Empowering Design and Production Automation with Data-Driven and Machine Learning Approaches

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.

• 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