The Advanced Casting Research Center (ACRC) at the University of California, Irvine (UCI) is currently working on several digital-manufacturing projects. In this piece, we feature two material manufacturing research projects with far-reaching impact.
The first study aims to develop design guidelines that can be utilized to build conformally cooled tooling with a longer lifetime and improved thermal-management properties to reduce cycle time and production costs. The second study is about leveraging the massive collection of product data and to transform data into information and information into knowledge.
ACRC is one of the largest industry-university centers in North America dedicated to collaborative research in metal processing and manufacturing. Its focus is metal casting and digital manufacturing. It was first established in the mid-1980s, and it has a long-standing track record for carrying out fundamental research that is impactful and of utility to the industrial sector. It is located within the Samueli School of Engineering at UCI.
Rapid Creation of Tooling with Conformal Cooling
The geometric complexity and material requirements of tooling for high-pressure die casting means a significant part of the cost of each casting is the capital investment in tooling and machinery. It is for this reason that ACRC is focusing on ways to minimize the cycle time and maximize the lifetime of tooling through additive-manufacturing (AM) applications to help industry be more competitive
Additive manufacturing enables unprecedented design freedom for placement and geometry of cooling channels within the tool. This freedom offers heat-extraction advantages that lead to reduced cycle times and a reduction in wear due to die soldering. While the placement of cooling channels in relationship to the cavity surface can greatly increase heat extraction, however, it also increases the overall thermomechanical stress of the tool. These cyclic stresses can significantly decrease the tool’s lifetime. Thus, it is important to understand the variables that contribute to the thermomechanical stress.
Thermomechanical stress within the tool is generated when it is subject to temperature gradients caused by the solidifying casting. The stress fields are generated within the tool when parts of it thermally expand and concentrate around cooling channels. The cyclic nature of high-pressure die casting can cause cracks to propagate from the cooling channels to the cavity walls, resulting in premature failure. Factors such as coolant fluid flow and distance from cavity surface to cooling-channel wall can and will influence the magnitude of stress that is evolved within the conformally cooled tool.
It is also important to consider the directionality of stress when investigating the root cause of the stress. The maximum tensional stress tends to be located at the surface of the cooling channel, while the maximum compressive stresses tend to be at the cavity surface. Tensional stress in combination with the as-manufactured surface roughness of the cooling channel could cause rapid crack formation and propagation. If the compressive stress on the cavity surface is strong enough to induce plastic deformation, then a crack could open up as the tool cools down.
This research is sponsored by the Defense Logistics Agency – DLA Troop Support in Philadelphia, Pa., and the Defense Logistics Agency Information Operations, J68, Research & Development in Fort Belvoir, Va.
Data Science in Materials Manufacturing
ACRC is also working on research to help simplify the massive collection of product data. These data could be in the form of insight into the effects of process parameters, component designs, environmental issues, materials composition, etc.
Since materials-manufacturing operations generate extensive amounts of data with enough high-dimensional samples, it makes analysis by traditional methods challenging. In the same way that the Internet and service providers gather data about their customers, materials processors gather data about their products. However, it is less common for manufacturing operations to apply data science to knowledge creation and to make product predictions.
As materials-processing companies bring their data to the data-science community to find answers, new insight into how the data is traditionally collected and the challenges that are created come to light. Some examples include:
- A culture of departmental data keeping
- Collection of many input data and few outputs
- Missing input data (heterogeneous data)
- An imbalance in output data class where high-quality samples far outweigh unacceptable samples
ACRC’s research takes aim at these data challenges through explorative case studies, innovative machine learning and deep-learning algorithms using real-world manufacturing data supplied by ACRC-member companies. The goal is to educate the industry on how to best organize data, assess the performance of algorithms, identify useful data science tools and recommend new data sources. Two recently published papers give further details of this work. These are:
Sun, N., Kopper, A., Karkare, R., Paffenroth, R.C. and Apelian, D., “Machine Learning Pathway for Harnessing Knowledge and Data in Material Processing,” International Journal of Metalcasting, pp.1-13, 2020
Kopper, A., Karkare, R., Paffenroth, R.C. and Apelian, D., “Model Selection and Evaluation for Machine Learning: Deep Learning in Materials Processing,” Integrating Materials and Manufacturing Innovation, 9(3), pp.287-300, 2020
To learn more about the exciting work that ACRC is doing on conformal cooling and data management in materials science, please visit https://acrc.manufacturing.uci.edu/. For additional information, please contact Prof. Diran Apelian at UCI by e-mail at firstname.lastname@example.org.