Development of an Imaging AOI + AI Ecosystem
Friday, June 3, 2022

Section: Smart Manufacturing

Project Leader

 Wayne Zhange, IBM

Call-for-Participation Webinar

Presentation and link to recorded webinar (September 1 & 2, 2022)

SOW & Project Statement

Statement of Work (August 3, 2022)
Project Statement (August 3, 2022)

Project Motivation

  • Current industry practice: every EMS company has its own AOI image data, which is private and isolated
  • Electronics manufacturers are facing the challenge of insufficient image libraries (data sets) to train AI models for AOI and AXI, which can result in inaccurate AI models for image classification purposes
  • Image data collection — data acquisition, identification, labeling, etc. — is typically time consuming
  • There is no collaboration model for sharing image data among the EMS players, balancing the benefit and risk mitigation

Project Objective

  • Create an industrial level collaboration to construct/share image resources (in an appropriate way) to develop AI models

Proposed Strategy/Approach: Establish an Ecosystem to Collect and Share Image Data

  • Individual companies will contribute their respective image data sets to a central data repository where data will be consolidated into different categories/types to input to the AI training cycle, significantly increasing the scale of the image data
  • With the consolidated data sets in the repository center/server, these data could be further trained into different AI models for future service requests from companies
  • Companies can also request either a data set from the repository for its own AI model training purpose, or classification service from the data/AI repository server via RESTful API request
  • With this shared data/AI classification service, the accuracy of the AI model could be significantly improved, and the AOI process performance could be further improved with this new type of AI model
Reference: usage of image datasets in the AI data science field 


Mark Schaffer, iNEMI