Displaying 91-100 of 187 results for

WEST Session: As manufacturers strive to improve machine utilization, reduce downtime, and address workforce shortages, robotic CNC machine tending has become a critical automation strategy. Advances in robotic platforms and machine-tending technologies now enable shops of all sizes to increase throughput while maintaining consistent quality in competitive markets. In this session, Verx Corporation—a FANUC Robotics Authorized System Integrator and full-service distributor for VersaBuilt —will introduce the fundamentals of robotic CNC machine tending. Attendees will gain a framework for understanding how system components—robots, grippers, workholding, and operator interfaces—work together in collaborative applications. Following this introduction, VersaBuilt will highlight key challenges and solutions specific to CNC machine tending. Topics will include gripping strategies for varied parts and high-mix applications, simplified integration methods that reduce operator complexity while improving reliability, and how VersaBuilt’s MultiGrip, DuoGrip, and Zero Point (ZPS) Automation Systems leverage FANUC CRX robots to deliver scalable automation to a broader range of manufacturers.   The session will conclude with a live Q&A, giving participants the opportunity to connect these insights with their own manufacturing challenges. Attendees will leave with both a strong conceptual foundation and practical examples for addressing common barriers in robotic machine tending.

Speaker at WEST: Guillermo Peregrino, Manufacturing Engineering Manager, Aero Bending Company

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WEST Session: This presentation challenges the prevailing narrative that manufacturing must conform to AI, proposing instead that AI must evolve to meet manufacturing's unique demands. After over a decade of attempting to transplant cloud-designed AI models into manufacturing environments, it has become clear that this approach is not practical. Rather than continuing to quotes percentages of failures, we advocate for a fundamental shift in perspective. If AI's core strength lies in pattern recognition and learning, why not leverage this capability to make AI itself more adaptable to manufacturing contexts? This talk demonstrates how AI can be redesigned to thrive in manufacturing environments through concrete examples that accelerate the development of robust, continuously learning models. We examine three critical assumptions that, when reconsidered, significantly enhance AI adoption and scalability in manufacturing. First, we start with quantifying success. Time invested in understanding and quantifying the trade-offs that matter to a production line is invariably worthwhile. Consider quality control as an example: should you prioritize developing a model that catches every defect, or one that minimizes false positives by avoiding the misclassification of good products as defective? Like human decision-making, AI systems will inevitably make errors—the key is to design systems that account for and manage these errors rather than pretending they won't occur. Second, we tackle data strategy. Manufacturing data represents valuable intellectual property that demands strategic handling. Contrary to popular belief, more data doesn't always yield better results. Our experience shows that indiscriminate data usage often produces sluggish, costly models that are challenging to troubleshoot and maintain. Hence, data selection strategies play a crucial role in the long-term success of a solution. Finally, we emphasize AI's inherently non-deterministic nature. Treating AI as a deterministic tool fundamentally limits its adaptive potential. Instead of rebuilding AI systems with every product change, we propose designing solutions that inherently evolve with environmental shifts—both incremental and substantial. This approach positions AI as a dynamic partner in manufacturing, capable of continuous learning and adaptation rather than a rigid tool requiring costly reconfiguration.

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WEST Session: In this presentation, we’ll explore how GibbsCAM empowers modern machine shops to overcome complex manufacturing challenges through advanced, yet intuitive, CAM technology. We’ll walk through real-world part examples that demonstrate how GibbsCAM streamlines programming for Milling, Turning, and Multi-Task Machines. Attendees will learn how to reduce cycle times, improve toolpath quality, and eliminate redundant operations using intelligent automation, toolpath optimization, and post processor customization. We’ll highlight strategies like adaptive roughing, simultaneous machining, and sync management for multi-channel machines—all designed to help manufacturers maximize spindle uptime and shorten setup times. We'll also showcase how GibbsCAM’s associative modeling, geometry creation tools, and integrated simulation reduce scrap and improve confidence before the part hits the machine. This session will provide actionable insights to improve programming workflow. By combining powerful functionality with a user-friendly interface, GibbsCAM gives you the control and flexibility needed to stay competitive in today’s fast-paced manufacturing world. Join us to see how GibbsCAM can help you do more with your machines.

WEST Session: This presentation challenges the prevailing narrative that manufacturing must conform to AI, proposing instead that AI must evolve to meet manufacturing's unique demands. After over a decade of attempting to transplant cloud-designed AI models into manufacturing environments, it has become clear that this approach is not practical. Rather than continuing to quotes percentages of failures, we advocate for a fundamental shift in perspective. If AI's core strength lies in pattern recognition and learning, why not leverage this capability to make AI itself more adaptable to manufacturing contexts? This talk demonstrates how AI can be redesigned to thrive in manufacturing environments through concrete examples that accelerate the development of robust, continuously learning models. We examine three critical assumptions that, when reconsidered, significantly enhance AI adoption and scalability in manufacturing. First, we start with quantifying success. Time invested in understanding and quantifying the trade-offs that matter to a production line is invariably worthwhile. Consider quality control as an example: should you prioritize developing a model that catches every defect, or one that minimizes false positives by avoiding the misclassification of good products as defective? Like human decision-making, AI systems will inevitably make errors—the key is to design systems that account for and manage these errors rather than pretending they won't occur. Second, we tackle data strategy. Manufacturing data represents valuable intellectual property that demands strategic handling. Contrary to popular belief, more data doesn't always yield better results. Our experience shows that indiscriminate data usage often produces sluggish, costly models that are challenging to troubleshoot and maintain. Hence, data selection strategies play a crucial role in the long-term success of a solution. Finally, we emphasize AI's inherently non-deterministic nature. Treating AI as a deterministic tool fundamentally limits its adaptive potential. Instead of rebuilding AI systems with every product change, we propose designing solutions that inherently evolve with environmental shifts—both incremental and substantial. This approach positions AI as a dynamic partner in manufacturing, capable of continuous learning and adaptation rather than a rigid tool requiring costly reconfiguration.

Speaker at WEST: Rob Sims, Founder & CTO, Alchemi Data Management, Inc.

Speaker at WEST: Matthew Dainko, Director of Business Development, Complete

WEST Session: