The aim of the challenge was to promote sustainable productions in industries and to equip the flagbearers of tomorrow (college students) with the necessary knowledge & skills required to achieve the same. Germany, compared to India, is a technologically more advanced and environmentally more aware country. Despite being only a 'developing' Nation, India has taken a stand to protect the environment. This challenge, hence, had more impact than any other competition as it involved both learning and implementation. Imbibing the good aspects of the 2 countries was the ulterior motive.
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I was part of the Data Team, a group of 5 students including the 2 of us from India. Our objective was to set up the Festo Learning factory and relevant sensors, access the sensory data and analyze it to draw conclusions. The refined data was then hosted online on the webserver for the other participants to use it. Hence, there was good communication among the groups so that we knew what data were to be procured and derived.
LEARNING FACTORY - Setup
The video (at the top) shows the learning factory where mock production of a part was being done. It had 5 stations:-
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Cap Sealing Station - In this station, the pneumatic arms sucked the caps from the inventory and put it on the cylinder. This was to seal the ingredients inside the cylinder. There were different coloured caps and cylinders available to give it an industry-like feeling for variety. Colour and light sensors were used to track the motion of the product along the assembly line. These sensors helped in the segregation of the product as per the further operations needed to be performed on each. They also helped in quality control by ensuring to report an error before the WIP was sent to next operation. In one of the stations, we had also made a provision to remove the defected product after this station.
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Drilling Station - Holes are drilled in the caps and cylindrical containers. Though the actual operations were not possible in lab environments owing to safety concerns and repeatability issues of the experiment, we used this setup with similar time and energy the operation would require. The values of both were found individually and fed to the sensor readings for reference to the entire end-to-end production.
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Similar to station 3, this station also had readings for time and energy stored for the particular operation required.
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Furnace - The heating station was the final step of the mock production as per our plant. It was also the bottleneck operation as it required the most time for the WIP to stay there. The temperature in the furnace was changed according to the material/colour of the cap and cylindrical body. This was also the operation that consumed most of the energy.
DATA
OPCUA and Node-Red were primarily used for the project. OPCUA or OPC Unified Architecture is a machine to machine communication protocol used for industrial automation. In our case, the main function of OPCUA was to help communicate with types of equipment,i.e., sensors and actuators, and the system for data collection and control. Node-Red was deployed for online hosting of data and make it available for download in the required format. It also helped in flow of data online. PLC or Programmable Logical Controllers were used to connect, control and power the stations.
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Python was used for analysing the data. Using time stamps from the system and energy readings, the work flow of the parts was designed. To increase the efficiency and decrease the lead time of manufacturing optimisation of the process was done. The running time and timestamps gave the position of the parts inside the system as well as any errors that were reported. The individual energy consumption of the machines and the products' parts were also evaluated and forwarded to the Live-LCA team and AR/VR teams.
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The Live-LCA or Live-Life Cycle Analysis team used our data of time and energy to find the carbon footprint of the product on the environment while the AR (Augmented Reality) team incorporated the analysed data to give the employees a visual representation of the real-time working production system.