Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.2
3.3
Journals
JMMP
Special Issues
Technological Advances and Industrial Applications in Intelligent Manufacturing
share announcement

Technological Advances and Industrial Applications in Intelligent Manufacturing

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 10389

Special Issue Editor

Dr. Musaddiq Al Ali

E-Mail Website
Guest Editor
Department of Advanced Science and Technology, Toyota Technological Institute, Nagoya, Japan
Interests: laser matter interaction; composite structures; topology optimization; additive manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to a Special Issue of the Journal of Manufacturing and Materials Processing on "Technological Advances and Industrial Applications in Intelligent Manufacturing". This collection focuses on the transformative integration of additive manufacturing and inverse problem-solving within intelligent manufacturing.

Additive manufacturing, which is known for creating intricate geometries and minimizing material waste, has revolutionized traditional manufacturing. Coupled with inverse problem-solving techniques, it enables the optimization of material distribution, structural integrity, and functional performance, facilitating the design of highly efficient and customized components. Inverse problem-solving identifies optimal design parameters and manufacturing strategies based on desired outcomes, ensuring precision in evaluating material behavior, structural performance, and thermal properties.

The convergence of these technologies enhances innovation, efficiency, and cost-effectiveness, with applications spanning the aerospace and automotive industries, biomedical devices, and energy systems. This Special Issue invites cutting-edge research, theoretical insights, and practical implementations that address the following: 

  • Innovative solutions to inverse problems in design and production; 
  • Material characterization, properties, and simulation techniques; 
  • Non-parametric and structural optimization; 
  • Multiphysics design and industrial case studies; 
  • AI integration in intelligent manufacturing; 
  • Applications of nanomaterials in additive manufacturing. 

We welcome original research, reviews, and case studies and look forward to advancing intelligent manufacturing together.

Dr. Musaddiq Al Ali
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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 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 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.

Keywords

  • additive manufacturing
  • inverse problem solving
  • artificial intelligence
  • non-parametric optimization
  • nanomaterials
  • simulation
  • intelligent manufacturing
  • multiphysics design
  • structural optimization
  • industrial applications

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

35 pages, 7857 KB  
Article
Toward Large Language Model-Driven Symbolic Topology Optimisation for Rapid Structural Concept Generation in Manufacturable Design
by Musaddiq Al Ali
J. Manuf. Mater. Process. 2026, 10(4), 117; https://doi.org/10.3390/jmmp10040117 - 30 Mar 2026
Viewed by 907
Abstract
Topology optimisation is a powerful methodology for identifying efficient material distributions within prescribed design domains. However, conventional approaches rely heavily on gradient-based optimisation and repeated numerical simulations, which impose significant computational cost and limit their use in early-stage design exploration. This work introduces [...] Read more.
Topology optimisation is a powerful methodology for identifying efficient material distributions within prescribed design domains. However, conventional approaches rely heavily on gradient-based optimisation and repeated numerical simulations, which impose significant computational cost and limit their use in early-stage design exploration. This work introduces a generative design framework, referred to as Large Language Model-Driven Symbolic Topology Optimisation (LLM-DSTO), in which large language models act as conceptual design generators. Engineering problems are formulated through structured textual descriptions defining the design domain, boundary conditions, loading scenarios, and material constraints. The language model interprets these inputs and produces symbolic representations of candidate structural topologies. The generated layouts are evaluated using physics-informed objective functions and refined iteratively through lightweight computational procedures. The resulting designs exhibit coherent load paths, strong structural connectivity, and material distributions that are consistent with practical manufacturing requirements, including additive manufacturing constraints. The proposed framework is validated across structural, thermal, thermofluid, and compliant mechanism design problems. Quantitative results show that the generated structures achieve approximately 87.5% of the stiffness obtained using the classical SIMP method for the cantilever benchmark, while reaching about 94.3% of the thermal performance in heat sink optimisation. These results are obtained without repeated finite element simulations, demonstrating a significant reduction in computational cost. In addition, the framework is extended to three-dimensional topology generation, producing volumetric structures under a 50% material volume constraint with coherent internal load paths. Full article
(This article belongs to the Special Issue Technological Advances and Industrial Applications in Intelligent Manufacturing)
►▼ Show Figures

