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Machines
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Selected Papers from 31st Flexible Automation and Intelligent Manufacturing International...
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Selected Papers from 31st Flexible Automation and Intelligent Manufacturing International Conference (FAIM2022)

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Automation and Control Systems".

Deadline for manuscript submissions: closed (15 February 2023) | Viewed by 12093

Special Issue Editors

Prof. Dr. Kyoung-Yun Kim

E-Mail Website
Guest Editor
Department of Industrial and Systems Engineering, Wayne State University, Detroit, MI 48202, USA
Interests: design science; design informatics; semantic assembly design; welding and joining; smart manufacturing
Dr. Yanchao Liu

E-Mail Website
Guest Editor
Department of Industrial and Systems Engineering, Wayne State University, Detroit, MI 48202, USA
Interests: optimization; power systems; transportation
Dr. Murat Yildirim

E-Mail Website
Guest Editor
Director, Cyber-Physical Systems Laboratory, Industrial & Systems Engineering Department, College of Engineering, Wayne State University, 4815 Fourth Street, Detroit, MI 48201, USA
Interests: monitoring; diagnostics; cyberphysical systems; federated learning; operations research
Special Issues, Collections and Topics in MDPI journals
Dr. Jeremy Rickli

E-Mail Website
Guest Editor
Department of Industrial and Systems Engineering, Wayne State Universtiy, 4815 Fourth St., Detroit, MI 48201,USA
Interests: sustainable manufacturing; digital manufacturing enterprise; collaborative robotic automation

Special Issue Information

Dear Colleagues,

FAIM (Flexible Automation and Intelligent Manufacturing, https://www.faimconference.org/) is a renowned international forum for academia and industry to disseminate novel research, theories, and practices relevant to automation and manufacturing. The conference will be held on 19–23 June 2022 at the Wayne State University Campus in Midtown Detroit. The program will facilitate high-quality paper presentations, high-caliber keynote speeches, industrial visits, with various social programs focusing on the local cultures in the Detroit area.

The theme of FAIM 2022 is “Human–Data–Technology Nexus in Intelligent Manufacturing and Next-Generation Automation”. Four thematic pillars in automation and intelligence streams underpin this theme:

Manufacturing processes

  • New and innovative processes
  • New and innovative processed materials
  • Precision engineering and manufacturing
  • Processing at the micro and nano scales

Machine tools

  • Design and dynamics
  • Numerical control and mechatronics
  • Accuracy and metrology
  • Intelligent machine tools
  • Process and condition monitoring

Manufacturing systems

  • New manufacturing paradigms
  • Flexible/intelligent automation including collaborative robots (RRC, HRC, etc.)
  • System lifecycle engineering
  • Process planning, production planning/scheduling/control
  • Quality control and inspection, TQM
  • Logistics and supply chain engineering
  • Ergonomics, health, and safety
  • Education and training

Enabling technologies

  • Applied artificial intelligence
  • Machine learning
  • Virtual/augmented reality and digital twins
  • Computational geometry
  • CAD / CAM
  • Manufacturing networks and security
  • Ontologies and information modeling

Prof. Dr. Kyoung-Yun Kim
Dr. Yanchao Liu
Dr. Murat Yildirim
Dr. Jeremy Rickli
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. Machines is an international peer-reviewed open access monthly 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 2400 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 (6 papers)

