Data Mining and Machine Learning in Industrial World
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".
Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 4777
Special Issue Editors
Interests: identity management; IoT; WSN; mini-robotics; E-learning
Interests: mobile computing; pervasive systems; monitoring tools; context awareness
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Special Issue Information
Dear Colleagues,
A continuous increase in industrial applications and technologies has generated not only variety, but also a high degree of heterogeneity. Items with similar or even identical functionalities and limitations have completely different sizes or shapes, even for the same manufacturer. In the end, all these generate higher costs for the end user, and greater efforts to manage the portfolio for the manufacturer. Automotive industry, for example, was forced to find solutions to these problems by uniting several companies into a single, huge group. In this way, some form of standardization is possible: the same platforms and parts can be used in common by various brands from the group. However, this requires extra effort to find the best ways to find and pair sub-assemblies, and this is where data mining can be extremely useful.
Manufacturing lines, especially those with the ability to detect defects or wrong parts, can become “smart” using data mining and machine learning techniques. These can also be used for inspection lines. Either way, the results will be a higher level of safety, lower costs, faster, maybe real-time, identification, and removal of problems. This direction is particularly interesting to investigate in the case of older lines that can be modernized.
Industrial fixed robots (robotic arms) are not the only robots that can benefit from data mining and machine learning. Mobile robots are already in use in various warehouses to carry various packages or heavy parts from point A to point B using some form of rigid indoor navigation: color lines, QR codes, BT beacons. Prototypes of partially autonomous robots are tested for home delivery, even if we refer to terrestrial robots or drones. Data mining and machine learning can be of great use for finding optimal routes, detecting and avoiding obstacles or dangers, optimizing multiple deliveries and so on. By doing this, robots’ autonomy will increase and operational costs will be lower.
Regarding autonomy, autonomous driving may be one of the hottest topics of the moment. In fact, we could think of autonomous vehicles as autonomous robots dedicated to carrying people or goods, under severe constraints related to safety, comfort, range, etc. This is another area where data mining could be beneficial. The discussion can be extended to vehicular ad-hoc networks (VANETs), where finding patterns or clustering can help to avoid critical events.
This Issue aims to gather the latest approaches based on data mining in the context of the industrial world. Original research, practical results or emerging ideas are welcomed. The main topics include, but are not limited to:
- Data Mining in heavy industry.
- Data Mining in robotic applications.
- Improving defect detection using Data Mining.
- Improving autonomous robots’ orientation, service, and autonomy using Data Mining techniques.
- Data Mining in Autonomous Driving.
- Data Mining in VANETs.
Dr. Stefan Mocanu
Prof. Dr. Ciprian Dobre
Guest Editors
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Keywords
- data mining
- heavy industry
- manufacturing lines
- inspection lines
- robotic applications
- decision making
- autonomous robots
- autonomous driving
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