An IoT based framework for energy monitoring and analysis of die casting workshop

In order to reduce the impact on resource shortage and environmental pollution caused by the massive use of energy, meeting the requirements of the fierce market competition, manufacturing enterprises must strengthen the awareness of the energy utilization in the workshop. As a widely distributed manufacturing system, die casting workshop consumes lots of energy with low efficiency. Due to the dynamic and complicated of energy usage, an effective energy monitoring and analysis method is still lacked in die casting workshop. This paper divides the die casting workshop into die casting machine level, die casting task level, and die casting workshop level and it proposes an energy monitoring and analysis system including data acquisition layer, data transmission layer, data storage layer, data processing layer, and data display layer based on Internet of Things (IoT) technology. A set of indicators, such as energy per process in die casting workshop, energy per part in die casting task, and energy per part in die casting machine, and so on, were calculated to interpret the energy data and evaluate the performance of a die casting workshop through data mining. With the application of the proposed system in a die casting workshop, enterprise managers can easily find potential opportunities for energy consumption reduction, and energy efficiency improvement, and propose ways to reduce energy consumption cost. The feasibility and practicability of the developed energy monitoring and analysis system was verified through a case study. (C) 2019 The Authors. Published by Elsevier B.V.