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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: Scaling Hardware Without the Headaches: The Critical Need for Automated Procurement Technology Hardware manufacturing teams face unprecedented challenges in scaling procurement operations while maintaining speed, compliance, and cost efficiency. Electronic components represent 70% of the bill of materials in mission-critical industries, yet traditional procurement systems rely on manual processes that create bottlenecks and force engineering teams to spend valuable time on logistics rather than core product development. The hardware industry urgently needs technological solutions that transform procurement from a laborious, error-prone process into automated workflows that scale efficiently without proportional increases in headcount. Modern procurement platforms must integrate advanced technologies to address the unique complexities of electronic component sourcing. Critical Technology Requirements: AI-Powered Process Automation - Advanced systems that can automatically parse unstructured data from supplier communications, emails, and attachments, converting them into structured data for ERP integration. Machine learning algorithms should track complete bills of materials across hundreds of suppliers, providing predictive insights and automated exception reporting to prevent supply disruptions. Intelligent Supplier Network Management - Technology platforms that leverage large databases of pre-vetted suppliers, using automated matching algorithms to connect procurement teams with suppliers based on technical specifications extracted from 2D/3D drawings and process documentation. This eliminates the time-consuming manual vetting process that often takes weeks. Real-Time Supply Chain Visibility - Integrated systems that provide instant access to inventory levels, bill of materials analysis, and global supply intelligence, with predictive algorithms that alert teams to compliance issues, supply disruptions, and component lifecycle changes weeks before they impact production. Research indicates that organizations implementing automated procurement technologies achieve 20% efficiency improvements while maintaining compliance with strict industry regulations. The future of hardware manufacturing depends on developing and adopting these technological solutions to eliminate traditional procurement bottlenecks and enable innovation in aerospace, defense, robotics, and other critical industries.

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

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WEST Session: Effective data collection is critical for optimizing production lines, yet traditional methods such as manual recording and PLC-coded data collection are fraught with inefficiencies and inaccuracies. Manual data entry often misses short downtime events and is subject to operator bias, while PLC-based systems suffer from inconsistencies, excessive costs, and revalidation challenges. The future of data collection lies in automation, modular modeling, and intelligent data processing, providing a foundation for digital transformation and sustainable manufacturing excellence. This session will explore the following concepts: · Advanced data collection goes beyond monitoring bottleneck operations, incorporating machine-level insights across all assets. · A multi-layered approach – integrating real-time signal processing, logic engines, and high-speed data acquisition – enhances fidelity, reduces integration costs, and improves root cause analysis. · Additionally, Aa Fault Learning approach dynamically identifies and ranks faults, leading to better diagnostics and predictive maintenance. · By leveraging digital twins, synchronizing multiple data streams, and enabling fast data validation, companies can significantly improve operational efficiency. · A robust data collection strategy supports MES, OEE, and AI/ML applications, ensuring accurate modeling, predictive analytics, and enterprise-wide standardization.

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.

WEST Session: If you’re in the manufacturing field, then you must be passionate about how things are made. Manufacturing matters, and it’s crucial that leaders step out of their buildings and focus on the business, rather than just working in the business. Most small to mid-sized manufacturers don’t realize there is much power in utilizing peer networks, communities, regions, and states to look from the outside in. We want to leave our businesses in a better condition than when we arrived. We want more youth in manufacturing, we want better data about our manufacturing operations, and we want to understand how to incorporate the newest technology into our operations. Fourteen years in the family’s manufacturing business has allowed me to inform myself and bring solutions to our business and remain competitive. Additionally, amid exploring opportunities, I have discovered the importance of the critical need to bridge the skills gap and the tacit knowledge that is being lost as our skilled workforce retires. The impact that harnessing and leveraging this knowledge will help alleviate the existential crisis that threatens us all.

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WEST 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.