An official website of the United States government
design, both inter- and intra-chiplet, impacts overall system performance as well as its manufacturing cost and thermal feasibility. This paper introduces a cross-layer methodology for designing networks in 2.5D systems. We optimize the network design and chiplet placement jointly across logical, physical, and circuit layers to achieve an energy-efficient network, while maximizing system performance, minimizing manufacturing cost, and adhering to thermal constraints. In the logical layer, our co-optimization considers eight different network topologies. In the physical layer, we consider routing, microbump assignment, and microbump pitch constraints to account for the extra costs associated with microbump utilization in the inter-chiplet communication. In the circuit layer, we consider both passive and active links with five different link types, including a gas station link design. Using our cross-layer methodology results in more accurate determination of (superior) inter-chiplet network and 2.5D system designs compared to prior methods. Compared to 2D systems, our approach achieves 29% better performance with the same manufacturing cost, or 25% lower cost with the same performance.
more »
« less
This content will become publicly available on July 1, 2026
Spatial Packaging and Routing Optimization of Complex Interacting Engineered Systems
Abstract Designing the 3D layout of interconnected systems (SPI2), which is a ubiquitous task in engineered systems, is of crucial importance. Intuitively, it can be thought of as the simultaneous placement of (typically rigid) components and subsystems, as well as the design of the routing of (typically deformable) interconnects between these components and subsystems. However, obtaining solutions that meet the design, manufacturing, and life-cycle constraints is extremely challenging due to highly complex and nonlinear interactions between geometries, the multi-physics environment in which the systems participate, the intricate mix of rigid and deformable geometry, as well as the difficult manufacturing and life-cycle constraints. Currently, this design task heavily relies on human interaction even though the complexity of searching the design space of most practical problems rapidly exceeds human abilities. In this work, we take advantage of high-performance hierarchical geometric representations and automatic differentiation to simultaneously optimize the packing and routing of complex engineered systems, while completely relaxing the constraints on the complexity of the solid shapes that can be handled and enable intricate yet functionally meaningful objective functions. Moreover, we show that by simultaneously optimizing the packing volume as well as the routing lengths, we produce tighter packing and routing designs than by focusing on the bounding volume alone. We show that our proposed approach has a number of significant advantages and offers a highly parallelizable, more integrated solution for complex SPI2 designs, leading to faster development cycles with fewer iterations, and better system complexity management. Moreover, we show that our formulation can handle complex cost functions in the optimization, such as manufacturing and life-cycle constraints, thus paving the way for significant advancements in engineering novel complex interconnected systems.
more »
« less
- PAR ID:
- 10624933
- Publisher / Repository:
- ASME
- Date Published:
- Journal Name:
- Journal of Mechanical Design
- Volume:
- 147
- Issue:
- 7
- ISSN:
- 1050-0472
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Additive manufacturing (AM) can produce designs in a manner that greatly differs from the methods used in the older, more familiar technologies of traditional manufacturing (TM). As an example, AM's layer-by-layer approach to manufacturing designs can lead to the production of intricate geometries and make use of multiple materials, made possible without added manufacturing cost and time due to AM's “free complexity.” Despite this contrasting method for manufacturing designs, designers often forgo the new design considerations for AM (AM design heuristics). Instead, they rely on their familiarity with the design considerations for TM (TM design heuristics) regardless of the intended manufacturing process. For designs that are intended to be manufactured using AM, this usage of TM design considerations is wasteful as it leads to unnecessary material usage, increased manufacturing time, and can result in designs that are poorly manufactured. To remedy this problem, there is a need to intervene early in the design process to help address any concerns regarding the use of AM design heuristics. This work aims to address this opportunity through a preliminary exploration of the design heuristics that students naturally leverage when creating designs in the context of TM and AM. In this study, 117 students in an upper-level engineering design course were given an open-ended design challenge and later tasked with self-evaluating their designs for their manufacturability with TM and AM. This evaluation of the students' designs was later repeated by relevant experts, who would identify the common design heuristics that students are most likely to use in their designs. Future studies will build on these findings by cementing early-stage design support tools that emphasize the significant heuristics found herein. For example, this work found that the design heuristic “incorporating complexity” was the most significant indicator of designs most suited for AM and should therefore be highly encouraged/emphasized when guiding designers in the use of AM. In doing so, it will be possible for early-stage design support tools to maximally improve designs that are intended to be manufactured for AM.more » « less
-
