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JMMP
Special Issues
AI Applications in Smart and Advanced Manufacturing
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AI Applications in Smart and Advanced Manufacturing

A special issue of Journal of Manufacturing and Materials Processing (ISSN 2504-4494).

Deadline for manuscript submissions: closed (31 July 2020) | Viewed by 21635

Special Issue Editors

Prof. Dr. Thorsten Wuest

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Guest Editor
Department of Industrial and Management Systems Engineering, West Virginia University, Morgantown, WV, USA
Interests: smart manufacturing; Industry 4.0; Artificial Intelligence; machine learning; hybrid analytics; closed-loop product lifecycle management; digital supply networks
Prof. Dr. Gisela Lanza

E-Mail Website
Guest Editor
Institute of Production Science (wbk), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Interests: management of global production networks; quality management and control; smart manufacturing systems; additive manufacturing; data analytics; robust and reactive production control systems
Prof. Dr. Fei Tao

E-Mail Website1 Website2
Guest Editor
School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
Interests: service-oriented smart manufacturing; manufacturing service management; sustainable manufacturing; digital twin (DT)-driven product design/manufacturing/service
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ang Liu

E-Mail Website
Guest Editor
School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, Australia
Interests: mechanical engineering; design innovation; innovation and technology management; design history and theory; manufacturing engineering
Prof. Dr. Alejandro G. Frank

E-Mail Website
Guest Editor
Department of Industrial Engineering, Federal University of Rio Grande do Sul, 90035190 Porto Alegre, Brazil
Interests: technology and operations management with focus on servitization; Industry 4.0 and industrial performance in manufacturing companies

Special Issue Information

Dear Colleagues,

Smart and advanced manufacturing are transforming the global manufacturing industry. A myriad of new digital technologies are paving the way for the fourth industrial revolution that promises to make manufacturing processes more efficient, sustainable, and profitable. Products and processes become smarter and connected, providing large quantities of data. To create value from this new wealth of manufacturing and usage data, we need new, innovative tools, algorithms, and applications to process, manage, and analyze them in order to create valuable insights for manufacturers. Artificial Intelligence (AI) provides such capabilities and means to help manufacturers to gain insights to improve design, manufacturing, and handling of processes and products.

In this Special Issue of JMMP, we aim to report on novel applications in the field of AI applications in smart and advanced manufacturing. We are looking for original contributions for both general AI and dedicated machine learning applications to improve products and processes throughout manufacturing systems and global digital supply networks (DSN). Both scientific contributions pushing the state of the art as well as industrial applications (following methodological rigor) are welcome.

Topics include but are not limited to:

  • AI applications in additive manufacturing (AM);
  • Time series applications in smart manufacturing systems (SMS);
  • AI agents in decentral production control systems;
  • AI applications in smart maintenance;
  • AI-based tool wear prediction;
  • AI in product lifecycle management (PLM);
  • AI-based optimization of resource efficiency;
  • AI-enhanced digital twins;
  • AI enabled adaptive control systems;
  • Computer vision applications in manufacturing;
  • AI-based operator assistance systems (Operator 4.0);
  • AI in digital supply networks (DSN);
  • Global issues of AI in manufacturing (policy, cybersecurity).

Prof. Dr. Thorsten Wuest
Prof. Dr. Gisela Lanza
Prof. Dr. Fei Tao
Prof. Dr. Ang Liu
Prof. Dr. Alejandro G. Frank
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Manufacturing and Materials Processing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (4 papers)

