Sustainable Smart Manufacturing

Bringing Industries to Industry 4.0 and Beyond

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Real-time process monitoring

Research Team: Vignesh Selvaraj, Andrew Glaeser, Sooyoung Lee, Yunseob Hwang, Namjeong Lee, Kangsan Lee

Current research concentrates on adding smart manufacturing capability to industrial cold forging. In cold forging, tools are subject to extreme stress and tool failure can cause production delays or, when tool failure is undetected, thousands of scrapped products. Our research aims to monitor machine health, predict and react (autonomous systems) to major events that may occur during the forging process based on real-time collected data, and complete intensive data analysis through deep learning application. The ability to accurately predict when a significant event is likely to occur, such as machine failure, will enable the manufacturer to run machines as efficiently as possible and increase product output. By collecting signals directly from the machine, different machine states can be indicated by variations in the signal. The variations in the signal can indicate tool failure, process changes, poor operating conditions, or other states. Determining the correct signal to capture meaningful variation indicative of changing conditions is a large part of this research, in addition, understanding how different variations correlate to different conditions.

Energy Monitoring

Research Team: Vignesh Selvaraj, Zhicheng Xu

Manufacturing is responsible for almost 45% of total energy consumption in the US and a similar portion in other countries. Machine energy consumption accounts for a big portion of 45% and thus understanding how machine tools consume energy helps to minimize energy consumption by design optimization of the machine tools, strategic operation control, energy balance on the production line, and energy footprint and control of supply chain. Monitoring and control of horizontal and vertical integration of total manufacturing infra can be achieved by IIOT (Industrial Internet of Thing) with a similar manufacturing paradigm called smart manufacturing, digital manufacturing, and industry 4.0.

In this project, the real-time signals from the controller and other additional installed sensors were used to identify events of interest on the machine. The signals analyzed include controller data using FOCAS, energy consumption data of the machine by using an external power analyzer, and other signals from sensors like accelerometer, acoustics, etc. The data was obtained from the controller using FOCAS2. The servo current signals were dependent on the machine-relative position of the axis.

The experiments were repeated in order to capture the variance in the data obtained between different runs. As can be seen, the servo signals have a profile that is pretty consistent between trials. This can be exploited using a pattern recognition algorithm to build a model in the latter stages for event identification. In order to ensure universal applicability of the energy consumption model, i.e., for the case of legacy machines and to support the development of a mathematical model for energy consumption, the power input to the machine was also measured using a High Precision Power Analyzer from Yokogawa.

The event recognition would enable us to progress towards energy-efficient manufacturing. As we can identify the distortions in the energy consumption profile due to inefficient/abnormal operation of a component in a piece of equipment. In an effort towards IIoT, we have installed temperature sensors by the machine that could record and upload temperature data to an online server. These temperature values can be monitored in real-time. The temperature values are also stored in a database that can be used later to determine the impact of temperature on the equipment’s operation and quality of machining.

Digital-gradient Cascade Manufacturing of Core-multi-shell Cathode Materials

Research Team: Dr. Sangjin Maeng, in collaboration with Dr. Youngho Shin  @ Argonne National Laboratory

In order to overcome structural and surface instability of Ni-rich cathode materials which causes poor cyclability and thermal stability, cathode materials with a digital-gradient particle structure wrapping Ni-rich high-capacity core with Mn-rich high-stability multi-shells were synthesized successfully by Argonne National Laboratory. Mechanical properties of cathode particles is closely related to battery life and mechanical characterization of synthesized particles are being studied.