An Open Standards Based Approach to the Exchange of Data in an Automated Electronics Assembly Operation

摘要

A tier one supplier to the automotive industry has determined that a key to staying competitive in the electronics manufacturing industry is to adopt open standards for the exchange of data. Specifically, adopting the Computer Aided Manufacturing using eXtensionable markup language data exchange standards, which are being developed and maintained by the IPC as an open based standard. By making use of open standards, the cost and complexity of exchanging data on the factory floor between various machines can be significantly reduced. In addition, it prevents competitive lockout, enables portable processes, and facilitates reuse and redeployment. Through open standards, this manufacturer of automotive components feels it can more closely monitor and correct out-of-control conditions and respond more quickly to part outages and other various negative variables that effect manufacturing. To reduce the cost of implementing CAMX, a project has been started among 9 companies to produce an application program interface (API), which can be embedded into various equipment manufacturer's software programs or equipment control programs. This will allow remote machine process interaction to reduce downtime, improve efficiency, and help eliminate manual data collection and data crunching. Since all the assembly equipment will now be sending out the same data format, they will collect this data in a Message Broker. The message broker acts like a mailbox, collecting information from various machines that publish their run time data. Sending out information like alarms, process parametric data, cycle time and other production and process information depending on the application. The message broker has built in redundancy encase the network goes down. It will continue collecting and storing this critical manufacturing data. Now other applications will subscribe to this information residing in the message broker for real-time data analysis, trend analysis, and bottleneck analysis. We also store the historical data into a database for further analysis such as root cause analysis or comparing efficiencies across different shifts and even facilities.