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JMMP
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Smart and Advanced Manufacturing
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Smart and Advanced Manufacturing

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

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 22121

Special Issue Editors

Dr. Azadeh Haghighi

E-Mail Website
Guest Editor
Department of Mechanical and Industrial Engineering, University of Illinois Chicago, 842 W. Taylor Street, Chicago, IL 60607, USA
Interests: smart manufacturing; additive and hybrid manufacturing; robotic manufacturing; cyber-physical systems
Dr. Prahalada Rao

E-Mail Website
Guest Editor
Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
Interests: monitoring and modeling of additive manufacturing and ultraprecision processes; big data analytics and artificial intelligence
Dr. Yunbo Zhang

E-Mail Website
Guest Editor
Department of Industrial & Systems Engineering, Rochester Institute of Technology, 81 Lomb Memorial Drive, Rochester, NY 14623-5603, USA
Interests: smart manufacturing; augmented and virtual reality for advanced manufacturing; human-computer interaction; geometric modeling & processing; additive manufacturing

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI), machine learning, and Big Data analytics are the keystones of advanced manufacturing. The value of these tools encompasses nearly all aspects of advanced manufacturing, including market research, product life cycle management, product design, process optimization and control, and quality assurance. The recent advancements within robotics, the cyber physical system (CPS), AR/VR, digital twins, and human–robot interaction, incorporating tools of AI, machine learning, and data analytics, illuminates advanced manufacturing and its importance. This Special Issue seeks to publish fundamental and applied research in the following broad areas to enhance advanced manufacturing systems, processes, or products in all manufacturing domains, not limited to subtractive, formative, additive, hybrid, continuous, and robot-assisted manufacturing processes.

  • Predictive modeling and design
  • Data-driven process monitoring, control, and optimization
  • Physics-informed machine learning (Scientific machine learning/explicit AI)
  • Digital twins (process or product digital twins) and applications
  • Sensor data fusion
  • Novel sensing and monitoring systems
  • Robot-assisted manufacturing processes and computational methods
  • Industrial IoTs and applications
  • Human–robot/machine interaction/collaboration
  • AR/VR and other immersive techniques in advanced manufacturing
  • Hybrid manufacturing (combination of additive and subtractive manufacturing)

Dr. Azadeh Haghighi
Dr. Prahalada Rao
Dr. Yunbo Zhang
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.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (7 papers)