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Research

14 pages, 2538 KiB  
Article
Digital Twin Based Optimization of a Manufacturing Execution System to Handle High Degrees of Customer Specifications
by Andrea Barni, Dario Pietraroia, Simon Züst, Shaun West and Oliver Stoll
J. Manuf. Mater. Process. 2020, 4(4), 109; https://doi.org/10.3390/jmmp4040109 - 17 Nov 2020
Cited by 21 | Viewed by 6611
Abstract
Lean production principles have greatly contributed to the efficient and customer-oriented mass production of goods and services. A core element of lean production is the focus on cycle times and designing production controls and buffers around any bottlenecks in the system. Hence, a [...] Read more.
Lean production principles have greatly contributed to the efficient and customer-oriented mass production of goods and services. A core element of lean production is the focus on cycle times and designing production controls and buffers around any bottlenecks in the system. Hence, a production line organized by lean principles will operate in a static or at least quasi-static way. While the individualization of products is an interesting business approach, it can influence cycle times and in-time production. This work demonstrates how performance losses induced by highly variable cycle times can be recovered using a digital twin. The unit under analysis is an industrial joiner’s workshop. Due to the high variance in cycle time, the joinery fails its production target, even if all machines are below 80% usage. Using a discrete event simulation of the production line, different production strategies can be evaluated efficiently and systematically. It is successfully shown that the performance losses due to the highly variable cycle times can be compensated using a digital twin in combination with optimization strategies. This is achieved by operating the system in a non-static mode, exploiting the flexibilities within the systems. Full article
(This article belongs to the Special Issue AI Applications in Smart and Advanced Manufacturing)
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20 pages, 6900 KiB  
Article
Pattern Recognition in Multivariate Time Series: Towards an Automated Event Detection Method for Smart Manufacturing Systems
by Vadim Kapp, Marvin Carl May, Gisela Lanza and Thorsten Wuest
J. Manuf. Mater. Process. 2020, 4(3), 88; https://doi.org/10.3390/jmmp4030088 - 05 Sep 2020
Cited by 20 | Viewed by 7901
Abstract
This paper presents a framework to utilize multivariate time series data to automatically identify reoccurring events, e.g., resembling failure patterns in real-world manufacturing data by combining selected data mining techniques. The use case revolves around the auxiliary polymer manufacturing process of drying and [...] Read more.
This paper presents a framework to utilize multivariate time series data to automatically identify reoccurring events, e.g., resembling failure patterns in real-world manufacturing data by combining selected data mining techniques. The use case revolves around the auxiliary polymer manufacturing process of drying and feeding plastic granulate to extrusion or injection molding machines. The overall framework presented in this paper includes a comparison of two different approaches towards the identification of unique patterns in the real-world industrial data set. The first approach uses a subsequent heuristic segmentation and clustering approach, the second branch features a collaborative method with a built-in time dependency structure at its core (TICC). Both alternatives have been facilitated by a standard principle component analysis PCA (feature fusion) and a hyperparameter optimization (TPE) approach. The performance of the corresponding approaches was evaluated through established and commonly accepted metrics in the field of (unsupervised) machine learning. The results suggest the existence of several common failure sources (patterns) for the machine. Insights such as these automatically detected events can be harnessed to develop an advanced monitoring method to predict upcoming failures, ultimately reducing unplanned machine downtime in the future. Full article
(This article belongs to the Special Issue AI Applications in Smart and Advanced Manufacturing)
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15 pages, 3070 KiB  
Article
Machine Tool Component Health Identification with Unsupervised Learning
by Thomas Gittler, Stephan Scholze, Alisa Rupenyan and Konrad Wegener
J. Manuf. Mater. Process. 2020, 4(3), 86; https://doi.org/10.3390/jmmp4030086 - 02 Sep 2020
Cited by 10 | Viewed by 2765
Abstract
Unforeseen machine tool component failures cause considerable losses. This study presents a new approach to unsupervised machine component condition identification. It uses test cycle data of machine components in healthy and various faulty conditions for modelling. The novelty in the approach consists of [...] Read more.
Unforeseen machine tool component failures cause considerable losses. This study presents a new approach to unsupervised machine component condition identification. It uses test cycle data of machine components in healthy and various faulty conditions for modelling. The novelty in the approach consists of the time series representation as features, the filtering of the features for statistical significance, and the use of this feature representation to train a clustering model. The benefit in the proposed approach is its small engineering effort, the potential for automation, the small amount of data necessary for training and updating the model, and the potential to distinguish between multiple known and unknown conditions. Online measurements on machines in unknown conditions are performed to predict the component condition with the aid of the trained model. The approach was exemplarily tested and verified on different healthy and faulty states of a grinding machine axis. For the accurate classification of the component condition, different clustering algorithms were evaluated and compared. The proposed solution demonstrated encouraging results as it accurately classified the component condition. It requires little data, is straightforward to implement and update, and is able to precisely differentiate minor differences of faults in test cycle time series. Full article
(This article belongs to the Special Issue AI Applications in Smart and Advanced Manufacturing)
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21 pages, 1580 KiB  
Article
Predicting the Ultimate Tensile Strength of Friction Stir Welds Using Gaussian Process Regression
by Roman Hartl, Fabian Vieltorf, Maximilian Benker and Michael F. Zaeh
J. Manuf. Mater. Process. 2020, 4(3), 75; https://doi.org/10.3390/jmmp4030075 - 22 Jul 2020
Cited by 8 | Viewed by 3010
Abstract
In the work described here, Gaussian process regression was applied to predict the ultimate tensile strength of friction stir welds through data evaluation and to therefore avoid destructive testing. For data generation, a total of 54 welding experiments were conducted in the butt [...] Read more.
In the work described here, Gaussian process regression was applied to predict the ultimate tensile strength of friction stir welds through data evaluation and to therefore avoid destructive testing. For data generation, a total of 54 welding experiments were conducted in the butt joint configuration using the aluminum alloy EN AW-6082-T6. Four tensile samples were taken from each of the 54 experiments and the resulting ultimate tensile strength of the weld seam segment was modeled as a function of the weld’s surface topography. Further models were created for comparison, which received either the process variables or the process parameters to predict the ultimate tensile strength. It was shown that the ultimate tensile strength can be accurately predicted based on the weld’s surface topography. Especially for low welding speeds, the correlation coefficients between the true and the predicted ultimate tensile strength were high. However, overall, even higher correlation coefficients could be achieved when providing the process variables or the process parameters to the model. In conclusion, it was shown that the developed Gaussian process regression model is a powerful approach for replacing destructive testing and for predicting ultimate tensile strength based solely on data that can be collected non-destructively. Full article
(This article belongs to the Special Issue AI Applications in Smart and Advanced Manufacturing)
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