Latest preprints

Small businesses in the semiconductor industry face unique challenges in optimizing low-volume, highly customized production. Our study introduces an optimization framework that integrates system-dynamics modeling, linear programming, and predictive analytics to streamline supply chain networks and improve manufacturing efficiency. By leveraging Python-based simulations, our approach enhances cost-effectiveness, supports rapid prototyping, and utilizes cross-validated machine learning for predictive modeling to optimize production outcomes. Through statistical validation including correlation analysis and ANOVA, plus comparative analysis with alternative optimization techniques, our framework demonstrates significant improvements in both theoretical efficiency and practical application. The framework not only advances the theoretical foundation for specialized semiconductor manufacturing but also provides practical insights tailored to the constraints and implementation challenges faced by Small and Medium Enterprises (SMEs).

In Laser Powder Bed Fusion (LPBF), the applied laser energy produces high thermal gradients that lead to unacceptable final part distortion. Accurate distortion prediction is essential for optimizing the 3D printing process and manufacturing a part that meets geometric accuracy requirements. This study introduces data-driven parameterized reduced-order models (ROMs) to predict distortion in LPBF across various machine process settings. We propose a ROM framework that combines Proper Orthogonal Decomposition (POD) with Gaussian Process Regression (GPR) and compare its performance against a deep-learning based parameterized graph convolutional autoencoder (GCA). The POD-GPR model demonstrates high accuracy, predicting distortions within $\pm 0.001\text{mm}$, and delivers a computational speed-up of approximately 1800x.

Process Flow Diagrams (PFDs) and Process and Instrumentation Diagrams (PIDs) are critical tools for industrial process design, control, and safety. However, the generation of precise and regulation-compliant diagrams remains a significant challenge, particularly in scaling breakthroughs from material discovery to industrial production in an era of automation and digitalization. This paper introduces an autonomous agentic framework to address these challenges through a two-stage approach involving knowledge acquisition and generation. The framework integrates specialized sub-agents for retrieving and synthesizing multimodal data from publicly available online sources and constructs ontological knowledge graphs using a Graph Retrieval-Augmented Generation (Graph RAG) paradigm. These capabilities enable the automation of diagram generation and open-domain question answering (ODQA) tasks with high contextual accuracy. Extensive empirical experiments demonstrate the framework’s ability to deliver regulation-compliant diagrams with minimal expert intervention, highlighting its practical utility for industrial applications.

The Aerosol Jet® (AJ) direct-write technology enables material deposition for producing micron-scale fine features on various substrates with almost arbitrary geometry, which is of particular interest in manufacturing 3D conformal electronics among other applications. It operates by delivering an aerosol mist stream consisting of micron-size “ink” droplets, through an aerodynamic focusing nozzle with co-flowing sheath gas in a tapered channel, to the substrate several millimetres away with an impinging jet flow [1]. The ink pattern produced with AJ printing is essentially controlled in a “vector drawing mode” by the relative motion of the substrate with respect to the deposition nozzle. When a constant mist flow rate is fed into the deposition head, a mechanism for stopping the mist flow toward substrate must be implemented to prevent ink deposition in places where ink is undesired along the substrate moving toolpath. In other words, the mist stream toward substrate needs to be switched off at the time when the places of substrate where ink deposition is unwanted are moved under the AJ deposition nozzle, and to be switched on at the time when ink deposition is wanted. For the mist flow toward substrate, it can be considered as a function of time with on or off at certain time points. Here we describe a vacuum-boost diverter for rapid mist flow on-and-off switching in AJ printing, with OpenFOAM® simulations to aid its development and to offer fundamental understanding of its operational behaviour.

Efficient cargo packing and transport unit stacking play a vital role in enhancing logistics efficiency and reducing costs in the field of logistics. This article focuses on the challenging problem of loading transport units onto pallets, which belongs to the class of NP-hard problems. We propose a novel method for solving the pallet loading problem using a branch and bound algorithm, where there is a loading order of transport units. The derived algorithm considers only a heuristically favourable subset of possible positions of the transport units, which has a positive effect on computability. Furthermore, it is ensured that the pallet configuration meets real-world constraints, such as the stability of the position of transport units under the influence of transport inertial forces and gravity.