Engineering Design Principles Research Articles

Process Optimization in a Condiment SME through Improved Lean Six Sigma with a Surface Tension Neural Network

This study offers an innovative solution to address performance issues in the manufacturing process of garlic salt within a condiment-producing SME. A hybrid Lean/Six Sigma model utilizing a Surface Tension Neural Network (STNN) was implemented to control temperature and relative humidity in real-time. The model follows the Define, Measure, Analyze, Improve, Control (DMAIC) methodology to identify root causes and correlate them with waste. By integrating statistical tools, artificial intelligence, and engineering design principles, alternative solutions were evaluated to minimize waste. This document contributes to existing knowledge by demonstrating the integration of an STNN with the Lean/Six Sigma framework in condiment production, an area with limited empirical research. It underscores the benefits of advanced AI technologies in enhancing traditional process optimization methods. The STNN model achieved 97.31% accuracy for temperature classification and 97.37% for humidity, outperforming a Naive Bayes model, which attained 90% accuracy for both. The results showed a 3.15% increase in yield, saving 39.7 kg of waste per batch. Additionally, a 2.13-point improvement at the Six Sigma level was achieved, reducing defects per million opportunities by 551.722. These improvements resulted in significant cost savings, with a reduction in waste-related losses amounting to USD 1585 per batch. The study demonstrates that incorporating artificial intelligence into the Lean/Six Sigma methodology effectively addresses the limitations of traditional statistical methods. Significant improvements in yield and waste reduction highlight the potential of this approach, enhancing operational efficiency and profitability, and fostering sustainable manufacturing practices critical for SMEs’ competitiveness and sustainability in the global market.

Preface for special issue on Microbial Chemical Factories

Microbial chemical factories utilize engineering design principles to re-build natural production pathways, enabling the precise, quantitative, and efficient synthesis of chemicals. This is achieved through the optimization of synthetic pathways, the reconstruction of biochemical networks, the development of novel components, and the integration of pathways with cellular and environmental contexts. As a transformative approach to chemical production, microbial chemical factories play a critical role in establishing renewable raw material pathways for industrial economic development and advancing sustainable growth. This innovative model has become a strategic priority for technological advancement and industrial competitiveness in developed nations. This industry has expanded into diverse sectors, including pharmaceuticals, food production, chemicals, and energy. To showcase the most recent scientific advancements in the field of microbial chemical production and to promote the evolution of the bio-manufacturing industry, we organized a special issue entitled "Microbial Chemical Factories". This edition features the latest research conducted by domestic scientists, focusing on areas such as the synthesis of material monomers, pharmaceutical intermediates, functional food ingredients, organic acid biosynthesis, and the development and utilization of non-food feedstock. It provides reference and guidance for the further development of microbial chemical factories.

Soil Steel Composite Bridge (SSCB)

This project focuses on the design, construction, and implementation of a soil-steel composite bridge, aiming to provide a durable and cost-effective solution for spanning. The study involves a comprehensive site assessment to understand soil conditions, hydrological aspects, and environmental considerations. Engineering design principles are employed to create a structure that optimizes load-bearing capacity while ensuring adaptability to varying terrains and traffic demands. The material selection process involves the careful choice of steel components, considering their resistance to corrosion and compatibility with the surrounding soil. Construction planning includes detailed strategies for assembly, installation, quality control measures, and adherence to safety standards throughout the project lifecycle. This abstract highlights the necessity of collaboration between engineers, designers, construction teams, and stakeholders to ensure successful execution. The expected outcomes include a durable, sustainable, and efficiently constructed soil-steel composite bridge, catering to the specific needs and challenges of the designated site. The FEM analysis results in showing rational outcome to the field measurement for structural response. The known design method of SSCB are Pettersson-Sundquist design method and Kloppel and Glock design method.

Design optimization of geometrically confined cardiac organoids enabled by machine learning techniques

Stem cell organoids are powerful models for studying organ development, disease modeling, drug screening, and regenerative medicine applications. The convergence of organoid technology, tissue engineering, and artificial intelligence (AI) could potentially enhance our understanding of the design principles for organoid engineering. In this study, we utilized micropatterning techniques to create a designer library of 230 cardiac organoids with 7 geometric designs. We employed manifold learning techniques to analyze single organoid heterogeneity based on 10 physiological parameters. We clustered and refined the cardiac organoids based on their functional similarity using unsupervised machine learning approaches, thus elucidating unique functionalities associated with geometric designs. We also highlighted the critical role of calcium transient rising time in distinguishing organoids based on geometric patterns and clustering results. This integration of organoid engineering and machine learning enhances our understanding of structure-function relationships in cardiac organoids, paving the way for more controlled and optimized organoid design.