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Research

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19 pages, 13616 KiB  
Article
Synthetic-to-Real Composite Semantic Segmentation in Additive Manufacturing
by Aliaksei Petsiuk, Harnoor Singh, Himanshu Dadhwal and Joshua M. Pearce
J. Manuf. Mater. Process. 2024, 8(2), 66; https://doi.org/10.3390/jmmp8020066 - 28 Mar 2024
Cited by 2 | Viewed by 2099
Abstract
The application of computer vision and machine learning methods for semantic segmentation of the structural elements of 3D-printed products in the field of additive manufacturing (AM) can improve real-time failure analysis systems and potentially reduce the number of defects by providing additional tools [...] Read more.
The application of computer vision and machine learning methods for semantic segmentation of the structural elements of 3D-printed products in the field of additive manufacturing (AM) can improve real-time failure analysis systems and potentially reduce the number of defects by providing additional tools for in situ corrections. This work demonstrates the possibilities of using physics-based rendering for labeled image dataset generation, as well as image-to-image style transfer capabilities to improve the accuracy of real image segmentation for AM systems. Multi-class semantic segmentation experiments were carried out based on the U-Net model and the cycle generative adversarial network. The test results demonstrated the capacity of this method to detect such structural elements of 3D-printed parts as a top (last printed) layer, infill, shell, and support. A basis for further segmentation system enhancement by utilizing image-to-image style transfer and domain adaptation technologies was also considered. The results indicate that using style transfer as a precursor to domain adaptation can improve real 3D printing image segmentation in situations where a model trained on synthetic data is the only tool available. The mean intersection over union (mIoU) scores for synthetic test datasets included 94.90% for the entire 3D-printed part, 73.33% for the top layer, 78.93% for the infill, 55.31% for the shell, and 69.45% for supports. Full article
(This article belongs to the Special Issue Smart and Advanced Manufacturing)
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19 pages, 13425 KiB  
Article
A Machine Learning Perspective to the Investigation of Surface Integrity of Al/SiC/Gr Composite on EDM
by Adel T. Abbas, Neeraj Sharma, Essam A. Al-Bahkali, Vishal S. Sharma, Irfan Farooq and Ahmed Elkaseer
J. Manuf. Mater. Process. 2023, 7(5), 163; https://doi.org/10.3390/jmmp7050163 - 8 Sep 2023
Cited by 7 | Viewed by 1869
Abstract
Conventional mechanical machining of composite is a challenging task, and thus, electric discharge machining (EDM) was used for the processing of the developed material. The processing of developed composite using different electrodes on EDM generates different surface characteristics. In the current work, the [...] Read more.
Conventional mechanical machining of composite is a challenging task, and thus, electric discharge machining (EDM) was used for the processing of the developed material. The processing of developed composite using different electrodes on EDM generates different surface characteristics. In the current work, the effect of tool material on the surface characteristics, along with other input parameters, is investigated as per the experimental design. The experimental design followed is an RSM-based Box–Behnken design, and the input parameters in the current research are tool material, current, voltage, pulse-off time, and pulse-on time. Three levels of each parameter are selected, and 46 experiments are conducted. The surface roughness (Ra) is investigated for each experimental setting. The machine learning approach is used for the prediction of surface integrity by different techniques, namely Xgboost, random forest, and decision tree. Out of all the techniques, the Xgboost technique shows maximum accuracy as compared to other techniques. The analysis of variance of the predicted solutions is investigated. The empirical model is developed using RSM and is further solved with the help of a teaching learning-based algorithm (TLBO). The SR value predicted after RSM and integrated approach of RSM-ML-TLBO are 2.51 and 2.47 µm corresponding to Ton: 45 µs; Toff: 73 µs; SV:8V; I: 10A; tool: brass and Ton: 47 µs; Toff: 76 µs; SV:8V; I: 10A; tool: brass, respectively. The surface integrity at the optimized setting reveals the presence of microcracks, globules, deposited lumps, and sub-surface formation due to different amounts of discharge energy. Full article
(This article belongs to the Special Issue Smart and Advanced Manufacturing)
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15 pages, 2276 KiB  
Article
Machine-Learning-Based Thermal Conductivity Prediction for Additively Manufactured Alloys
by Uttam Bhandari, Yehong Chen, Huan Ding, Congyuan Zeng, Selami Emanet, Paul R. Gradl and Shengmin Guo
J. Manuf. Mater. Process. 2023, 7(5), 160; https://doi.org/10.3390/jmmp7050160 - 3 Sep 2023
Cited by 3 | Viewed by 3370
Abstract