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Research

25 pages, 5556 KiB  
Article
An Investigation of Factors Influencing Tool Life in the Metal Cutting Turning Process by Dimensional Analysis
by Sara M. Bazaz, Juho Ratava, Mika Lohtander and Juha Varis
Machines 2023, 11(3), 393; https://doi.org/10.3390/machines11030393 - 17 Mar 2023
Cited by 1 | Viewed by 2873
Abstract
This article uses dimensional analysis to formulate the tool life in the turning process of metal cutting for small-lot production by considering the impacts of the most important parameters. The estimation of tool life specifies process efficiency, machining productivity, resource consumption, machining time, [...] Read more.
This article uses dimensional analysis to formulate the tool life in the turning process of metal cutting for small-lot production by considering the impacts of the most important parameters. The estimation of tool life specifies process efficiency, machining productivity, resource consumption, machining time, and cost. Many parameters influence tool life on the real shop floor in small-lot production. This literature review studies 29 parameters affecting tool life directly or indirectly. The results of this research are represented as a graph-based analysis in the form of a web of interdependencies and a relationship matrix. The relationship matrix illustrates the direct and indirect interdependencies of the parameters which influence tool life in the turning process. The graph visualizes the weight of the parameters for the estimation of tool life in small-lot production. A cause-and-effect diagram is extracted from the relationship matrix to study the parameters affecting tool life in small-lot production. A dimensional analysis is executed based on the cause-and-effect diagram in order to calculate the tool life. The functions of tool life involve the cutting conditions, tool and workpiece hardness, cutting force, and cutting temperature. The dimensional analysis shows that the cutting speed, feed rate, and workpiece hardness are the most effective factors impacting tool life in the turning process. Full article
(This article belongs to the Special Issue Selected Papers from 31st Flexible Automation and Intelligent Manufacturing International Conference (FAIM2022))
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21 pages, 9470 KiB  
Article
Processing Strategies for Dieless Forming of Fiber-Reinforced Plastic Composites
by Jan-Erik Rath, Robert Graupner and Thorsten Schüppstuhl
Machines 2023, 11(3), 365; https://doi.org/10.3390/machines11030365 - 08 Mar 2023
Cited by 1 | Viewed by 1317
Abstract
The demand for lightweight materials, such as fiber-reinforced plastics (FRP), is constantly growing. However, current FRP production mostly relies on expensive molds representing the final part geometry, which is not economical for prototyping or highly individualized products, such as in the medical or [...] Read more.
The demand for lightweight materials, such as fiber-reinforced plastics (FRP), is constantly growing. However, current FRP production mostly relies on expensive molds representing the final part geometry, which is not economical for prototyping or highly individualized products, such as in the medical or sporting goods sector. Therefore, inspired by incremental sheet metal forming, we conduct a systematic functional analysis on new processing methods for shaping woven FRP without the use of molds. Considering different material combinations, such as dry fabric with thermoset resin, thermoset prepreg, thermoplastic commingled yarn weave and organo sheets, we propose potential technical implementations of novel dieless forming techniques, making use of simple robot-guided standard tools, such as hemispherical tool tips or rollers. Feasibility of selected approaches is investigated in basic practical experiments with handheld tools. Results show that the main challenge of dieless local forming, the conservation of already formed shapes while allowing drapability of remaining areas, is best fulfilled by local impregnation, consolidation and solidification of commingled yarn fabric, as well as concurrent forming of prepreg and metal wire mesh support material. Further research is proposed to improve part quality. Full article
(This article belongs to the Special Issue Selected Papers from 31st Flexible Automation and Intelligent Manufacturing International Conference (FAIM2022))
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20 pages, 8665 KiB  
Article
Robust and Secure Quality Monitoring for Welding through Platform-as-a-Service: A Resistance and Submerged Arc Welding Study
by Panagiotis Stavropoulos, Alexios Papacharalampopoulos and Kyriakos Sabatakakis
Machines 2023, 11(2), 298; https://doi.org/10.3390/machines11020298 - 17 Feb 2023
Cited by 6 | Viewed by 1233
Abstract
For smart manufacturing systems, quality monitoring of welding has already started to shift from empirical modeling to knowledge integration directly from the captured data by utilizing one of the most promising Industry 4.0’s key enabling technologies, artificial intelligence (AI)/machine learning (ML). However, beyond [...] Read more.
For smart manufacturing systems, quality monitoring of welding has already started to shift from empirical modeling to knowledge integration directly from the captured data by utilizing one of the most promising Industry 4.0’s key enabling technologies, artificial intelligence (AI)/machine learning (ML). However, beyond the advantages that they bring, AI/ML introduces new types of security threats, which are related to their very nature and eventually, they will pose real threats to the production cost and quality of products. These types of security threats, such as adversarial attacks, are causing the targeted AI system to produce incorrect or malicious outputs. This may undermine the performance (and the efficiency) of the quality monitoring systems. Herein, a software platform servicing quality monitoring for welding is presented in the context of resistance and submerged arc welding. The hosted ML classification models that are trained to perform quality monitoring are subjected to two different types of untargeted, black-box, adversarial attacks. The first one is based on a purely statistical approach and the second one is based on process knowledge for crafting these adversarial inputs that can compromise the models’ accuracy. Finally, the mechanisms upon which these adversarial attacks are inflicting damage are discussed to identify which process features or defects are replicated. This way, a roadmap is sketched toward testing the vulnerability and robustness of an AI-based quality monitoring system. Full article
(This article belongs to the Special Issue Selected Papers from 31st Flexible Automation and Intelligent Manufacturing International Conference (FAIM2022))
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18 pages, 8562 KiB  
Article
Optimization of Milling Processes: Chatter Detection via a Sensor-Integrated Vice
by Panagiotis Stavropoulos, Thanassis Souflas, Dimitris Manitaras, Christos Papaioannou and Harry Bikas
Machines 2023, 11(1), 52; https://doi.org/10.3390/machines11010052 - 02 Jan 2023
