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Highlights from MT Series WEST 2025.

Images from MT Series WEST 2025.

WEST Session: Sponsored by: UiPath Moderated by: Paul Boris This panel brings together different perspectives, from a small, family-owned manufacturing company to one of the largest digital solutions providers, to discuss the deployment of advanced technologies and how the landscape continues to change rapidly.  Using AI to improve efficiency, automate tasks and analyze data allows manufacturers enhanced decision making and is even transforming entire industries.  AI is disrupting traditional thinking at an accelerated pace.  This panel will cover organizational factors/business considerations, technical challenges, skills/labor gaps, ethical considerations and other.

WEST Session: Brought to you by SME

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: Attendees can expect to hear an in depth description of the machine equipment that Kintek specially makes for turning and milling machines. They will understand that Kintek/ All Industrial Services CNC can perform service and installation as well. We want to help people realize that Kintek is there to simplify your needs, helping you from the very beginning of the problem until we solve it.

WEST Session:

WEST Session: The R&D Tax Credit is a powerful federal incentive that rewards manufacturers for innovation in product design, process improvements, and new technologies. It directly reduces tax liability or payroll tax, freeing up cash to reinvest in equipment, workforce, and growth. Manufacturers often face challenges such as rising production costs, global competition, supply chain constraints, and the need to modernize with automation, robotics, and sustainable practices. The R&D Tax Credit helps offset these pressures by turning day-to-day problem-solving—like improving tooling, enhancing production efficiency, or developing prototypes—into measurable tax savings. To qualify, activities must pass the IRS “Four-Part Test”: seeking to resolve technical uncertainty, relying on science/engineering, involving experimentation, and aiming to improve a product or process. Eligible expenses include wages, materials consumed in development, and contractor costs. Two paths provide benefits: the Standard Credit , which reduces income taxes, and the Payroll Credit , which offsets up to $500,000 annually in employer payroll taxes—especially valuable for manufacturers reinvesting in growth. The One Big Beautiful Bill Act (2025) restored immediate expensing of domestic R&D costs, eliminating the burdensome 5-year amortization. It also allows companies that capitalized expenses since 2022 to retroactively accelerate deductions. Strategies for manufacturers include building stronger documentation systems, aligning R&D tracking with engineering workflows, and leveraging tax planning to maximize credits year after year. Together, these updates give manufacturers powerful tools to manage costs, stay competitive, and invest confidently in new technologies.

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.