Machine learning and AI applications for SMT production

Machine learning and AI applications for SMT production

Centralization and standardization of data as well as real-time analysis are also the basis for increasing efficiency, avoiding errors and creating more added value in SMT production. However, electronics manufacturers are faced with the challenge of collecting and analyzing the data accordingly in order to exploit these benefits. This is where the machine integration platform of an MES/MOM specialist can help.

Itac Software AG offers the iTAC.SMT.Edge integration platform for standardizing and centralizing data. The iTAC.IIoT.Edge software is responsible for the subsequent real-time data analysis and further processing. These combined solutions can be used to implement machine learning and AI applications, among other things.

"In SMT production, there are machines and systems from different manufacturers and of different ages that use different communication methods. This makes data transfer and analysis difficult," explains Peter Bollinger, Itac CEO: "The data must be reliably transmitted to higher-level systems. Our tools make it easy to record, link and analyze the data from all SMT machines in real time."

One of the tasks of the analysis tool is to combine IIoT data with MES data to create flat data structures and to analyze this data in real time. The data packages can also be forwarded to other analysis or ML/AI tools used by the customer.

AI algorithms for monitoring and analysis

By using the two edge solutions as components of MOM (Manufacturing Operations Management), numerous use cases for advanced and digitalized SMT production can be developed. For example, cycle time monitoring: AI algorithms intelligently monitor the cycle time and detect abnormal device behavior.

"In production, the pursuit of greater efficiency requires a continuous reduction in cycle times," says Bollinger. "Active monitoring of times and the use of AI to detect abnormal equipment behavior and alert in the event of deviations results in significant time savings. This is because the response times for problems and the associated throughput times are reduced. Targeted, proactive problem solving is also possible."

Another use case can be the reduction of AOI pseudo errors. AI algorithms minimize the number of pseudo errors in automatic testing devices. This is because most SMT lines with AOI have to contend with a high rate of pseudo errors (30 to 80%). With the use of AI, it is possible to distinguish between real defects and false calls with a high degree of reliability. The need for manual testing and the associated time and costs are reduced by up to 60%. This results in a higher throughput while simultaneously supporting zero-defect production.

Based on the solution presented, AI algorithms can also calculate the remaining useful life of devices in favor of predictive maintenance. By monitoring machine condition data, AI algorithms can predict problems or impending equipment faults, for example to enable timely machine repairs or estimate the remaining useful life.

These are just three of numerous possible scenarios that can be used to achieve efficiency gains, cost savings and digitalization advantages in SMT production. One user of these tools, for example, is RAFI GmbH & Co. On the one hand, it produces interfaces for human-machine communication (HMI) for a wide range of technology platforms and applications. The Ravensburg-based company relies on a high level of vertical integration in many areas: Touch sensors, metalworking, electronics production, plastic injection molding, application technology, component and system assembly - all of this is manufactured in-house and assembled into systems. A major area of application is the functions of mobile machinery for agriculture, construction or municipal applications. RAFI is also an EMS provider. The AI-supported data analysis and management tools bring decisive advantages both in terms of the vertical integration of the company's own product range and the flexibility of the manufacturing services business.

The iTAC.MOM.Suite is a holistic manufacturing management system that is used worldwide by companies in various industries such as automotive, electronics/EMS, telecommunications, medical technology, metal industry and energy. Developer Itac was founded in 1998 and is an independent company of the Dürr mechanical and plant engineering group. Itac is headquartered in Montabaur. There are subsidiaries in the USA, Mexico, China and Japan. There is also a worldwide partner network for sales and service.

www.itacsoftware.com, www.rafi-group.com

  • Issue: Januar
  • Year: 2020
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