Abstract 497: Plant viruses against cancer

Abstract Immunotherapy has significantly transformed the landscape of cancer treatment; however, concerns about safety, efficacy, and long-term outcomes across various tumor types persist. We have developed an intratumoral immunotherapy strategy with an engineered plant virus nanotechnology and demonstrated efficacy in tumor mouse models and canine cancer patients. This method is designed to elicit a systemic (abscopal effect), safe, and enduring anti-tumor immune response that is not limited by tumor type. The power of immunotherapy lies in synergistic combination treatments and toward this goal, we are engineering the next-generation plant virus nanotechnologies. We demonstrate synergistic combination with chemotherapy and immunization strategies the hone in on tumor-associated antigens and key players of inflammation. The combinatorial immunotherapy strategies prevent recurrence post-surgery in mice and canine patients. We will discuss the engineering design principles leading toward synergetic combination immunotherapies to treat and prevent cancer; efficacy studies and underlying mechanism is elucidated through immunomics studies. Selected references: Steinmetz N.F. et al (2023) Viral nanoparticle vaccines against S100A9 reduce lung tumor seeding and metastasis. Proc. Natl. Acad. Sci. U.S.A. 120 (43) e2221859120. Wang C. and Steinmetz N.F. (2020) A Combination of Cowpea mosaic virus and Immune Checkpoint Therapy Synergistically Improves Therapeutic Efficacy in Three Tumor Models. Advanced Functional Materials, 2002299. Citation Format: Nicole F. Steinmetz, Young Hun Chung, Miguel Moreno-Gonzalez, Zhongchao Zhao. Plant viruses against cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 497.

Rancang Bangun Sistem Penyiraman Otomatis pada Tanaman Sawi dengan Sistem Irigasi Tetes untuk Lahan Pertanian Lereng Gunung Ungaran

Cultivating mustard greens requires maintaining the ideal soil moisture level within the range of 50% to 70% Relative Humidity (RH). To fulfill water requirements and preserve soil moisture, irrigation processes are essential. Historically, farmers have employed manual irrigation methods, such as flooding entire agricultural fields, leading to water wastage and requiring significant labor. This research aims to develop an automated irrigation system based on soil conditions to assist farmers in the irrigation process. The automated irrigation system operates by responding to soil conditions. It initiates irrigation when the soil is dry and automatically ceases when the soil becomes adequately moist. This system utilizes Arduino as the central control unit, soil moisture sensors as input devices, an LCD for monitoring, and a water pump for distributing water to the agricultural fields through drip irrigation. The research methodology follows engineering design principles and encompasses three stages: design, installation, and implementation. The design phase involves creating the system, programming, and designing the drip irrigation system. Installation entails assembling the components of the automated irrigation system, while implementation involves deploying the system in agricultural fields and conducting tests. The research findings indicate that the automated irrigation system effectively irrigates when soil moisture falls below 50% and halts when it reaches 70%. Calibration tests reveal a 2% variance between the capacitive soil moisture sensor and the Soil Moisture Meter. The water requirement for a single mustard greens plant to reach the ideal soil moisture level (50-70% RH) is approximately 150 ml.

A deep learning approach for balance optimisation of patch panel assembly line

This paper introduces an integrated methodology to enhance the efficiency of patch panel assembly lines by harnessing advanced artificial intelligence (AI) techniques. The primary objective is to minimise the total completion time of patch panels, a pivotal metric for manufacturing efficiency. The study employs the Assembly Line Production Optimisation (ALPO) model to analyze and enhance the production capacity of the bottleneck station. Existing challenges related to patch panel assembly line task distribution, process content, and tooling layout are scrutinised against production process standards for operational measurements. To achieve improvement, Deep Neural Network (DNN) and Recurrent Neural Network (RNN) models are employed in hidden layers, supplemented by deep feed-forward (DFF) layers for output, in adherence to engineering design principles. The bottleneck station's load rate is successfully reduced to below 80%, contributing to an overall balance rate increase from 73% to 88%. These outcomes signify a notable enhancement in production efficiency and employee satisfaction. The research utilises a dataset derived from the Microsoft Azure AI-based PdM dataset. The paper illustrates how AI techniques offer a comprehensive solution to address the inherent complexities in patch panel assembly line optimisation.

An architecturally rational hemostat for rapid stopping of massive bleeding on anticoagulation therapy

Hemostatic devices are critical for managing emergent severe bleeding. With the increased use of anticoagulant therapy, there is a need for next-generation hemostats. We rationalized that a hemostat with an architecture designed to increase contact with blood, and engineered from a material that activates a distinct and undrugged coagulation pathway can address the emerging need. Inspired by lung alveolar architecture, here, we describe the engineering of a next-generation single-phase chitosan hemostat with a tortuous spherical microporous design that enables rapid blood absorption and concentrated platelets and fibrin microthrombi in localized regions, a phenomenon less observed with other classical hemostats without structural optimization. The interaction between blood components and the porous hemostat was further amplified based on the charged surface of chitosan. Contrary to the dogma that chitosan does not directly affect physiological clotting mechanism, the hemostat induced coagulation via a direct activation of platelet Toll-like receptor 2. Our engineered porous hemostat effectively stopped the bleeding from murine liver wounds, swine liver and carotid artery injuries, and the human radial artery puncture site within a few minutes with significantly reduced blood loss, even under the anticoagulant treatment. The integration of engineering design principles with an understanding of the molecular mechanisms can lead to hemostats with improved functions to address emerging medical needs.

