Real-Time Data Analysis For Smart Manufacturing

Edge computing is making a serious comeback in manufacturing—and it’s not just hype. We’ve seen the growing challenges around cloud computing, like unpredictable costs, latency, and lack of control. Edge computing is stepping in to change the game by bringing processing power on-site, right where the data is generated. (I know, I know - this is far from a new concept). Here’s why it matters: ⚡ Real-time data processing: critical for industries relying on AI-driven automation. 🔒 Data sovereignty: keep sensitive production data close, rather than sending it off to the cloud. 💸 Cost control: no unpredictable cloud bills. With edge computing, costs are often fixed and stable, making budgeting and planning significantly easier. But the real magic happens in specific scenarios: 📸 Machine vision at the edge: in manufacturing, real-time defect detection powered by AI means faster quality control, without the lag from cloud processing. 🤖 AI-driven closed-loop automation: think real-time adjustments to machinery, optimizing production lines on the fly based on instant feedback. With edge computing, these systems can self-regulate in real time, significantly reducing downtime and human error. 🏭 Industrial IoT (and the new AI + IoT / AIoT): where sensors, machines, and equipment generate massive amounts of data, edge computing enables instant analysis and decision-making, avoiding delays caused by sending all that data to a distant server. AI is being utilized at the edge (on-premise) to process data locally, allowing for real-time decision-making without reliance on external cloud services. This is essential in applications like machine vision, predictive maintenance, and autonomous systems, where latency must be minimized. In contrast, online providers like OpenAI offer cloud-based AI models that process vast amounts of data in centralized locations, ideal for applications requiring massive computational power, like large-scale language models or AI research. The key difference lies in speed and data control: edge computing enables immediate, localized processing, while cloud AI handles large-scale, remote tasks. #EdgeComputing #Manufacturing #AI #Automation #MachineVision #DataSovereignty #DigitalTransformation

Meet anyone in manufacturing, and for their top two concerns, you'll hear about:   1. Supply Chain Disruptions: Challenges related to inventory and supply chain management. 2. Operating Costs: Navigating economic headwinds and operational inefficiency.   Our clients in the manufacturing sector work in a fast-paced world where maintaining operational efficiency is crucial. One of our clients faced significant challenges with their Clean-In-Place (CIP) process, which directly impacted their quality check procedures. Frequent unplanned downtimes due to equipment failures were hampering productivity and throughput, highlighting the need for a more proactive maintenance approach. They needed real-time insights to make informed preventive maintenance decisions! To address their challenges, our team developed and implemented an AI-based predictive maintenance solution for the CIP equipment. Leveraging data analytics and machine learning, this solution integrated critical datasets from batch processes, sensors, and maintenance records.   By empowering our client with real-time insights through anomaly detection and a risk scoring system, we enabled them to make informed preventive maintenance decisions. This proactive approach not only improved their operational efficiency but also set a new standard for maintenance practices in the manufacturing industry.   Our client went from reactive and corrective maintenance to predictive maintenance! Would love to hear from the network on what you are seeing in this area. If you have a story, let us talk.

Excited to share our latest publication on real-time decision-making for Digital Twins in additive manufacturing, powered by data-driven and physics-based Model Predictive Control (MPC) and a time-series deep neural network called TiDE. The method enables • One-shot multi-step-ahead prediction of melt pool temperature and depth • Proactive control of laser power for quality assurance • Constraint handling to minimize defects like porosity in Directed Energy Deposition (DED) This work showcases the capability of machine learning in modeling complex systems which facilitates real-time decision making, bringing us closer to intelligent, self-adaptive manufacturing. Shout out to all the co-authors @Yi-Ping Chen, Vispi Karkaria, Ying-Kuan (Rick) Tsai-Kuan Tsai, @Faith Rolark, @Daniel Quispe, @Robert X. Gao and Jian Cao, for the collaboration, and the funding support from NSF HAMMER ERC https://hammer.osu.edu/. Read the paper here: https://lnkd.in/gsczp74Z. #DigitalTwin #AdditiveManufacturing #MPC #DeepLearning #SmartManufacturing #AIControl