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Guidance for Predictive Quality with Eigen on AWS

Overview

This Guidance demonstrates how Eigen Industrial Vision integrates machine vision in addition to manufacturing process and quality data on every machine part. It shows how process parametric data from machines' programmable logic controllers (PLCs) and visual defects are used to build scalable machine learning (ML) models for predicting quality. With these insights, you can then take actions that prevent defective products by adjusting process controls to reduce or eliminate quality issues. You can also access, search, and compare quality results and process data to conduct root cause analysis.

How it works

These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.

Download the architecture diagram

Well-Architected Pillars

The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.

This Guidance uses Amazon Managed Grafana to monitor metrics that relate to operational performance and are configured to alert directly to teams on operational exceptions. Eigen has built-in custom metrics that are tracked to help ensure and measure operational performance. Additionally, Eigen streamlines resource management through automated job scheduling, enhancing responsiveness to varying demands.

Read the Operational Excellence whitepaper 

This Guidance uses AWS IAM Identity Center for single sign-on (SSO), protected by multi-factor authentication (MFA). IAM Identity Center simplifies user authentication, providing a seamless and secure log-in experience. Eigen uses IAM Identity Center for user access control, reducing the complexity of managing multiple credentials and improving overall system usability.

Read the Security whitepaper 

This Guidance is implemented using a loosely-coupled architecture that leverages queues to help ensure data ingestion is scalable. Edge devices employ their own queues, allowing network interrupts to be queued and retried. This Guidance uses the following reliability-focused strategies:

  • Amazon Simple Queue Service (Amazon SQS) queues are designed for scalable and reliable data ingestion. This design allows the system to handle varying data loads without compromising performance or reliability.
  • Edge devices use their own queues, allowing network interrupts to be queued and retried. This mechanism helps ensure that interruptions or failures in data transmission are managed and retried systematically for improved reliability.
  • All API calls are designed to be stateless and idempotent, reducing the risk of errors and promoting consistent behavior, even in the event of network interruptions or transient failures.
Read the Reliability whitepaper 

In this Guidance, Lambda enables serverless computing which allows for the ability to run code without the need for provisioning or managing servers. By utilizing Lambda functions, Eigen can automate tasks to ensure quick and efficient execution of operations, reduce latency, and enhance overall responsiveness.

Read the Performance Efficiency whitepaper 

This Guidance optimizes costs through AWS Cost Explorer , Lambda , and AWS Auto Scaling . AWS Cost Explorer enables users to perform regular bill analysis, helping them understand the cost breakdown of storage and compute resources. Lambda automates resource management and schedules the start and stop of compute resources based on predefined patterns. This serverless approach enhances efficiency and reduces costs by helping ensure that resources are active only when required. Additionally, AWS Auto Scaling allows for dynamic resource adjustments. The custom auto-scaling monitoring mechanisms respond to fluctuations in customer load by automatically scaling compute resources. This helps optimal performance during peak times, while minimizing costs during periods of lower demand.

Read the Cost Optimization whitepaper 

Using Lambda functions, this Guidance helps ensures optimal compute resource allocation by stopping and starting resources based on predefined schedules. By efficiently managing compute resources based on demand, Lambda minimizes unnecessary energy consumption during idle periods. This approach ensures that resources are only active when needed, reducing overall energy consumption.

Additionally, serverless Lambda functions contribute to a more sustainable computing model. Serverless architectures, such as those employed by Lambda , enable optimal resource utilization by automatically scaling based on demand. This results in reduced energy usage compared to traditional server-based models.

Read the Sustainability whitepaper 

Disclaimer

The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.