More than just a printer, Functgraph can manipulate a 3D printer head to make sandwiches, fold items, build toys, and more:
Improving advanced manufacturing practices through AI's Bayesian network
With experience, we learn awareness of events and conditions in our plant environment. As our experience matures, we learn the possibility of a given set of events and conditions resulting in certain outcomes. Computational models can perform the same service by capturing events and conditions, then calculating the probability of certain consequences. If the probability of an anticipated outcome is unacceptable, our computers can inform us of a condition needing attention or address the situation themselves. This, along with collecting meaningful volumes of relevant data, is the core of AI.
One mathematical model employed in AI is the Bayesian network (BN), which is a graph that defines the relationships between conditions or events and their possible consequences. The conditions or events are random variables that are identified on a BN as a node.
Transforming quality and warranty through advanced analytics
For companies seeking to improve financial performance and customer satisfaction, the quickest route to success is often a product-quality transformation that focuses on reducing warranty costs. Quality problems can be found across all industries, and even the best companies can have weak spots in their quality systems. These problems can lead to accidents, failures, or product recalls that harm the company’s reputation. They also create the need for prevention measures that increase the total cost of quality. The ultimate outcomes are often poor customer satisfaction that decreases top-line growth, and additional costs that damage bottom-line profitability.
To transform quality and warranty, leading industrial companies are combining traditional tools with the latest in artificial-intelligence (AI) and machine-learning (ML) techniques. The combined approach allows these manufacturers to reduce the total cost of quality, ensure that their products perform, and improve customer expectations. The impact of a well-designed and rigorously executed transformation thus extends beyond cost reduction to include higher profits and revenues as well.
How Honeywell's CEO is turning the legacy manufacturer into a SaaS player
Cumulatively, it marked a significant step forward in Adamczyk’s vision to turn Honeywell from a legacy industrial manufacturer into a top software provider for sectors like real estate, life sciences and aviation.
“The one common fiber across all our businesses is we are a controls company,” he told Protocol at an event on Tuesday. “When you’re a controls company, you’re connected to everything, you’re connected to all the systems in that building, in that aircraft. We use that data to drive controls, but we could use that data to drive energy savings, to drive efficiency.”
Using tactile-based reinforcement learning for insertion tasks
A paper entitled “Tactile-RL for Insertion: Generalization to Objects of Unknown Geometry” was submitted by MERL and MIT researchers to the IEEE International Conference on Robotics and Automation (ICRA) in which reinforcement learning was used to enable a robot arm equipped with a parallel jaw gripper having tactile sensing arrays on both fingers to insert differently shaped novel objects into a corresponding hole with an overall average success rate of 85% with 3-4 tries.
Total Cost of Ownership Guide: No-Code App Platforms vs Traditional MES
You’ve found a no-code, IIoT native application platform that can replace your MES partially or fully. You are excited about augmenting human workflows, flexible deployments, and continuous improvements — but you have to do your due diligence and prove ROI.
We get it! No-Code App Platforms are new to the Industrial and Manufacturing technology landscape. Even though they were developed for a different era, Manufacturing Execution Systems (MES) are a tried and tested means of coordinating, executing, and tracking manufacturing processes.
Integrated intelligent technologies optimize yield and increase profits for rice millers
The digitally connected technology provides mill operators with the insights they need to correctly adjust solution settings. Over time, the intelligent system is capable of adjusting autonomously. Where millers were once left to take corrective action after an incident occurred, they can now prevent costly reprocessing steps and proactively manage the entire process. With these advances, the miller can optimize operating costs, quality and yield, all of which have a direct impact on the profit of the mill.
What Walmart learned from its machine learning deployment
As more businesses turn to automation to realize business value, retail’s wide variety of ML use cases can provide insights into how to overcome challenges associated with the technology. The goal should be trying to solve a problem by using ML as a tool to get there, Kamdar said.
For example, Walmart uses a ML model to optimize the timing and pricing of markdowns, and to examine real estate data to find places to cut costs, according to executives on an earnings call in February.
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