Closed-Loop Manufacturing and the Circular Economy


Visual Inspection

PixelPaint - The future of customized paint jobs

Read more about PixelPaint at ABB.

Also, Cognex released a series of videos about their 3D Vision Solutions for Food and Beverage and Consumer Product Inspection.

Acoustic Monitoring

The Engineer Talks Episode 7: Industrial applications of big science

Assembly Line

AI Vision for Monitoring Applications in Manufacturing and Industrial Environments

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πŸ”– Topics: AI, quality assurance, machine vision, worker safety

🏒 Organizations: ADLINK

In traditional industrial and manufacturing environments, monitoring worker safety, enhancing operator efficiency, and improving quality assurance were physical tasks. Today, AI-enabled machine vision technologies replace many of these inefficient, labor-intensive operations for greater reliability, safety, and efficiency. This article explores how, by deploying AI smart cameras, further performance improvements are possible since the data used to empower AI machine vision comes from the camera itself.

Read more at Electronics Media

How Materialise Research Makes Multi-Laser 3D Printers Accessible with Future-Proof Software

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✍️ Author: Madeleine Fiello

πŸ”– Topics: additive manufacturing, 3D printing

🏭 Vertical: Machinery

🏒 Organizations: Materialise

A major goal for many in the 3D printing industry is boosting productivity to ultimately scale operations. Materialise’s software research team predicts that multi-laser machines will be key in enabling 3D printing factories to accomplish this goal.

In this blog, we’ll dive into this topic with Tom Craeghs, Research Manager within our Central Research & Technology department. Read on to discover the advantages and challenges of multi-laser machines, as well as how advancements in software will enable these printers and their associated productivity to become a reality.

Read more at Materialise Blog

Circular Economy 3D Printing: Opportunities to Improve Sustainability in AM

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✍️ Author: Hayley Everett

πŸ”– Topics: additive manufacturing, 3D printing, sustainability

🏭 Vertical: Machinery, Automotive

🏒 Organizations: Ford, Renault, Reflow, Recreus, HP, Materiom

Within the 3D printing sector alone, there are various initiatives currently underway to develop closed-loop manufacturing processes that reuse and repurpose waste materials. Within the automotive sector, Groupe Renault is creating a facility entirely dedicated to sustainable automotive production through recycling and retrofitting vehicles using 3D printing, while Ford and HP have teamed up to recycle 3D printing waste into end-use automotive parts.

One notable project that is addressing circular economy 3D printing is BARBARA (Biopolymers with Advanced functionalities foR Building and Automotive parts processed through Additive Manufacturing), a Horizon 2020 project that brought together 11 partners from across Europe to produce bio-based materials from food waste suitable for 3D printing prototypes in the automotive and construction sectors.

Read more at 3D Printing Industry

Augmented reality becomes actual reality

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✍️ Author: Golsa Fouladinejad

πŸ”– Topics: augmented reality, worker safety

🏒 Organizations: Schneider Electric

When applied to electrical power distribution across a wide range of businesses and industries, AR has the potential to greatly increase power availability, electrical safety, and efficiency. Here’s why:

  • Availability: AR helps organizations optimize operations and maximize continuity for better productivity and profitability
  • Safety: AR helps to reduce the risk of occupational injuries and fatalities
  • Efficiency: AR help reduces the total cost of ownership by offering more accessible and effective training

Read more at Schneider Electric Blog

Toward Generalized Sim-to-Real Transfer for Robot Learning

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✍️ Authors: Daniel Ho, Kanishka Rao

πŸ”– Topics: reinforcement learning, AI, robotics, imitation learning, generative adversarial networks

🏒 Organizations: Google

A limitation for their use in sim-to-real transfer, however, is that because GANs translate images at the pixel-level, multi-pixel features or structures that are necessary for robot task learning may be arbitrarily modified or even removed.

To address the above limitation, and in collaboration with the Everyday Robot Project at X, we introduce two works, RL-CycleGAN and RetinaGAN, that train GANs with robot-specific consistencies β€” so that they do not arbitrarily modify visual features that are specifically necessary for robot task learning β€” and thus bridge the visual discrepancy between sim and real.

Read more at Google AI Blog

Surge Demand

There’s a manufacturing boom in America’s southwest. Carnegie Melon unveils some fascinating research on active search robotics.