Integrating Adaptive Computing Directly into the ROS Ecosystem
Assembly Line
Capturing this week's trending industry 4.0 and emerging industrial technology media
Adaptive Computing in Robotics: Making the Intelligent Factory Possible
Demand for robotics is accelerating rapidly. According to the research firm, Statista, the global market for industrial robots, as an example, will more than double from US$81 billion in 2021, to over US$165 billion in 2028 (1). Today, you can find the technologies you need to build a robot that is safe and secure and can operate alongside humans. But getting these technologies working together can be a huge undertaking. Complicating matters is the addition of artificial intelligence which is making it more difficult to keep up with computational demands. In order to meet today’s rapid pace of innovation, roboticists are turning toward adaptive computing platforms. These offer lower latency and deterministic, multi-axis control with built-in safety and security on a modular platform that is scalable for the future.
Robot integration ease of use a priority
Leading robot manufacturers – ABB, Comau, Epson, Fanuc, Jaka, Kawasaki, Kuka, Nachi, Panasonic, Stäubli, TM Robot, Yamaha, Yaskawa – joined forces at the initiative of Siemens to develop a solution. Around 70 percent of the world’s robot manufacturers were on board. Now, the joint work has paid off. A uniform data interface between the PLC and the robot controllers has been defined to make robot programming uniform – and thus more efficient – for PLC programmers and PLC suppliers. Via this data interface, robot programs can be written completely in the PLC by calling the robot functions and reporting the required robot state information back to the PLC.
Inspection of Tapered Rollers for a Global Bearings Manufacturer
It was decided to use a Deep Learning AI powered inspection technique since the defects were qualitative and across a wide range of roller SKUs. The key steps followed in this workflow consisted of image collection, image annotation, Deep Learning model selection/training, deriving an optimized Edge inference model, deployment on the production floor and, finally, maintenance.
Qualitas worked collaboratively with the customer to collect and annotate a sufficient number of good (G) and not-good (NG) images of the tapered rollers, showing both the cylindrical and larger flat surfaces. A few hundred images were thus collected and processed. This image data was used to train the chosen Deep Learning AI model iteratively till acceptable performance was achieved. A key consideration was to keep false positive and false negative predictions sufficiently low across the wide variety of SKUs for a range of subjective surface defects.
An Effort Towards Reducing Industrial Textile Waste
Textiles include various types of materials made from natural and synthetic fibers. To ensure the finished products are defect-free, inspecting the fibers during the production process is important. This also can result in a 45% to 60% savings on the total expenditure due to wastage or recalling defective products.
Line scan cameras are widely used to detect defects in the textile industry. These use single pixel lines for the construction of continuous 2D images as the materials pass through the production line. The cameras can capture superior quality images of various types of materials, which help in detecting any pattern changes without any breaks. Additionally, these cameras can notify operators about any changes in color and texture.
Capital Expenditure
Tracking this week's major mergers, partnerships, and funding events in manufacturing and supply chain
Luminovo raises €11 million in seed funding to fix the broken electronics value chain with transformative software suite
For electronics manufacturers (EMS), Luminovo’s software brings together and reorganises the quoting process - including material and production costing - in one cloud-based tool, replacing the need for different on-premise methods. By bringing the whole workflow together and connecting customers and suppliers, Luminovo automates many repetitive, manual parts of the process and allows manufacturers to focus on the more complex steps that need collaboration.
Lumachain Raises US$19.5M in Series A Funding Led by Bessemer Venture Partners
Lumachain, an end-to-end solution for food supply chains, today announced it has raised US$19.5 million in Series A funding, led by Bessemer Venture Partners with participation from existing investor Main Sequence. The company has also announced its first U.S. headquarters in Denver.
This investment will allow the company to accelerate the roll out of its world-first computer vision-based artificial intelligence platform at meat and food processing plants across the U.S. and globally. In addition, Lumachain will significantly scale up its team of computer vision and software engineers, and delivery and product experts from the meat industry.