From Anomaly Detection to Defect Classification.

This paper proposes a new approach to defect detection system design focused on exact damaged areas demonstrated through visual data containing gear wheel images. The main advantage of the system is the capability to detect a wide range of patterns of defects occurring in datasets. The methodology is built on three processes that combine different approaches from unsupervised and supervised methods.

Mechatronic Device Control by Artificial Intelligence.

Nowadays, artificial intelligence is used everywhere in the world and is becoming a key factor for innovation and progress in many areas of human life. From medicine to industry to consumer electronics, its influence is ever-expanding and permeates all aspects of our modern society. This article presents the use of artificial intelligence (prediction) for the control of three motors used for effector control in a spherical parallel kinematic structure of a designed device.

Waste Management with the Use of Heuristic Algorithms and Internet of Things Technology.

Studies have been performed to improve the process of waste management. They were fulfilled by changing the base of waste logistics management using a combination of intelligent algorithms and the IMPACT IoT platform instead of a human factor. The research was carried out on the example of real data with respect to waste management in a given area.

Analysis of Laser Sensors and Camera Vision in the Shoe Position Inspection System.

Inspection systems are currently an evolving field in the industry. The main goal is to provide a picture of the quality of intermediates and products in the production process. The most widespread sensory system is camera equipment.

Analysis of the Possibilities of Tire-Defect Inspection Based on Unsupervised Learning and Deep Learning.

At present, inspection systems process visual data captured by cameras, with deep learning approaches applied to detect defects. Defect detection results usually have an accuracy higher than 94%. Real-life applications, however, are not very common.

Parameter Identification of Cutting Forces in Crankshaft Grinding Using Artificial Neural Networks.

The intensifying of the manufacturing process and increasing the efficiency of production planning of precise and non-rigid parts, mainly crankshafts, are the first-priority task in modern manufacturing. The use of various methods for controlling the cutting force under cylindrical infeed grinding and studying its impact on crankpin machining quality and accuracy can improve machining efficiency. The paper deals with developing a comprehensive scientific and methodological approach for determining the experimental dependence parameters' quantitative values for cutting-force calculation in cylindrical infeed grinding.

Reasons for the Formation of Non-Fibrous Inclusions When Preparing Basalt Fibers by the Duplex Method.

Materials based on basalt fiber are widely used as thermal insulating material. These materials have a number of advantages, including their low thermal conductivity and fire resistance due to their natural composition. However, there is a significant drawback in that the material contain non-fibrous inclusions.