Displaying 131-140 of 292 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: 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.

WEST Session: The modern manufacturing shop floor demands seamless data exchange, real-time visibility, and operational efficiency to stay competitive. IMAGINiT FormsConnected optimizes these workflows by bridging the gap between design, production, and field operations. By digitizing paper-based forms and integrating them directly with Autodesk Fusion Manage and other enterprise systems, FormsConnected ensures accurate data capture, reduces manual errors, and accelerates decision-making. This enhanced connectivity empowers teams to track production status, manage change requests, and streamline quality checks in real time. As a result, manufacturers experience improved productivity, reduced downtime, and greater collaboration across departments, driving measurable gains in efficiency, profitability and overall growth.

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.

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: Matthew Dainko, Director of Business Development, Complete