APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES FOR THE AUTOMATION OF DESIGN AND TECHNOLOGICAL PRE-PRODUCTION IN THE FOOTWEAR INDUSTRY
Authors:
1.
ZAO MOF "Parizhskaya kommuna"
Moskva, Russian Federation
Moskva, Russian Federation
2.
ZAO MOF "Parizhskaya kommuna"
Moskva, Russian Federation
Moskva, Russian Federation
3.
Rossiyskiy gosudarstvennyy universitet im. A.N. Kosygina (Tehnologii. Dizayn. Iskusstvo)
Moskva, Russian Federation
Moskva, Russian Federation
4.
KNITU
Type:
Article
Pages:
from 159
to 165
Status:
Published
Received:
14.02.2026
Accepted:
02.03.2026
Published:
05.04.2026
Language:
Russian
Keywords:
FOOTWEAR INDUSTRY, DESIGN AND TECHNOLOGICAL PRE-PRODUCTION, 3D SCANNING, COMPUTER VISION, DEFECT DETECTION, NESTING, OPTIMIZATION
Abstract:
The digital transformation of the footwear industry is increasing the demands on the speed of model development, the accuracy of design data, and the management of material waste in cutting operations. However, classical computer-aided design (CAD) systems largely automate the construction of patterns and components, as well as the output of documentation, but they offer limited support for flexible design, production data analysis, and quality control. This paper proposes a methodology for integrating artificial intelligence modules into the design and technological pre-production process for footwear: ranging from the processing of 3D last or foot data and the construction of component patterns, to computer vision technologies for assessing material quality and improving nesting algorithms while accounting for material defects and their technological constraints. The paper describes the data sources, the rules for their unification and preparation, the solution architecture, performance indicators, as well as the principles of data verification and logging to ensure the reproducibility of results. The methodology is intended for the gradual enhancement of existing enterprise CAD systems, without replacing them. The implementation of this methodology in a pilot production run demonstrated an increase in the material utilization rate by 4.4% and a reduction in data preparation time for cutting by 14.3%.
The digital transformation of the footwear industry is increasing the demands on the speed of model development, the accuracy of design data, and the management of material waste in cutting operations. However, classical computer-aided design (CAD) systems largely automate the construction of patterns and components, as well as the output of documentation, but they offer limited support for flexible design, production data analysis, and quality control. This paper proposes a methodology for integrating artificial intelligence modules into the design and technological pre-production process for footwear: ranging from the processing of 3D last or foot data and the construction of component patterns, to computer vision technologies for assessing material quality and improving nesting algorithms while accounting for material defects and their technological constraints. The paper describes the data sources, the rules for their unification and preparation, the solution architecture, performance indicators, as well as the principles of data verification and logging to ensure the reproducibility of results. The methodology is intended for the gradual enhancement of existing enterprise CAD systems, without replacing them. The implementation of this methodology in a pilot production run demonstrated an increase in the material utilization rate by 4.4% and a reduction in data preparation time for cutting by 14.3%.
Keywords:
FOOTWEAR INDUSTRY, DESIGN AND TECHNOLOGICAL PRE-PRODUCTION, 3D SCANNING, COMPUTER VISION, DEFECT DETECTION, NESTING, OPTIMIZATION
FOOTWEAR INDUSTRY, DESIGN AND TECHNOLOGICAL PRE-PRODUCTION, 3D SCANNING, COMPUTER VISION, DEFECT DETECTION, NESTING, OPTIMIZATION
