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Machines
Special Issues
Empowering Design and Production Automation with Data-Driven and Machine Learning...
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Empowering Design and Production Automation with Data-Driven and Machine Learning Approaches

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

Deadline for manuscript submissions: 15 June 2025 | Viewed by 5030

Special Issue Editor

Dr. Mehdi Tarkian

E-Mail Website
Guest Editor
Department of Management and Engineering, Linköpings Universitet, SE-581 83 Linköpin, Sweden
Interests: design automation; machine learning; data-driven

Special Issue Information

Dear Colleagues,

In today's fast paced technological landscape, the need for efficiency and adaptability in design and production cannot be overemphasized. Industrial and societal sectors struggle with fragmented knowledge and organizational silos. Modern production environments, especially ones relating automation, demand high levels of capital investment, and customized automations integral to operations are a notable financial obligation. Given their cost, it is imperative that the results of these investments operate efficiently and adaptably.

With the convergence of massive data availability and advancements in machine learning (ML) techniques, industries are empowered to embrace novel paradigms in design and production automation. This Special Issue aims to present advanced research, applications, and trends in harnessing the potential of data-driven ML methods to enhance the realms of design and production automation.

We invite authors from academia, industry, and research institutions to contribute original research articles and review papers that reflect the state of the art and emerging developments in this interdisciplinary domain.

Topics of Interest include, but are not limited to:

Foundations and Algorithms:
ML algorithms tailored for design and production tasks.
Deep learning architectures for automated design.
Reinforcement learning in production optimization.

Data Acquisition and Processing:
Synthetic data generation
Sensor networks for data collection in production environments.
Data preprocessing, cleaning, and augmentation for design automation.

Design Automation:
ML-enhanced computer-aided design (CAD) methods.
Generative design using neural networks.

Production Automation:
Predictive maintenance using ML.
Data-driven optimization of production lines.
Smart factories and Industry 4.0.

Quality Control and Assurance:
ML techniques for automated defect detection.
Data-driven approaches to quality prediction.

Supply Chain and Logistics:
Forecasting and inventory optimization using ML.
Automated warehousing solutions.

Integration of IoT with ML:
Edge computing for design and production tasks.
IoT-enabled data acquisition systems.

Case Studies and Industrial Applications:
Real-world implementations of ML in design and production settings.
Success stories, challenges, and lessons learned.

Ethical, Social, and Security Implications:
Data privacy and security in ML-driven automation.
Impact of ML automation on labor markets and job roles.
Addressing biases in data-driven design.

Dr. Mehdi Tarkian
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 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.

Keywords

  • foundations and algorithms
  • data acquisition and processing
  • design automation
  • production automation
  • quality control and assurance
  • supply chain and logistics
  • integration of IoT with ML
  • case studies and industrial applications
  • ethical, social, and security implications

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

Published Papers (4 papers)