Figure 1

15 pages, 1668 KB  
Article
Dynamic Reuleaux Venturi with Boundary-Imposed Swirl
by Lorenzo Albanese
J. Manuf. Mater. Process. 2026, 10(3), 81; https://doi.org/10.3390/jmmp10030081 - 26 Feb 2026
Cited by 1 | Viewed by 499
Abstract
In-line cavitation is relevant to many continuous processes; however, its intensity depends on flow rate, available pressure, temperature, fluid properties, and plant conditions, complicating the maintenance of a repeatable regime within a prescribed band. This paper presents the DVRA, an actuated Venturi module [...] Read more.
In-line cavitation is relevant to many continuous processes; however, its intensity depends on flow rate, available pressure, temperature, fluid properties, and plant conditions, complicating the maintenance of a repeatable regime within a prescribed band. This paper presents the DVRA, an actuated Venturi module with a Reuleaux triangular cross-section for in-operation regulation of hydrodynamic cavitation through device configuration. The novelty lies in combining two degrees of freedom—an in-operation adjustable hydraulic throat and boundary-imposed swirl forcing—within a compact in-line device: all rotation is confined to the module, and no rotation of the process line is required. The hydraulic throat is tuned via an actuated elastomeric liner, while swirl is generated by external end collars. Reproducible operational conventions are introduced together with a normalized input set and a configuration-space formalism that distinguishes admissible from achievable configurations. Regulation is cast as a control-oriented inverse mapping given a target band for an in-line estimated cavitation indicator and standard industrial measurements of flow rate, pressure, and temperature; configuration commands are selected to keep the indicator within bounds. The contribution is methodological and provides an implementable basis; comprehensive validation and performance benchmarking are outside the scope of this paper and will be reported separately. Full article
(This article belongs to the Special Issue Technological Advances and Industrial Applications in Intelligent Manufacturing)
►▼ Show Figures

Figure 1

22 pages, 4496 KB  
Article
Non-Isothermal Process of Liquid Transfer Molding: Transient 3D Simulations of Fluid Flow Through a Porous Preform Including a Sink Term
by João V. N. Sousa, João M. P. Q. Delgado, Ricardo S. Gomez, Hortência L. F. Magalhães, Felipe S. Lima, Glauco R. F. Brito, Railson M. N. Alves, Fernando F. Vieira, Márcia R. Luiz, Ivonete B. Santos, Stephane K. B. M. Silva and Antonio G. B. Lima
J. Manuf. Mater. Process. 2025, 9(7), 243; https://doi.org/10.3390/jmmp9070243 - 18 Jul 2025
Cited by 1 | Viewed by 1454
Abstract
Resin Transfer Molding (RTM) is a widely used composite manufacturing process where liquid resin is injected into a closed mold filled with a fibrous preform. By applying this process, large pieces with complex shapes can be produced on an industrial scale, presenting excellent [...] Read more.
Resin Transfer Molding (RTM) is a widely used composite manufacturing process where liquid resin is injected into a closed mold filled with a fibrous preform. By applying this process, large pieces with complex shapes can be produced on an industrial scale, presenting excellent properties and quality. A true physical phenomenon occurring in the RTM process, especially when using vegetable fibers, is related to the absorption of resin by the fiber during the infiltration process. The real effect is related to the slowdown in the advance of the fluid flow front, increasing the mold filling time. This phenomenon is little explored in the literature, especially for non-isothermal conditions. In this sense, this paper does a numerical study of the liquid injection process in a closed and heated mold. The proposed mathematical modeling considers the radial, three-dimensional, and transient flow, variable injection pressure, and fluid viscosity, including the effect of liquid fluid absorption by the reinforcement (fiber). Simulations were carried out using Computational Fluid Dynamic tools. The numerical results of the filling time were compared with experimental results, and a good approximation was obtained. Further, the pressure, temperature, velocity, and volumetric fraction fields, as well as the transient history of the fluid front position and injection fluid volumetric flow rate, are presented and analyzed. Full article
(This article belongs to the Special Issue Technological Advances and Industrial Applications in Intelligent Manufacturing)
►▼ Show Figures

