Displaying 291-300 of 668 results for

Speaker at WEST: Joe Mattekatt, CEO, Mesoware Inc.

WEST Session: Robotics is advancing at a remarkable pace. Smarter AI models and improved hardware have made robots more capable and accessible than ever before. These shifts are transforming how we can make use of automation in fundamental ways: 1. Expand use cases through adaptability. Flexible robotic deployments open up opportunities to capture value from a wider range of applications. Adaptive automation becomes a tool for navigating uncertainty rather than a rigid asset. 2. Deploy faster with simplified programming. Your team no longer needs to spend days learning how to program the robot. Instruct robots in plain English instead. 3. Scale through modularity. Ecosystems designed around modular components allow deployments to grow and evolve with your operations. The cost of integration falls as automation spreads across your workflows. 4. Empower your workforce. Automation learns and scales the manufacturing knowledge of your company, enabling your team to focus on higher-value, higher-impact work. Join us to explore how these changes are reshaping both the economics and the role of automation, and what they mean for the future of your operations.

Speaker at WEST: James Meyette, Senior Application Engineer, Selway Machine Tool Co.

WEST Session: Moderated by: Graham Hargreaves, CAD/CAM Consulting Services “You just have to finesse it...” “...finagle it” “...jockey it around a little.” These are highly technical terms to describe how engineers, programmers, and machinists make the software and machines at hand do something a little avant-garde to make a workpiece as spec’ed. For machine shop owners, pressure is growing to deliver increasingly complex, never-before-seen parts—and so are the challenges. From tight timelines to tighter tolerances, the path from design to finished part is rarely straightforward. But there is a path, and it involves creating a collaborative environment where engineers, programmers and machinists engage in open communication to problem solve on the fly. This panel will address the key challenges manufacturers face today, including: · Handling first-time parts with no proven toolpaths or machining history · Working around software limitations when standard CAM strategies fall short · Bridging the gap between engineering, programming, and machining to avoid costly miscommunication · Maximizing existing machine capabilities without compromising part quality · Collaborating under pressure to solve problems in real time on the shop floor Panelists will share real-world examples and proven strategies for overcoming these obstacles through smarter programming, tighter collaboration, and creative problem-solving. Whether you're running a small job shop or managing a larger operation, this session will offer practical insights to help your team work more efficiently, reduce rework, and stay competitive in a fast-changing manufacturing landscape. This conversation will bring this reality to light and attempt to lift up the entire industry, and encourage everyone to never stop learning, tinkering, and tweaking.

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

WEST Session:

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.