Test and Measurement
Big data inspection, the foundation for Industry 4.0
By Joe Booth, CEO, Altus Group B
ig data is the foundation for Industry 4.0, developed so advanced inspection systems must evolve from tools that simply judge ‘pass’ and ‘fail’ into highly intuitive, dynamic decision-making systems, which emphasises the need for reliable, traceable data. Artifi cial Intelligence (AI) engines can empower tools to help customers analyse and optimise the production process by managing process data from connected Serial Peripheral Interface (SPI) and Automated Optical Inspection (AOI) systems.
When we look at the optical inspection market growth trajectory, we can see how process challenges helped create innovations. For example, Solder Paste Inspection (SPI) has undergone a shift from 2D to 3D, because the 2D inspection technologies manufacturers traditionally used to collect solder deposit images could not solve shadowing problems. Thus, companies developed 3D SPI to capture the printed solder paste height to accurately measure the total volume of paste printed. Several years later, we see the same need for surface mounted component inspection with AOI systems.
Today’s board complexity is increasing with more components and joints, higher density, and shrinking package technologies. Just think about the increasing prevalence of wearables.
Whether checking exercise levels through smartwatches, or blood sugar levels with glucose monitoring devices, the market size is growing. According to Deloitte it is predicted that ‘320 million consumer health and wellness wearable devices will ship worldwide in 2022. By 2024, that fi gure will likely reach nearly 440 million units as new offerings hit the market’1
.
Basic AOI technologies using blob analysis or high megapixel cameras are no longer practical. Most decisions made are based on a ‘good/bad’ comparison of reference images, which can easily be affected by variables like component surface fi nish, board condition, component proximity, and more. Although 2D AOI is still a technology in the market, more manufacturers are adopting 3D AOI to increase board quality. The benefi ts are obvious: using clearly defi ned thresholds – backed by accurate data – will eliminate the need to constantly debug inspection programs. Moreover, measurement data generated from some 3D AOIs provides meaningful insights about the process and helps eliminate the root causes of a defect. Combining a 3D SPI with 3D AOIs enable manufacturers to accurately control and monitor the board assembly process. But with so much data, engineers are hard pressed to collect, process, and implement all the data using traditional techniques and software. Artifi cial intelligence and deep learning lay the foundation for machines to learn from the vast amounts of process data collected by adjusting the output based on the data inputs and performing tasks to help engineers perform tasks more intelligently. The many examples we hear about like autonomous vehicles use deep learning to achieve tasks by processing large amounts of data and recognising patterns in the data. This is ideal for PCB production and helps create a data set for a smart factory. From statistical process control to instant program refi nements, AI-powered platforms can intelligently apply real-time data to improve production processes. Going beyond smart
46 October 2022 Components in Electronics
factory solutions, manufacturers can use the same technology to optimise the process and adjust process parameters by exercising complex machine-learning algorithms. Realising a smart factory means taking a practical approach to process and systems, while examining areas to improve productivity. Combining machine learning with 3D measurement data generated during inspection helps manufacturers defi ne ineffi ciencies and boost line effi ciency. Machine learning uses programmed algorithms that receive and analyse input data to predict output values within an acceptable range. As new data is fed to these algorithms, they learn and optimise their operations to improve performance, developing intelligence over time.
For example, some tools allow manufacturers to simultaneously deploy programs and inspection conditions across multiple lines, which enhances productivity and, more importantly, data integrity with consistent performance. Operators can further improve line maintenance with other tools for real-time monitoring to instantly display relevant process parameters at remote locations for immediate analysis and action. What’s more, combining multipoint views from SPI, pre-refl ow AOI, and post-refl ow AOI with real data management and monitoring allows operators to determine actionable insights to optimise the processes. However, the adaptation of AI-powered process tools takes optimisation to a higher level. Converting all the data requires a simulation tool to review identifi ed defects with accumulated historical data from PCBA lines, while avoiding unnecessary downtime. Software tools can reliably allow manufacturers to predict the effects from fi ne-tuning without stopping the line. Moving forward, an AI-powered platform can autonomously render complex process
References
[1]
https://www2.deloitte.com/uk/en/insights/industry/technology/technology-media-and- telecom-predictions/2022/
wearable-technology-healthcare.html
www.cieonline.co.uk
optimisation decisions typically reserved for dedicated process engineers.
Some advantages include:
● Detecting and correcting SMT defects during process monitoring is less expensive than after fi nal test and inspection, where repairs are typically fi ve to ten times more costly.
● Detect trends in process behaviour, such as placement drift or incorrect mounting, earlier in the overall process. Without early inspection, more boards with the same defect could be rejected during functional test and fi nal inspection.
● Identify missing, skewed, or misplaced components with incorrect polarity earlier in the assembly process when component placement is verifi ed before refl owing. The above is only the tip of the iceberg of what is available today when using 3D measurement. When we start to talk about the benefi ts of connecting SPI and AOI to the rest of the line and unlocking line data, the value of machine connectivity and real time data from the whole process and analysis through algorithms becomes exponentially greater to automated process optimisations.
www.altusgroup.co.uk
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