Abstract This paper details the design and operation of a testbed to evaluate the concept of autonomous manufacturing to achieve a desired manufactured part performance specification. This testbed, the autonomous manufacturing system for phononic crystals (AMSPnC), is composed of additive manufacturing, material transport, ultrasonic testing, and cognition subsystems. Critically, the AMSPnC exhibits common manufacturing deficiencies such as process operating window limits, process uncertainty, and probabilistic failure. A case study illustrates the AMSPnC function using a standard supervised learning model trained by printing and testing an array of 48 unique designs that span the allowable design space. Using this model, three separate performance specifications are defined and an optimization algorithm is applied to autonomously select three corresponding design sets to achieve the specified performance. Validation manufacturing and testing confirms that two of the three optimal designs, as defined by an objective function, achieve the desired performance, with the third being outside the design window in which a distinct bandpass is achieved in phononic crystals (PnCs). Furthermore, across all samples, there is a marked difference between the observed bandpass characteristics and predictions from finite elements method computation, highlighting the importance of autonomous manufacturing for complex manufacturing objectives.more » « less
-
Abstract Increasing complexity, and requirements for the precise creation of parts, necessitate the use of computer numerical control (CNC) manufacturing. This process involves programmed instructions to remove material from a workpiece through operations such as milling, turning, and drilling. This manufacturing technique incorporates various process parameters (e.g., tools, spindle speed, feed rate, cut depth), leading to a highly complex operation. Additionally, interacting phenomena between the workpiece, tools, and environmental conditions further add to complexity which can lead to defects and poor product quality. Two main areas are of focus for an efficient automated system: monitoring and swift quality assessment. Within these areas, the critical aspects ascertaining the quality of a CNC manufacturing operation are: 1) Tool wear: the inherent deterioration of machine components caused by prolonged utilization, 2) Chatter: vibration that occurs during the machining process, and 3) Surface finish: the final product’s surface roughness. Many research domains tend to focus on just one of these areas while neglecting the interconnected influences of all three. Therefore, to capture a more holistic and comprehensive assessment of a manufacturing process, the overall product quality should be considered, as that’s what ultimately counts. The integration of CNC systems with in-situ monitoring devices such as acoustic sensors, high-speed cameras, and thermal cameras is aimed at understanding the underlying physical aspects of the CNC machining process, including tool wear, chatter, and surface roughness. The incorporation of these monitoring devices has allowed the use of artificial intelligence and machine learning (ML) in smart CNC systems with hopes of increasing productivity, minimizing downtime, and ensuring product quality. By capturing the underlying phenomena that occur during the manufacturing process, users hope to understand the interlinking dynamics for zero-defect automated manufacturing. However, even though the use of ML methods has yielded noteworthy results in analyzing in-situ process data for CNC manufacturing, the black-box nature of these models and their tendency to focus predominantly on single-task objectives rather than multi-task scenarios pose challenges. In real-world part creation and manufacturing scenarios, there is often a need to address multiple interconnected tasks simultaneously which demands models that can multitask effectively. Yet, many ML models designed and trained for singular objectives are limited in their applicability and efficiency in more complex multi-faceted environments. Addressing these challenges, we introduce MTaskHD, a novel multi-task framework, that leverages hyperdimensional computing (HDC) to effortlessly fuse data from various channels and process signals while characterizing quality within a multi-task manufacturing operation. Moreover, it yields interpretable outcomes, allowing users to understand the process behind predictions. In a real-world experiment conducted on a hybrid 5-axis CNC Deckel-Maho-Gildemeister, MTaskHD was implemented to forecast the quality of three distinct features: left 25.4 mm counterbore diameter, right 25.4 mm counterbore diameter, and 2.54 mm milled radius. Demonstrating remarkable performance, the model excelled in predicting the quality levels of all three features in its multi-task configuration with an F1-Score of 95.3%, outperforming alternative machine learning approaches, including support vector machines, Naïve Bayes, multi-layer perceptron, convolutional neural network, and time-LeNet. The inherent multi-task capability, robustness, and interpretability of HDC collectively offer a solution for comprehending intricate manufacturing dynamics and operations.more » « less
-
The shift towards Next Generation Science Standards represents a paradigmatic change in teaching, transitioning from knowledge transmission to knowledge generation approaches. This reform underscores the complexity of teaching expertise, extending beyond mere knowledge to require a profound comprehension of generative learning environments. In this study, we explore Adaptive Teaching Expertise (AdTex), defining it as a teacher’s capacity characterized by fluidity and reflexiveness in teaching dynamics, rather than just flexibility. Through a complexity framing approach, we delineate three layers of AdTex: the visible actions of teachers, the semi-visible use of epistemic tools such as language, dialogue, and argument, and the tacit orientations towards learning that encompass epistemological, ontological, and axiological dimensions. Our research primarily investigates the intricate relationship between the epistemic tool and orientation layers. Our findings highlight the significance of an interconnected understanding and the impact of philosophical orientations on adaptive teaching practices. A notable contribution of this study is the development of a framework that articulates the belief and knowledge systems crucial for fostering generative learning environments, alongside the introduction of complexity maps to illustrate the interplay among these subsystems.more » « less