Thermal conductivity (TC) is greatly influenced by the working temperature, microstructures, thermal processing (heat treatment) history and the composition of alloys. Due to computational costs and lengthy experimental procedures, obtaining the thermal conductivity for novel alloys, particularly parts made with additive manufacturing, is [...] Read more.
Thermal conductivity (TC) is greatly influenced by the working temperature, microstructures, thermal processing (heat treatment) history and the composition of alloys. Due to computational costs and lengthy experimental procedures, obtaining the thermal conductivity for novel alloys, particularly parts made with additive manufacturing, is difficult and it is almost impossible to optimize the compositional space for an absolute targeted value of thermal conductivity. To address these difficulties, a machine learning method is explored to predict the TC of additive manufactured alloys. To accomplish this, an extensive thermal conductivity dataset for additively manufactured alloys was generated for several AM alloy families (nickel, copper, iron, cobalt-based) over various temperatures (300–1273 K). This unique dataset was used in training and validating machine learning models. Among the five different regression machine learning models trained with the dataset, extreme gradient boosting performs the best as compared with other models with an R2 score of 0.99. Furthermore, the accuracy of this model was tested using Inconel 718 and GRCop-42 fabricated with laser powder bed fusion-based additive manufacture, which have never been observed by the extreme gradient boosting model, and a good match between the experimental results and machine learning prediction was observed. The average mean error in predicting the thermal conductivity of Inconel 718 and GRCop-42 at different temperatures was 3.9% and 2.08%, respectively. This paper demonstrates that the thermal conductivity of novel AM alloys could be predicted quickly based on the dataset and the ML model. Full article
(This article belongs to the Special Issue Smart and Advanced Manufacturing)
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18 pages, 2636 KiB  
Article
Digital Twin Based SUDIHA Architecture to Smart Shopfloor Scheduling
by Hassan Khadiri, Souhail Sekkat and Brahim Herrou
J. Manuf. Mater. Process. 2023, 7(3), 84; https://doi.org/10.3390/jmmp7030084 - 26 Apr 2023
Cited by 1 | Viewed by 2440
Abstract
Standing on the brink of the fourth industrial revolution, Cyber Physical Systems (CPS) are considered the basic components of the Smart Factory. One important challenge in cyber physical production systems is dynamic scheduling that can handle random disruptions such as failures, raw material [...] Read more.
Standing on the brink of the fourth industrial revolution, Cyber Physical Systems (CPS) are considered the basic components of the Smart Factory. One important challenge in cyber physical production systems is dynamic scheduling that can handle random disruptions such as failures, raw material shortages and quality defects. To achieve dynamic scheduling, we have proposed a Supervised and Distributed Holonic architecture we called SUDIHA. This architecture incorporates three Holons: Product Holon, Resource Holon and Order Holon and combines global supervision, achieved by Product Holon, with dynamic local control, achieved by Resource Holon. The Digital Twin (DT) concept is generally used to design CPS; it is virtual copies of the system that can interact with the physical counterparts in a bi-directional way. It seems to be promising to tackle the complexity and increase manufacturing system flexibility. In this paper, we use a DT Model to improve the SUDIHA architecture. We propose a Digital Twin based SUDIHA architecture (DT-SUDIHA). The paper will describe Digital Twins’ configuration of each Holon of the SUDIHA Architecture, and the intelligent and real time data driven operation control of this architecture. A case study is carried out at the ENSAM-Meknes flexible workshop to prove the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Smart and Advanced Manufacturing)
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23 pages, 5475 KiB  
Article
Systematic Approach for Investigating Temporal Variability in Production Systems to Improve Production Planning and Control
by Rocky Telatko and Dirk Reichelt
J. Manuf. Mater. Process. 2023, 7(2), 78; https://doi.org/10.3390/jmmp7020078 - 18 Apr 2023
Cited by 2 | Viewed by 2026
Abstract
Including the inherent temporal variability in a production system in planning and control processes can ensure the fulfillment of the production schedule and increase key performance indicators. This benefits the sustainable and efficient use of the system. The current lack of consideration of [...] Read more.
