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Machines
Special Issues
Advances in CAD/CAM/CAE Technologies for Modern Manufacturing
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Advances in CAD/CAM/CAE Technologies for Modern Manufacturing

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Advanced Manufacturing".

Deadline for manuscript submissions: 28 February 2027 | Viewed by 664

Editor

Prof. Dr. Panagiotis Kyratsis

E-Mail Website
Guest Editor
Department of Product and Systems Design Engineering, University of Western Macedonia, 50100 Kila Kozani, Greece
Interests: manufacturing/machining; CAD/CAM/CAE systems; computational design; artificial intelligence (AI); additive manufacturing (AM); reverse engineering and prototyping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue entitled “Advances in CAD/CAM/CAE Technologies for Modern Manufacturing” in Machines. The aim of this Special Issue is to bring together high-quality contributions in the areas of engineering design, manufacturing and engineering analysis. All accepted papers will undergo rigorous peer review and, upon acceptance, will be published with full open access.

Potential topics include, but are not limited to, the following:

  • Computational design and algorithmic design
  • Intelligent CAD, automated feature recognition, knowledge-based engineering (KBE) and semantic modeling
  • CAD/CAM/CAE technologies in smart manufacturing
  • Advanced modeling, simulation and optimization in CAE
  • CAD-based high-fidelity multiphysics simulation
  • CAD-based cultural heritage related representations, reverse engineering and prototyping
  • AI and digital twins in modern CAD/CAM/CAE frameworks
  • Generative design and topology optimization
  • Smart CAM and autonomous machining
  • CAD-to-inspection
  • CAD-based material selection and CAE/FEM materials simulations
  • Human-centric CAD and digital ergonomics
  • CAD-based PLM systems
  • Data-driven CAE
  • Design for additive manufacturing
  • Cloud-based CAD/CAM/CAE Platforms
  • CAD/CAE tools for lifecycle assessment (LCA)
  • 3D/4D printing
  • XR (AR/VR/MR) in CAD/CAM environments
  • CAD/PLM in the fashion industry
  • CAD/CAE for biomechanical applications

Prof. Dr. Panagiotis Kyratsis
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-anonymized 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.

Keywords

  • CAD/CAM/CAE
  • smart manufacturing
  • digital twins
  • generative design
  • simulation & optimization
  • additive manufacturing
  • artificial intelligence
  • reverse engineering
  • PLM
  • XR

Benefits of Publishing in a Special Issue

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

23 pages, 2948 KB  
Article
AI-Enabled Axiomatic Design for MES-Level Process Parameters Optimization in Cloud-Based Manufacturing Execution Systems
by Xin Chen and Kai Cheng
Machines 2026, 14(7), 787; https://doi.org/10.3390/machines14070787 - 13 Jul 2026
Viewed by 382
Abstract
In cloud manufacturing, Manufacturing Execution Systems (MES) must dynamically optimize MES-level process parameters under multi-objective and constraint-intensive conditions, including quality conformity, production-takt feasibility, energy consumption, and equipment-stability risk. Conventional rule-based approaches suffer from limited efficiency, weak handling of parameter coupling, and insufficient decision [...] Read more.
In cloud manufacturing, Manufacturing Execution Systems (MES) must dynamically optimize MES-level process parameters under multi-objective and constraint-intensive conditions, including quality conformity, production-takt feasibility, energy consumption, and equipment-stability risk. Conventional rule-based approaches suffer from limited efficiency, weak handling of parameter coupling, and insufficient decision traceability. This paper presents an AI-enabled Axiomatic Design (AD) governance framework for MES-level process parameter optimization and recommendation in cloud-based MES. AI capabilities are embedded within a structured AD-based framework to decompose manufacturing requirements, estimate functional requirement-design parameter coupling, and organize constraint-first decision making. A manufacturing knowledge graph encodes equipment boundaries and process rules as computable hard constraints; a multi-agent system (MAS) separates candidate generation, evaluation, and constraint validation; and a large language model (LLM) provides an evidence-driven semantic interface. Experiments were conducted on a 10,000-sample public manufacturing-process dataset. The rule audit confirmed that the dataset-defined Historical Favorable State label follows explicit thresholds on temperature, machine speed, and vibration; the classifier output is therefore used as label-consistency evidence within the evaluation module. In the offline recommendation case, the proposed AD-governed feasible-domain ranking achieved 0/20 hard-constraint violations in the Top-20 candidates, whereas the ungated weighted energy-stability ranking and the historical-label/data-driven ranking each produced 17/20 violations under the same deterministic tie-break rule. These results support the internal consistency and engineering feasibility of the proposed governance-oriented workflow at the MES execution-parameter level, with online industrial deployment identified as future work. Full article
(This article belongs to the Special Issue Advances in CAD/CAM/CAE Technologies for Modern Manufacturing)
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