The Industrial Design Engineering & Automation Conference

A series of lab demonstrations focused on how to build, manage and optimize connected systems.

A case study looking at how advanced manufacturing implementation can drive cost savings and improve safety and quality.

Additive manufacturing is no longer a nice-to-have option but has become a critical strategy for reducing time-to-market. It is now an essential part of early-stage development, tightening the time for functional prototypes and drastically reducing the turn-around of physical testing. This early confidence in the design further drives downstream time savings by providing insight on manufacturability and reduction and elimination of late-stage errors.

Often times conveyance system operating conditions including heavy impact or shock loading, aggressive contaminants and debris, moisture and washdown, and/or high operating temperatures set up conditions which cause premature failure of standard rolling element bearings.

The specific type and size of actuator that a design engineer needs depends on the application's nature. With an innumerable combination of stroke sizes, speeds, and voltage options – it can be difficult to determine the correct linear actuator components for your specific needs. Follow these five steps to achieve linear actuator design success.

Manufacturing environments are unique and deeply customized, supported by small IT teams and legacy technology and skill sets. Successful implementation of artificial intelligence in this environment requires customization, consideration for privacy protection, and easily-understood, actionable outputs. Both the hardware and software frameworks for machine learning and computer vision are now at a point to meet this challenge. Where implementation used to be a major undertaking, it is now a feasible cost. The result: manufacturers must harness this technology now to remain competitive. Using machine learning for computer vision, manufacturers can catch twice as many defects on their production line, in a fraction of the time, improving on the accuracy and speed of human quality inspection by several orders of magnitude. Lloyd Clark is an expert in artificial intelligence and machine learning and co-founder of BlueRidge Al, a visual inspection and defect detection solution for manufacturing.

In the last few years, the suppliers of image sensors have produced new products that have put much pressure on the manufacturers of camera components. The trend is relentless, and in many cases, the machine vision engineer is no longer satisfied with a USB3 or Ethernet camera. I will present machine vision trends and the newest interface technologies. Machine builders and OEM customers are now very skilled, and they expect a high standard from camera suppliers and camera components. They are not interested in receiving the image, but they need pre-process images in the cameras and to use only the data for image processing analyses. Come and join me to see benefits of high-end industrial cameras and how they can cost-effectively save bandwidth and even reduce of cameras in the application.

This presentation will introduce the concept of “hybrid AI” and discuss how it can help manufacturers more cost-effectively deploy advanced machine learning skills in existing applications. One of the key challenges for manufacturers evaluating AI is cost concerns related to replacing existing infrastructure and proven end-user processes. In comparison, a hybrid approach integrates AI with existing vision algorithms, infrastructure, and end-user processes to help reduce deployment costs and complexity. This session will highlight how machine learning differs from traditional machine vision and the advantages and drawbacks of the two approaches. In particular, we will highlight on how a hybrid approach combines the best of traditional vision algorithm and machine learning capabilities reduce inspection errors and costs. We will close the session with a real-world case study of a global consumer goods manufacturer now deploying hybrid AI in a consumer good inspection applications, and discuss some of their concerns, challenges and benefits as they have migrating to a hybrid inspection approach.