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Research

23 pages, 7045 KiB  
Article
Optimizing Text Recognition in Mechanical Drawings: A Comprehensive Approach
by Javier Villena Toro and Mehdi Tarkian
Machines 2025, 13(3), 254; https://doi.org/10.3390/machines13030254 - 20 Mar 2025
Viewed by 380
Abstract
The digitalization of engineering drawings is a pivotal step toward automating and improving the efficiency of product design and manufacturing systems (PDMSs). This study presents eDOCr2, a framework that combines traditional OCR and image processing to extract structured information from mechanical drawings. It [...] Read more.
The digitalization of engineering drawings is a pivotal step toward automating and improving the efficiency of product design and manufacturing systems (PDMSs). This study presents eDOCr2, a framework that combines traditional OCR and image processing to extract structured information from mechanical drawings. It segments drawings into key elements—such as information blocks, dimensions, and feature control frames—achieving a text recall of 93.75% and a character error rate (CER) below 1% in a benchmark with drawings from different sources. To improve semantic understanding and reasoning, eDOCr2 integrates Vision Language models (Qwen2-VL-7B and GPT-4o) after segmentation to verify, filter, or retrieve information. This integration enables PDMS applications such as automated design validation, quality control, or manufacturing assessment. The code is available on Github. Full article
(This article belongs to the Special Issue Empowering Design and Production Automation with Data-Driven and Machine Learning Approaches)
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20 pages, 4755 KiB  
Article
Advanced Defect Detection in Wrap Film Products: A Hybrid Approach with Convolutional Neural Networks and One-Class Support Vector Machines with Variational Autoencoder-Derived Covariance Vectors
by Tatsuki Shimizu, Fusaomi Nagata, Maki K. Habib, Koki Arima, Akimasa Otsuka and Keigo Watanabe
Machines 2024, 12(9), 603; https://doi.org/10.3390/machines12090603 - 31 Aug 2024
Cited by 2 | Viewed by 1119
Abstract
This study proposes a novel approach that utilizes Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) to tackle a critical challenge: detecting defects in wrapped film products. With their delicate and reflective film wound around a core material, these products present formidable [...] Read more.
This study proposes a novel approach that utilizes Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) to tackle a critical challenge: detecting defects in wrapped film products. With their delicate and reflective film wound around a core material, these products present formidable hurdles for conventional visual inspection systems. The complex task of identifying defects, such as unwound or protruding areas, remains a daunting endeavor. Despite the power of commercial image recognition systems, they struggle to capture anomalies within wrap film products. Our research methodology achieved a 90% defect detection accuracy, establishing its practical significance compared with existing methods. We introduce a pioneering methodology centered on covariance vectors extracted from latent variables, a product of a Variational Autoencoder (VAE). These covariance vectors serve as feature vectors for training a specialized One-Class SVM (OCSVM), a key component of our approach. Unlike conventional practices, our OCSVM does not require images containing defects for training; it uses defect-free images, thus circumventing the challenge of acquiring sufficient defect samples. We compare our methodology against feature vectors derived from the fully connected layers of established CNN models, AlexNet and VGG19, offering a comprehensive benchmarking perspective. Our research represents a significant advancement in defect detection technology. By harnessing the latent variable covariance vectors from a VAE encoder, our approach provides a unique solution to the challenges faced by commercial image recognition systems. These advancements in our study have the potential to revolutionize quality control mechanisms within manufacturing industries, offering a brighter future for product integrity and customer satisfaction. Full article
(This article belongs to the Special Issue Empowering Design and Production Automation with Data-Driven and Machine Learning Approaches)
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13 pages, 2783 KiB  
Article
From Dataset Creation to Defect Detection: A Proposed Procedure for a Custom CNN Approach for Polishing Applications on Low-Performance PCs
by Albin Bajrami and Matteo Claudio Palpacelli
Machines 2024, 12(7), 453; https://doi.org/10.3390/machines12070453 - 2 Jul 2024
Cited by 1 | Viewed by 838
Abstract
This study focuses on training a custom, small Convolutional Neural Network (CNN) using a limited dataset through data augmentation that is aimed at developing weights for subsequent fine-tuning on specific defects, namely improperly polished aluminum surfaces. The objective is to adapt the network [...] Read more.
This study focuses on training a custom, small Convolutional Neural Network (CNN) using a limited dataset through data augmentation that is aimed at developing weights for subsequent fine-tuning on specific defects, namely improperly polished aluminum surfaces. The objective is to adapt the network for use in computationally restricted environments. The methodology involves using two computers—a low-performance PC for network creation and initial testing and a more powerful PC for network training using the Darknet framework—after which the network is transferred back to the initial low-performance PC. The results demonstrate that the custom lightweight network suited for a low-performance PC effectively performs object detection under the described conditions. These findings suggest that using tailored lightweight networks for recognizing specific types of defects is feasible and warrants further investigation to enhance the industrial defect detection processes in limited computational settings. This approach highlights the potential for deploying AI-driven quality control in environments with constrained hardware capabilities. Full article
(This article belongs to the Special Issue Empowering Design and Production Automation with Data-Driven and Machine Learning Approaches)
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18 pages, 1853 KiB  
Article
Empowering Manufacturing Environments with Process Mining-Based Statistical Process Control
by Onur Dogan and Ourania Areta Hiziroglu
Machines 2024, 12(6), 411; https://doi.org/10.3390/machines12060411 - 15 Jun 2024
Cited by 4 | Viewed by 1958
Abstract
The production of high-quality products and efficient manufacturing processes in modern environments, where processes vary widely, is one of the most crucial issues today. Statistical process control (SPC) and process mining (PM) effectively trace and enhance the manufacturing processes. In this direction, this [...] Read more.
The production of high-quality products and efficient manufacturing processes in modern environments, where processes vary widely, is one of the most crucial issues today. Statistical process control (SPC) and process mining (PM) effectively trace and enhance the manufacturing processes. In this direction, this paper proposes an innovative approach involving SPC and PM strategies to empower the manufacturing environment. SPC monitors key performance indicators (KPIs) and identifies out-of-control processes that deviate from specification limits, while PM discovery techniques are applied for those abnormal processes to extract the actual process flow from event logs and model it using Petri nets. Different enhancement techniques in PM, such as decision rules and root cause analysis, are then used to return the process to control and prevent future deviations. The application of the integrated SPC–PM approach is shown through case studies of production processes. SPC charts found that over 6% of processes exceeded specification limits. At the same time, PM methodologies revealed that prolonged times for the ‘Quality Control’ activity is the fundamental factor increasing the cycle time. Moreover, decision tree analysis provides rules for decreasing the cycle times of unbalanced processes. The absence of a transition from the ‘Return from Waiting’ activity to ‘Packing and Shipment’ is a critical factor in decreasing cycle times, as is the shift information. Our newly proposed methodology, which combines process analysis from PM with statistical monitoring from SPC, ensures operational excellence and consistent quality in manufacturing. This study illustrates the application of the proposed methodology through a case study in production processes, highlighting its effectiveness in identifying and addressing process deviations. Full article
(This article belongs to the Special Issue Empowering Design and Production Automation with Data-Driven and Machine Learning Approaches)
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