18/04/2024 Progress (FINAL Testing and Plant Simulation)

The following testing phases represent the final stages of my project, building upon my previous testing phases 6 and 7.

In testing phase 8, I added a new drain (which represents the third slide on the MPS station) as shown in figure 1 below. As I added an extra 'excess slide' for the surplus workpieces in my NX model as a fourth one, I wanted to utilize the main three slides for collecting orders. This plan was to maximize efficiency as it would lead to more throughput. As a result, the overall throughput (per day) has come up to almost 5700 workpieces while the total numbers of workpieces produced were almost 5700 too. This shows that very few workpieces have not been reused throughout the simulation. Although this has resulted in a higher input of resources than the previous testing phases, the output to input ratio remained the same. This shows that the production costs were covered while also increasing efficiency and productivity in the production line.

Figure 1: Testing phase 8 model

In contrast to phases 7 and 8, in testing phase 9 only 1 robotic arm has been implemented in the system to compare the outcome to the previous phases where 3 robotic arms were used in the system. The robotic arm has been scaled up to closely resemble real-life dimensions as shown in figure 2 below. Moreover, a failure percentage of 10% has been added to the arm (as it has for all robotic arms throughout my testing phases) to account for potential downtimes and failures. I ran the simulation for one day to collect data that I can use to compare the model to the previous one, and to my surprise, the throughput and the resources that have been used were exactly similar to the previous testing phase. This indicates that the robot has been running continuously (as there was usually a few seconds in difference between each operation and the other when I used 3 robotic arms, but by using one, these seconds are filled). Overall, this led to significantly lower power consumption (almost a third) and reduced costs (also almost a third), making it the most efficient option.

Figure 2: Final Plant Simulation model