Enhancing Smart Factories Through Intelligent Measurement Devices Altering Smart Factories via IoT Infusion

Abstract

The technology’s integration into factories has accelerated automation’s growth, creating autonomous working conditions and cutting-edge capacity for production. Modern and smart factories provide consumers with time-saving solutions and reliable outcomes. The present paper presents the concept of Event-Dependent Process Planning (EDPP), which seeks to improve the time-effectiveness of smart factories. The suggested approach automatically arranges planned and queued activities according to previous results, matching them with customer demands. Before process planning, essential data are provided by intelligent measuring equipment in the factories. Recurrent learning ensures the integrated process planning is successful and aligned with customers’ needs. The efficiency with which the planning method exceeded customer expectations in earlier years is used to instruct this learning process. Applications of the technique are made to the manufacturing automation process’s delivery and production layers. Essential metrics like processing time, response ratio, delivery delay, and backlogs are evaluated in an experimental analysis to validate the suggested process strategy. The proposed EDPP achieves 11.38% less processing time, 5.43% high response ratio, 10.18% less delivery delay, and 3.8% less backlog rate.

Keywords

• IoT
• process planning
• queuing and scheduling
• recurrent learning
• smart factory
• cutting-edge capacity