Tool Wear Monitoring with Artificial Intelligence Methods: A Review2023

– Special issue of International Journal of Pattern Recognition and Artificial Intelligence
– Expands upon papers from FLAIRS conference, covers various AI techniques and applications


In this article , the authors provide an overview of the recent years of development of tool wear monitoring through artificial intelligence in the general and essential requirements of offline and online methods, including techniques such as machine learning, deep learning and neural networks, which allow for the study, construction and implementation of algorithms in order to understand, improve and optimize the wear process.

– AI and computational technology are revolutionizing interior design graphics and modeling.
– These technologies offer benefits such as design iterations, material visualization, and time optimization.

– AI-based tools are transforming oncology clinical applications for personalized care.
– Challenges in applying AI-based tools in cancer care are discussed.

– Overview of tool wear monitoring through artificial intelligence
– Description of offline and online methods for tool wear monitoring

The paper discusses the use of artificial intelligence (AI) techniques such as machine learning, deep learning, and neural networks for tool wear monitoring in the manufacturing industry.

– Machine learning, deep learning, and neural networks.

– Provides an overview of tool wear monitoring through artificial intelligence.
– Identifies methods, industrial sectors, processing types, materials, and tools used.

– Digital technology and tools are being used in the design industry.
– Artificial Intelligence (AI) is one of the latest computational technologies being utilized.

– The paper reviews existing AI technology and its potential applications in manufacturing systems.
– The paper discusses tools and techniques of AI relevant to the manufacturing environment.

– The paper discusses the application of stored programs in Artificial Intelligence.
– It focuses on production systems and their role in rule-based expert systems.