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May, 2019


Reducing Factory Downtime with Predictive Maintenance Tools


By Tony Picciola, Technical Services Manager, Fuji America Corporation W


hether it is reducing costs, optimizing uti- lization or enhancing the customer’s expe- rience, Industry 4.0 can offer improve-


ments in almost every aspect of the manufacturing process. However, there is one element that is a priority for nearly everyone: improved overall pro- ductivity. Overall productivity can be significantly improved through the use of predictive mainte- nance tools.


Predictive Maintenance In any manufacturing industry, most


are familiar with “preventive” maintenance guidelines to avoid equipment failure. The goal is to reduce the likelihood of machine failures that result in factory downtime. Predictive maintenance starts as a philoso- phy that utilizes the production equipment’s operating conditions to make data-driven decisions. With the rise of Industry 4.0, new data


streams can be leveraged with the use of inexpensive sensors to monitor how a produc- tion equipment is performing in a variety of new ways. This information may include data collection of vibrations, temperature or power/amp fluctuations. With the use of this data and artificial intelligence (AI) software, we can now make predictions about future failures allowing operators to schedule correc- tive maintenance and prevent unexpected equipment failures. In the SMT industry, we can define the objec-


tives of predictive maintenance as improved over- all productivity and improved maintenance effi- ciency. With the use of data collection and AI soft-


ware, we can enter into a predictive maintenance operation and improve overall machine uptime. By analyzing this data, we can trigger a variety of behaviors, including taking corrective action if trends are found. As production demands increase, many proac-


tive tasks, such as machine maintenance often fall behind. This reactive maintenance scheduling approach has been shown to have a negative impact on overall production line up time by any- where from 5 to 25 percent.


into fewer spare parts required to be kept on-hand, due to the prediction of failures. This then reduces overall costs to maintain equipment as it can reduce the number of extra parts that may never be needed.


Improved Product Quality Another direct benefit of implementing pre-


dictive maintenance is the improvement in the quality of the product that is being manufactured. Fuji carries prediction to the manufacturing process and creates a process analytics tool for defect monitoring. This software moni- tors historical processes to predict potential production variances at a very early stage by analyzing data from all machines throughout the process. This data includes monitoring of the


screen printing, SPI, pick-and-place, AOI, and reflow processes. All data is collected in one central location and analyzed for trends, equipment performance and process con- trols. The direct result of analyzing this data together allows Fuji to trigger a variety of behaviors, including taking corrective action if trends were found. Possible corrective actions include:


FactoryLogix IIot/MES manufacturing intelligence dashboard for Fuji AIMEX III.


With the use of predictive maintenance tools,


production can continue to run until the data ana- lytics provide a warning in advance. Maintenance can be planned around the production schedule, creating an efficient process that also translates


ous monitoring of the data. l


due to board warpage or solder paste mis- alignment. The end result is that the place- ment process is stabilized with the continu-


Start a mask cleaning task. l PCB panels that fail SPI inspection can be Continued on next page l Part placement locations can be adjusted,


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IPTE America LLC 6245 Shiloh Road East, Suite B Alpharetta GA 30005 USA T: +1 (0) 678 807 0067 x 101 F: +1 (0) 678 807 0072 E: sales.usa@ipte.com


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