Figure 1

17 pages, 4101 KB  
Article
Design and Manufacture of a Flexible Adaptive Fixture for Precision Grinding of Thin-Walled Bearing Rings
by Yao Shi, Yu He, Jun Zha, Bohao Chen, Chaoyu Shi and Ming Wu
J. Manuf. Mater. Process. 2025, 9(5), 139; https://doi.org/10.3390/jmmp9050139 - 22 Apr 2025
Cited by 3 | Viewed by 3945
Abstract
Addressing the issues of easy deformation and difficult-to-control machining accuracy of thin-walled bearing rings during precision grinding due to clamping forces, existing research mainly employs methods such as elastic clamping, hydraulic control, pneumatic control, and vacuum adsorption to tackle the clamping problem. However, [...] Read more.
Addressing the issues of easy deformation and difficult-to-control machining accuracy of thin-walled bearing rings during precision grinding due to clamping forces, existing research mainly employs methods such as elastic clamping, hydraulic control, pneumatic control, and vacuum adsorption to tackle the clamping problem. However, these methods still suffer from problems such as uneven clamping force, insufficient adaptability, and limited machining accuracy. In this paper, a novel fixture suitable for precision grinding of thin-walled bearing rings is designed. By analyzing the working principle of the fixture and considering the processing characteristics of thin-walled bearing rings, the fixture structure is designed and optimized to enhance its clamping stability and machining accuracy. Modal analysis and stress-displacement analysis are conducted to verify the stability and performance of the new fixture during the machining process. The research results show that the fixture can effectively reduce the deformation of thin-walled bearing rings, improve machining quality and efficiency, and provide a feasible solution for high-precision grinding of thin-walled bearing rings. Full article
(This article belongs to the Special Issue Technological Advances and Industrial Applications in Intelligent Manufacturing)
►▼ Show Figures

Figure 1

Review

Jump to: Research

41 pages, 5217 KB  
Review
Smart Drilling: Integrating AI for Process Optimisation and Quality Enhancement in Manufacturing
by Martina Panico and Luca Boccarusso
J. Manuf. Mater. Process. 2025, 9(12), 386; https://doi.org/10.3390/jmmp9120386 - 24 Nov 2025
Cited by 1 | Viewed by 2590
Abstract
Drilling is fundamental to the assembly of aerospace structures, where millions of fastening holes must meet stringent structural and geometric requirements. Despite significant technological advances, hole quality remains sensitive to nonlinear and stochastic interactions between mechanics, thermal effects, tribology, and structural configuration. This [...] Read more.
Drilling is fundamental to the assembly of aerospace structures, where millions of fastening holes must meet stringent structural and geometric requirements. Despite significant technological advances, hole quality remains sensitive to nonlinear and stochastic interactions between mechanics, thermal effects, tribology, and structural configuration. This review consolidates recent advances in intelligent drilling, focusing on how sensors and artificial intelligence (AI) are integrated to enable process understanding, prediction, and control. In-process monitoring modalities (e.g., cutting forces/torque, vibration, acoustic emission, motor current/active power, infrared thermography, and vision) are examined with respect to signal characteristics, feature design, and modelling choices for real-time anomaly detection, tool condition monitoring, and phase/interface recognition. Predictive quality modelling of burr, delamination, roughness, and roundness is discussed across statistical learning, kernel methods, and neural and hybrid models. Offline parameter optimisation via surrogate-assisted and evolutionary algorithms is considered alongside adaptive control strategies. Practical aspects of robotic drilling and multifunctional end-effectors are highlighted as enablers of embedded sensing and feedback. Finally, cross-cutting challenges (e.g., limited, heterogeneous datasets and model generalisability across materials, tools, and geometries) are outlined, together with research directions including curated multi-sensor benchmarks, multi-source transfer learning, physics-informed machine learning, and explainable AI to support trustworthy deployment in aerospace manufacturing. Full article
(This article belongs to the Special Issue Technological Advances and Industrial Applications in Intelligent Manufacturing)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop