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SOUTHEAST Session:

SOUTHEAST Session:

SOUTHEAST Session:

SOUTHEAST Session: Digital twins are rapidly becoming a cornerstone of advanced manufacturing, enabling companies to simulate, optimize, and validate their production processes in a virtual environment before committing to physical execution. This presentation explores the value of digital twins specifically in the domains of CNC machining, robotic automation, and the broader virtual factory. In CNC machining, digital twins replicate the behavior of machines, tools, and part geometries, allowing for precise simulation of toolpaths and real-time detection of potential collisions, over-travel, and inefficiencies. By simulating the exact machine kinematics, spindle dynamics, and tool libraries, manufacturers can reduce setup times, improve part quality, and significantly lower the risk of costly rework or downtime. In robotic work cells, digital twins mirror robotic behavior, motion, and task sequences. This enables manufacturers to program, test, and optimize robot trajectories and tool interactions virtually - ensuring safety, cycle time optimization, and maximum utilization of expensive automation assets. Collision detection, reach analysis, and process synchronization can all be handled digitally before deployment on the shop floor. At the virtual factory level, digital twins provide a holistic view of the entire manufacturing environment - integrating machines, robotics, material flow, operators, and logistics into a unified simulation. This enables strategic decision-making, accurate capacity planning, and the ability to test process changes in a risk-free virtual environment. The result is greater agility, resilience, and efficiency across the entire production lifecycle. Attendees will gain insight into how digital twins reduce risk, increase productivity, and enable smarter planning across manufacturing operations. By harnessing digital twins in CNC machining, robotic systems, and factory-wide simulations, companies can accelerate their journey toward digital transformation and fully realize the promise of Industry 4.0.

SOUTHEAST Session: In today's highly competitive manufacturing landscape, operational efficiency is more critical than ever. Yet, excessive energy consumption and unplanned downtime remain major challenges, significantly impacting productivity and costs. Traditional maintenance strategies often fail to address the root causes of inefficiencies, leading to unnecessary energy waste and unexpected failures. This session explores Energy-Centered Maintenance (ECM)—a data-driven, AI-powered approach that goes beyond conventional reliability-centered maintenance by integrating energy efficiency as a key decision-making factor. By leveraging advanced IoT sensors, AI-driven analytics, and real-time machine health monitoring, manufacturers can proactively detect faults, minimize energy loss, and extend asset life. Through real-world case studies and industry insights, attendees will learn how ECM enables manufacturers to reduce operational expenses, prevent unplanned downtime, and achieve sustainability goals—all without compromising productivity. The session will also highlight how machine learning and AI-driven predictive analytics help manufacturers make smarter maintenance decisions, optimizing energy use while ensuring equipment reliability. Whether you're looking to cut energy costs, enhance machine uptime, or align with Industry 4.0 and sustainability initiatives, this session will provide practical takeaways to help you transform your maintenance strategy. Learning Objectives Understand the limitations of traditional maintenance strategies and how excessive energy waste and unexpected downtime impact manufacturing costs and efficiency. Explore the principles of Energy-Centered Maintenance (ECM) and how AI-driven predictive analytics can optimize machine performance, reduce energy waste, and prevent costly breakdowns.

SOUTHEAST Session: Manufacturers performing visual inspections on their products have historically had two options: perform the inspection manually, or program a rule-based vision system. Both of these options are slow, inflexible, and costly to maintain. However, as Graphical Processing Units (GPUs) have become more powerful, a third approach has become available: deep learning. Deep learning-based vision systems learn directly from annotated images to perform inference on images. They offer numerous advantages including rapid deployment, greater detection flexibility, and lower development and acquisition costs. They are also easily retrained and reconfigured with minimal to no programming.  As such, they represent a massive opportunity to automate quality inspection for small to medium sized manufacturers. In this presentation, we will discuss the technology that enables AI-based computer vision, the advantages they provide, use cases, and how to best implement them. Attendees will walk away with a clear understanding of where deep learning fits into their operation and how to get started with the right tools and strategy.

Speaker at SOUTHEAST: Andy Henderson, President & Chief Executive Officer, Hendtech LLC

Speaker at SOUTHEAST: Ronald Graves, President, Poiema Corporation

Speaker at SOUTHEAST: Will Healy III, Global Industry Manager - Fabricated Metals, Teradyne Robots / UR

Speaker at SOUTHEAST: Sarah DePeri, South Carolina APEX Manager, South Carolina APEX Accelerator