Outcomes, Insights and Best Practices from IIC Testbeds: MQM Testbed
authorization) cost by improving the manufacturing quality. The reason this approach was chosen was because the feedback loop required for fine-tuning at this stage of the process did not exist. In the initial phase, the focus was on how certain platforms can help, especially on the analytical engines described in the IIRA.
One key factor contributing to the MQM Testbed’ s success was paying careful attention to the specific challenge chosen by the client for the testbed to address; in this case, making improvements to the false detection rate on the failed products of the production line was an issue the client cared about and was willing to work to improve. Haier, in spite of hearing all of the hype about how IoT process can help them, had never tried to act on it due to the risk of disrupting their existing manufacturing process. Despite an initial level of skepticism and even resistance to IoT concepts, the MQM Testbed was so successful that Haier now wants to create an internal branch to renovate all of their production processes. Originally, the focus of this testbed was on one of Haier’ s air-conditioner production lines. Then, because the results were so successful, Haier agreed to expand the MQM Testbed to a second line- kitchen vents.
CHALLENGES
Many factors affect production line quality in factories. The challenge faced in the air conditioning production line is that centered on a welding station where the cooling tubes at the heat exchanger are connected. The heat exchanger is the most crucial part of the air-conditioner and the most difficult part to be detected should there be a leak caused by the welding process. Initially, the first goal was to improve efficiency of the welding process and eliminate any point of failures.
However, trouble immediately arose because the team discovered it was almost impossible to add sensors and a controller to the old welding robots of the welding station.
Due to high temperatures and the intensive sparks generated during welding, the testbed team found it impossible to apply any kind of sensor into the welding station unless the sensor was pre-installed on the welding robot. The team reported this problem in one of the regular testbed updates during one of IIC’ s quarterly meetings and solicited help from other IIC members. FujiFilm and Olympus who both have experience and solutions in welding quality control, came forward to help and offered their solutions through thermal imaging. However, the solutions offered are good for post-welding quality check and the team found, again, that the solutions cannot be integrated easily with the existing system. The clock was ticking and the chance to find a quick solution was running out.
Since an immediate solution was not available, the team decided to seek an alternative solution. The first step was to reexamine the whole production process and see if there were other angles to be used. The initial approach focused on the production side of the production line and was deemed impractical. The team decided to take a field trip and get familiar with the actual production line. The focus had been on the production station, but the team felt there is something in the process they had possibly overlooked, especially on the quality control process. When the engineering team arrived at the factory, they soon discovered that the quality control process was very primitive and there was a lot of room for improvement.
IIC Journal of Innovation- 23-