Today, designers and engineers are accustomed to working with digital tools in their day-to-day jobs. Yet, over the last decade, these tools have evolved and unlocked new capabilities and productivity gains, enabling part and mold designs to be more complex and data-driven.

However, a central challenge in manufacturing lies in the scattered nature of data that exists across the product lifecycle. From design and moldmaking to manufacturing and quality control, valuable data is generated in silos, hindering seamless collaboration. the concept of the digital thread has emerged as a solution, envisioning an interconnected flow of data from the inception of an idea to the final product.

This digital continuum offers a centralized source of truth, facilitating easier tracking of changes, interconnecting production data with design and simulation, and unlocking the potential for creating digital twins and machine learning models.

The advantage of establishing a digital thread is evident in its ability to pave the way for digital twins and machine learning models. This shift from deterministic to probabilistic models opens avenues for preemptive defect detection and enhanced process simulation.

Design Libraries and Conformal Cooling

To bridge the gap between the physical and digital realms, manufacturing must adopt a software-driven approach that optimizes workflows, according to Dr. Wood. He shares two practical examples that illustrate this transformative journey: digital design libraries for enhanced productivity and additive manufacturing (AM) for improved performance.

Digital Design Libraries

Designing molds for manufacturing is a complex task that demands precision and efficiency. Digital design libraries offer a solution by introducing parametric design, enabling quick selection and placement of standardized components. This approach reduces the complexity of digital information storage by representing components through rules rather than individual instances.

This intentional shift toward a software-driven workflow has proven impactful, By gradually expanding the coverage of design libraries, teams experience faster design times, smoother onboarding of new designers and a significant reduction in design-related issues. The result is a controlled design environment that enhances overall productivity.

Cooling Action

Additive manufacturing (AM), or 3D printing, enables intricate and complex structures that conform more closely to the part’s shape, offering a promising solution to the challenges posed by traditional methods to speed up the cooling process.

The four main steps to the conformal cooling process are (1) component design and integration, (2) mold flow simulation and virtual testing, (3) digital integration and feedback loops, and (4) finishing and assembly. Each step requires attention to detail and expertise to ensure that the components meet the stringent requirements of production environments where millions of cycles may be involved.

Let’s take a mold with a small cavity and core, for example. It presents challenges, including long, slender parts, thin walls, tight tolerances and the need for high volume and throughput. The goal is to find a solution that instills confidence in the customer to run it in production without frequent maintenance interruptions.

Redesign for efficiency. To begin, they conducted a mold flow simulation that revealed hot spots on the tips of the parts, indicating a need for efficient heat extraction. To address this, we employed conformal cooling to incorporate more inlets and outlets constructed differently to enhance cooling efficiency — notably, cooling inserts on the front end to improve the interior cooling of the parts. The impact of this design change was substantial, particularly in reducing mold open time. The company cut the cycle time almost in half.

This not only accelerates injection into the part but also minimizes the time the operator needs to keep the mold open for proper solidification. Maximum temperature was also reduced from 193°F down to 85°F, illustrating the enhanced efficiency of the system with the added water.

It’s crucial to note that we’re not merely introducing water cooling; we’re refining the shape and width of the cooling lines to maximize their proximity to the part.

Identify the manufacturing process. After the digital design phase, build preparation involved surface offsets, part orientation and support addition in a CAM environment. we used a laser powder bed fusion system and carefully selected process parameters. Post-processing, including media blasting and EDM, were implemented to ensure the part met specifications.

Ensure precision in finishing. The finishing and assembly step is particularly vital, as the tolerances from metal 3D printing may not be suitable for direct use in a production mold. our approach involved post-machining on a CNC to achieve the required tolerances, ensuring that the conformal cooled mold met the customer’s production needs.

Tailor your approach. It is important to note that conformal cooling is not a one-size-fits-all solution. Productivity improvements can vary based on the geometry of the molded part.

Looking Ahead

Machine learning continue to evolve as a powerful tool for leveraging the vast amounts of data generated in mold manufacturing. It can help design engineers in early-stage decision-making identify statistical anomalies in manufacturing and inform design based on machine accuracy, such as defect prediction, accurate design for manufacturing and feature-based accuracy assessment.

The digital thread serves as the foundation for creating a data lake, enabling machine learning algorithms to identify correlations and streamline decision-making processes.

While the integration of machine learning in manufacturing might seem futuristic, Dr. Wood notes that many companies are already putting these models to use in a variety of ways, ranging from machine health monitoring to on-demand quoting engines. He believes that over time, we will find more use cases and capabilities of machine learning models that will unlock new levels of access and productivity for everyone.  For example, there are on-demand manufacturing platforms that use custom manufacturing execution system (MES) software to create a data lake that serves as the foundation for machine learning models.

The challenge lies in establishing robust data infrastructure across the industry, enabling accurate leveraging of machine learning capabilities. Dr. Wood emphasizes that the path forward for mold builders must involve a deliberate and phased approach, starting with critical components and gradually expanding their data models. Last but not least, the workforce involved in this transformation must possess not only software development skills but also a deep understanding of manufacturing logic.