Including the inherent temporal variability in a production system in planning and control processes can ensure the fulfillment of the production schedule and increase key performance indicators. This benefits the sustainable and efficient use of the system. The current lack of consideration of this inherent temporal variability in production planning leads to optimistic estimates and calculations of planned values that cannot be met. To complete this information, the inherent temporal variability in a production system is investigated using a systematic approach. This approach detects, identifies, and quantifies inherent temporal variability and is applied to a data base created via an automated, event-driven procedure. The approach is tested in a smart factory laboratory. The results to date on improving production planning and control are promising as key performance indicators have been increased. There is still a need for action to ensure the fulfillment of the production schedule. Concluding, work on this topic has just begun, as can be seen from the discussion section. More data need to be collected and aggregated for future research. This publication is intended to motivate researchers to address this issue and better manage the existing uncertainty in production through the use of data. Full article
(This article belongs to the Special Issue Smart and Advanced Manufacturing)
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22 pages, 12242 KiB  
Article
Abrasive Water Jet Milling as An Efficient Manufacturing Method for Superalloy Gas Turbine Components
by Jonas Holmberg, Anders Wretland and Johan Berglund
J. Manuf. Mater. Process. 2022, 6(5), 124; https://doi.org/10.3390/jmmp6050124 - 20 Oct 2022
Cited by 11 | Viewed by 5122
Abstract
In order to improve efficiency when manufacturing gas turbine components, alternative machining techniques need to be explored. In this work, abrasive water jet (AWJ) machining by milling has been investigated as an alternative to traditional milling. Various test campaigns have been conducted to [...] Read more.
In order to improve efficiency when manufacturing gas turbine components, alternative machining techniques need to be explored. In this work, abrasive water jet (AWJ) machining by milling has been investigated as an alternative to traditional milling. Various test campaigns have been conducted to show different aspects of using AWJ milling for the machining of superalloys, such as alloy 718. The test campaigns span from studies of individual AWJ-milled tracks, multi-pass tracks, and the machining of larger components and features with complex geometry. In regard to material removal rates, these studies show that AWJ milling is able to compete with traditional semi/finish milling but may not reach as high an MRR as rough milling when machining in alloy 718. However, AWJ milling requires post-processing which decreases the total MRR. It has been shown that a strong advantage with AWJ milling is to manufacture difficult geometries such as narrow radii, holes, or sharp transitions with kept material removal rates and low impact on the surface integrity of the cut surface. Additionally, abrasive water jet machining (AWJM) offers a range of machining possibilities as it can alter between cutting through and milling. The surface integrity of the AWJM surface is also advantageous as it introduces compressive residual stress but may require post-processing to meet similar surface roughness levels as traditional milling and to remove unwanted AWJM particles from the machined surface. Full article
(This article belongs to the Special Issue Smart and Advanced Manufacturing)
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23 pages, 5550 KiB  
Review
Recent Advancements in Post Processing of Additively Manufactured Metals Using Laser Polishing
by Majed Ali, Abdalmageed Almotari, Anwar Algamal and Ala Qattawi
J. Manuf. Mater. Process. 2023, 7(3), 115; https://doi.org/10.3390/jmmp7030115 - 15 Jun 2023
Cited by 6 | Viewed by 3257
Abstract
The poor surface roughness associated with additively manufactured parts can influence the surface integrity and geometric tolerances of produced components. In response to this issue, laser polishing (LP) has emerged as a potential technique for improving the surface finish and producing parts with [...] Read more.
The poor surface roughness associated with additively manufactured parts can influence the surface integrity and geometric tolerances of produced components. In response to this issue, laser polishing (LP) has emerged as a potential technique for improving the surface finish and producing parts with enhanced properties. Many studies have been conducted to investigate the effect of LP on parts produced using additive manufacturing. The results showed that applying such a unique treatment can significantly enhance the overall performance of the part. In LP processes, the surface of the part is re-melted by the laser, resulting in smaller peaks and shallower valleys, which enable the development of smoother surfaces with the help of gravity and surface tension. Precise selection of laser parameters is essential to achieve optimal enhancement in the surface finish, microstructure, and mechanical properties of the treated parts. This paper aims to compile state-of-the-art knowledge in LP of additively manufactured metals and presents the optimal process parameters experimentally and modeling using artificial machine learning. The effects of laser power, the number of laser re-melting passes, and scanning speed on the final surface roughness and mechanical properties are comprehensively discussed in this work. Full article
(This article belongs to the Special Issue Smart and Advanced Manufacturing)
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