IIoT-Enabled Manufacturing Process Monitoring and Resource Positioning

Abstract

Scheduling and planning are the central functions to increase the productivity in manufacturing. In a shop floor, these functions should be deployed in a real-time manner by considering the dynamic conditions of manufacturing processes. In this regard, the prerequisite is seamless manufacturing process monitoring to acquire live workplace data. Manual data acquisition by experienced workers can provide a reliable process report at low cost. However, this may shoulder additional responsibilities of the current workload which can affect job performance in a negative way. Recently, industrial internet-of-things technology with advanced sensors and long-ranged telecommunication devices have enabled us to acquire high quality workplace data. Therefore, the objective of this study is to develop a manufacturing process monitoring system that provides two main functions: (i) a production progress monitoring and (ii) a manufacturing resource positioning. To do this, we first analyze a target manufacturing system and extract the key characteristics for production progress monitoring. We then discuss how to select the appropriate process data and determine the data acquisition method. Production progress is measured by comparing the acquired field data with the scheduled manufacturing plan. We estimate manufacturing resources positions and workspace by (i) analyzing the operation data of overhead cranes in a shipyard and (ii) interpreting correlation lags between acoustic signals obtained by multiple microphone sensors. The developed manufacturing process monitoring system is illustrated and demonstrated with the case study of ship block assembly monitoring.