Cited by 2 | Viewed by 2016
Abstract
The future of the milling process is the fully autonomous operation of the machine tools. Developments in terms of automation and machine tool design are now enabling fully autonomous operation. However, the optimization and stability of the process itself still remains a challenge. [...] Read more.
The future of the milling process is the fully autonomous operation of the machine tools. Developments in terms of automation and machine tool design are now enabling fully autonomous operation. However, the optimization and stability of the process itself still remains a challenge. Chatter is the most significant bottleneck, and as such, it should be constantly monitored to ensure a stable process. This work proposes a sensor-integrated milling vice using an MEMS accelerometer as a non-invasive monitoring solution for chatter detection. The system is comprised by low-cost, industrial-grade components suitable for implementation in real production scenarios. The dynamic analysis of the sensor-integrated vice enables the definition of the sensor-integration point to ensure measurement quality. The use of advanced signal process algorithms for the demodulation of the vibration signal, along with the use of artificial intelligence for chatter detection, led to a high-performance system at a low cost. A wide set of milling experiments that has been conducted showcased that the proposed solution enables continuous, real-time process optimization in milling through in-process chatter detection. Full article
(This article belongs to the Special Issue Selected Papers from 31st Flexible Automation and Intelligent Manufacturing International Conference (FAIM2022))
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13 pages, 1183 KiB  
Article
Intelligent Insights for Manufacturing Inspections from Efficient Image Recognition
by Douglas Eddy, Michael White and Damon Blanchette
Machines 2023, 11(1), 45; https://doi.org/10.3390/machines11010045 - 01 Jan 2023
Viewed by 1888
Abstract
Many complex electromechanical assemblies that are essential to the vital function of certain products can be time-consuming to inspect to a sufficient level of certainty. Examples include subsystems of machine tools, robots, aircraft, and automobiles. Out-of-tolerance conditions can occur due to either random [...] Read more.
Many complex electromechanical assemblies that are essential to the vital function of certain products can be time-consuming to inspect to a sufficient level of certainty. Examples include subsystems of machine tools, robots, aircraft, and automobiles. Out-of-tolerance conditions can occur due to either random common-cause variability or undetected nonstandard deviations, such as those posed by debris from foreign objects. New methods need to be implemented to enable the utilization of detection technologies in ways that can significantly reduce inspection efforts. Some of the most informative three-dimensional image recognition methods may not be sufficiently reliable or versatile enough for a wide diversity of assemblies. It can also be an extensive process to train the recognition on all possible anomalies comprehensively enough for inspection certainty. This paper introduces a methodical technique to implement a semiautonomous inspection system and its algorithm, introduced in a prior publication, that can learn manufacturing inspection inference from image recognition capabilities. This fundamental capability accepts data inputs that can be obtained during the image recognition training process followed by machine learning of the likely results. The resulting intelligent insights can inform an inspector of the likelihood that an assembly scanned by image recognition technology will meet the manufacturing specifications. An experimental design is introduced to generate data that can train and test models with a realistic representation of manufacturing cases. A benchmark case study example is presented to enable comparison to models from manufacturing cases. The fundamental method is demonstrated using a realistic assembly manufacturing example. Recommendations are given to guide efforts to deploy this entire methodical technique comprehensively. Full article
(This article belongs to the Special Issue Selected Papers from 31st Flexible Automation and Intelligent Manufacturing International Conference (FAIM2022))
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17 pages, 1936 KiB  
Article
Reducing the Capacity Loss of Lithium-Ion Batteries with Machine Learning in Real-Time—A Study Case
by Joelton Deonei Gotz, José Rodolfo Galvão, Samuel Henrique Werlich, Alexandre Moura da Silveira, Fernanda Cristina Corrêa and Milton Borsato
Machines 2022, 10(12), 1114; https://doi.org/10.3390/machines10121114 - 24 Nov 2022
Cited by 1 | Viewed by 1373
Abstract
Lithium-ion batteries (LIBs) are the state-of-the-art technology for energy storage systems. LIBs can store energy for longer, with higher density and power capacity than other technologies. Despite that, they are sensitive to abuses and failures. If the battery management system (BMS) operates incorrectly [...] Read more.
Lithium-ion batteries (LIBs) are the state-of-the-art technology for energy storage systems. LIBs can store energy for longer, with higher density and power capacity than other technologies. Despite that, they are sensitive to abuses and failures. If the battery management system (BMS) operates incorrectly or some anomalies appear, performance and security issues can be observed in LIBs. BMSs are also hard-programmed, have complex circuits, and have low computational resources, which limit the use of prognoses and diagnoses systems operating in real-time and embedded in the vehicle. Therefore, some technologies, such as edge and cloud computing, data-driven approaches, and machine learning (ML) models, can be applied to help the BMS manage the LIBs. Therefore, this work presents an edge–cloud computing system composed of two ML approaches (anomaly detection and failure classification) to identify the abuses in the LIBs in real-time. To validate the work, 36 NMC cells with a nominal capacity of 2200 mAh and voltage of 3.7 V were used to build the experiments segmented into three steps. Firstly, 12 experiments under failures were realized, which resulted in a high capacity loss. Then, the data were used to build both ML models. In the second step, the anomaly approach was applied to 12 cells observing the cells’ temperature anomalies. Then, the combination of IF and RF was applied to another 12 cells. The IF could reduce the capacity loss by about 45% when multiple abuses were applied to the cells. Despite that, this approach could not avoid some failures, such as overdischarging. Conversely, combining IF and RF could significantly reduce the capacity loss by 91% for the multiple abuses. The results concluded that ML could help the BMS identify failures in the first stage and reduce the capacity loss in LIBs. Full article
(This article belongs to the Special Issue Selected Papers from 31st Flexible Automation and Intelligent Manufacturing International Conference (FAIM2022))
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