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Applied Sciences
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Innovative Approaches for Machining Technologies of Composite Materials
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Innovative Approaches for Machining Technologies of Composite Materials

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (13 March 2023) | Viewed by 3565

Special Issue Editors

Prof. Dr. Luigi Nele

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Guest Editor
Department of Chemical, Materials and Industrial Production Engineering, University of Naples “Federico II”, 80125 Naples, Italy
Interests: composite materials; welding; industry 4.0; automation
Dr. Alessandra Caggiano

E-Mail Website
Guest Editor
Manufacturing Technology and Systems, Department of Industrial Engineering, University of Naples Federico II, 80125 Napoli NA, Italy
Interests: digital factory technologies; intelligent sensor monitoring of manufacturing processes; 3D metrology and reverse engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, composite material technologies are playing an increasingly fundamental role in industries such as the automotive and aerospace sectors. Machining is one of the most important manufacturing technologies since the first industrial revolution and is largely applied to the manufacturing of composite material components. The latest technological developments, followed by the transition to Industry 4.0, have fostered innovative approaches based on advanced techniques such as artificial intelligence and robotic systems technologies, which can be fused together to achieve higher automation, quality and efficiency in the machining processes.

For this Special Issue, we are calling for papers that cover innovative topics related to the machining of composite materials such as:

  • Data-driven techniques or physics-based/mathematical modelling for the machining of composite materials;
  • AI solutions for defect detection (e.g., delamination onset, tool wear detection, Remaining Useful Life (RUL), etc.);
  • Process parameter optimisation techniques for the machining processes of innovative composite materials;
  • Robotic systems for composite materials machining (e.g., force/motion control, end-effector design, etc.);
  • Real-time process monitoring and closed-loop process control;
  • Advanced cutting tools (materials, geometry and coatings);
  • Machining of bio-composites;
  • Industry 4.0.

Prof. Dr. Luigi Nele
Dr. Alessandra Caggiano
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. Applied Sciences 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 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.

Keywords

  • composite materials
  • machining
  • artificial intelligence
  • robotics
  • quality inspection
  • process control
  • industry 4.0

Published Papers (2 papers)

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Research

16 pages, 3884 KiB  
Article
Smart Tool Wear Monitoring of CFRP/CFRP Stack Drilling Using Autoencoders and Memory-Based Neural Networks
by Alessandra Caggiano, Giulio Mattera and Luigi Nele
Appl. Sci. 2023, 13(5), 3307; https://doi.org/10.3390/app13053307 - 5 Mar 2023
Cited by 3 | Viewed by 1619
Abstract
The drilling of carbon fiber-reinforced plastic (CFRP) materials is a key process in the aerospace industry, where ensuring high product quality is a critical issue. Low-quality of final products may be caused by the occurrence of drilling-induced defects such as delamination, which can [...] Read more.
The drilling of carbon fiber-reinforced plastic (CFRP) materials is a key process in the aerospace industry, where ensuring high product quality is a critical issue. Low-quality of final products may be caused by the occurrence of drilling-induced defects such as delamination, which can be highly affected by the tool conditions. The abrasive carbon fibers generally produce very fast tool wear with negative effects on the hole quality. This suggests the need to develop a method able to accurately monitor the tool wear development during the drilling process in order to set up optimal tool management strategies. Nowadays, different types of sensors can be employed to acquire relevant signals associated with process variables which are useful to monitor tool wear during drilling. Moreover, the increasing computational capacity of modern computers allows the successful development of procedures based on Artificial Intelligence (AI) techniques for signal processing and decision making aimed at online tool condition monitoring. In this work, an advanced tool condition monitoring method based on the employment of autoencoders and gated recurrent unit (GRU) recurrent neural networks (RNN) is developed and implemented to estimate tool wear in the drilling of CFRP/CFRP stacks. This method exploits the automatic feature extraction capability of autoencoders to obtain relevant features from the sensor signals acquired by a multiple sensor system during the drilling process and the memory abilities of GRU to estimate tool wear based on the extracted sensor signal features. The results obtained with the proposed method are compared with other neural network approaches, such as traditional feedforward neural networks, and considerations are made on the influence that memory-based hyperparameters have on tool wear estimation performance. Full article
(This article belongs to the Special Issue Innovative Approaches for Machining Technologies of Composite Materials)
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18 pages, 5134 KiB  
Article
Tool Wear Prediction in Glass Fiber Reinforced Polymer Small-Hole Drilling Based on an Improved Circle Chaotic Mapping Grey Wolf Algorithm for BP Neural Network
by Shanshan Hu, Hui Liu, Yufei Feng, Chang Cui, Yujie Ma, Genge Zhang and Xuechuang Huang
Appl. Sci. 2023, 13(5), 2811; https://doi.org/10.3390/app13052811 - 22 Feb 2023
Cited by 8 | Viewed by 1523
Abstract
Glass fiber reinforced polymer (GFRP) is a typical difficult-to-process material. Its drilling quality is directly affected by the processing technology and tool life; burrs, tearing, delamination and other defects will reduce the service life of GFRP structural parts. Through drilling damage and tool [...] Read more.
Glass fiber reinforced polymer (GFRP) is a typical difficult-to-process material. Its drilling quality is directly affected by the processing technology and tool life; burrs, tearing, delamination and other defects will reduce the service life of GFRP structural parts. Through drilling damage and tool wear experiments of GFRP, the thrust force, vibration amplitude, the number of processed holes, feed rate and cutting speed were found to be the main factors in drilling damage and tool wear. Using those main factors as the input layer, a tool wear and delamination factors prediction model was established based on an improved circle chaotic mapping (CCM) Grey Wolf algorithm for a back propagation (BP) neural network. Compared with the original BP neural network, the maximum prediction error of the improved BP neural network model was reduced by 71.2% and the root mean square (RMS) prediction error was reduced by 63.82%. The maximum prediction error of the delamination factor at the entrance was less than 3%, and the maximum prediction error of the delamination factor at the exit was less than 1%. The prediction results showed that the BP neural network model optimized by an improved circle chaotic mapping Grey Wolf algorithm can better predict the GFRP drilling quality and tool wear, and had higher accuracy, optimization efficiency and better robustness than the ordinary BP neural network. Full article
(This article belongs to the Special Issue Innovative Approaches for Machining Technologies of Composite Materials)
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