Submission of revised papers: June 30, 2021
- Notification of final decision (tentative): July 31, 2021
- Journal publication (tentative): December 2021
Submission Guidelines
This thematic section only accepts submissions upon invitation. To submit to this thematic section, author(s) should have received a prior written invitation by the guest editors, otherwise the non-invited manuscripts will be withdrawn/unsubmitted.
Submitted papers must include new significant research-based technical contributions in the scope of the journal. Purely theoretical, technological or lacking methodological-and-generality papers are not suitable to this thematic section. The submissions must include clear evaluations of the proposed solutions (based on simulation and/or implementations results) and comparisons to state-of-the-art solutions. For additional information, please contact the guest editors by sending an email to tetc.smart.manufacturing@gmail.com.
Papers under review elsewhere are not acceptable for submission. Extended versions of published conference papers (to be included as part of the submission together with a summary of differences) are welcome but there must have at least 40% of new impacting technical or scientific material in the submitted journal version, and there should be less than 50% verbatim similarity level as reported by a tool (such as CrossRef).
Guidelines concerning the submission process and LaTeX and Word templates can be found here. As per TETC policies, only full-length papers (10-16 pages with technical material, double column – papers beyond 12 pages will be subject to MOPC, as per CS policies) can be submitted to special/thematic sections. References should not exceed 45 items and each author’s bio should not exceed 150 words.
While submitting through ScholarOne, please select the option “Thematic section, by invitation only, on Applications of Emerging Computing Technologies in Smart Manufacturing and Industry 4.0.”
Guest Editors
- Daniele Jahier Pagliari, Politecnico di Torino, Italy (IEEE Member)
- Enrico Macii, Politecnico di Torino, Italy (IEEE Fellow)
- Frank Schirrmeister, Cadence Design Systems Inc., USA (IEEE Member)
Corresponding TETC editor: Nader Bagherzadeh (IEEE Fellow)