Hyperconjugation-controlled molecular conformation weakens lithium-ion solvation and stabilizes lithium metal anodes

The hyperconjugation-controlled molecular conformation can tune the solvation properties of electrolytes, providing a new design principle for battery electrolyte engineering.

Design guidelines for HAWT / VAWT decision-making in the context of underserved communities

Underserved communities in rural areas often lack access to appropriate technologies that provide sustainable and reliable energy sources vital to their well-being and economic activities. In this context, the potential of small-scale wind turbine generators (WTGs) as a renewable energy source is investigated, as they can provide enough energy while being relatively easy to operate and maintain. Nevertheless, limited research is carried out about the comparative advantages of vertical axis wind turbines (VAWTs) and horizontal axis wind turbines (HAWTs) for these resource-limited conditions. The research approach includes a literature review, a field study in Kenya, and a multi-criteria decision analysis (MCDA) tailored to local wind conditions and humanitarian engineering design principles. Structural, operational, and performance differences between HAWTs and VAWTs are explored as well as the local needs of underserved communities. The MCDA results highlight design principles for the deployment of small wind turbines in rural areas and provide useful design guidelines for WTG development in underserved regions.

Intraluminal Flow Diverter Design Primer for Neurointerventionalists.

The clinical use of flow diverters for the treatment of intracranial aneurysms has rapidly grown. Consequently, the market and technology for these devices has also grown. Clinical performance characteristics of the flow diverter are well-known to the clinician. However, the engineering design principles behind how these devices achieve ideal clinical performance are less understood. This primer will summarize flow diverter design parameters for neurointerventionalists with the aim of promoting collaboration between clinicians and engineers.

Lactation physiokinetics-using advances in technology for a fresh perspective on human milk transfer.

Though the nature of breastfeeding is critical, scant information is available on how the action of the milk transfer from mother to infant is regulated in humans, where the points of dysfunction are, and what can be done to optimize breastfeeding outcomes. While better therapeutic strategies are needed, before they can be devised, a basic scientific understanding of the biomechanical mechanisms that regulate human milk transfer from breast to stomach must first be identified, defined, and understood. Combining systems biology and systems medicine into a conceptual framework, using engineering design principles, this work investigates the use of biosensors to characterize human milk flow from the breast to the infant's stomach to identify points of regulation. This exploratory study used this framework to characterize Maternal/Infant Lactation physioKinetics (MILK) utilizing a Biosensor ARray (BAR) as a data collection method. Participants tolerated the MILKBAR well during data collection. Changes in breast turgor and temperature were significant and related to the volume of milk transferred from the breast. The total milk volume transferred was evaluated in relation to contact force, oral pressure, and jaw movement. Contact force was correlated with milk flow. Oral pressure appears to be a redundant measure and reflective of jaw movements. Nipple and breast turgor, jaw movement, and swallowing were associated with the mass of milk transferred to the infant's stomach. More investigation is needed to better quantify the mass of milk transferred in relation to each variable and understand how each variable regulates milk transfer.

Welcome, How Can I Help You? Design Considerations for a Virtual Reality Environment to Support the Orientation of Online Initial Teacher Education Students

Alongside the rapid and broad uptake of online learning in higher education, fully online students report feeling isolated and disconnected from their institutions. Although formal course content may be expertly designed to engage online learners, much of the information provided to support higher education students’ orientation to the institution and to study is presented online in a written static form. Such presentations may not be accessible and engaging and may contribute to feelings of disconnection. Technologies such as virtual reality (VR) are being used in higher education to engage, motivate and connect students in their learning. This paper reports on the early design stages for a VR that aims to support initial teacher education students to connect and engage with key orienting information. The design of the VR was achieved by following a user-centred, iterative engineering design process and design principles of spatiality, interaction and narrative. The VR environment emulates the School of Education’s physical, on-campus reception area to provide an immersive experience where students have a choice in the types and format of key study information they receive. This experience was designed to be utilised in online orientation but also throughout students’ first year of study. Future research directions include collecting student responses to the VR to inform how students can be involved in enhancing the VR so that it supports their learning and sense of connection. Furthermore, future research can aim for the expansion of the VR inclusive of additional information, rooms and buildings and increased capabilities such as gamification and mobile access. This will enable the creation of a valuable teaching resource for online programs.

Design Principle of a Water-Dispersed Photocatalytic Perovskite through Ligand Deconstruction

Strategically designed surface modifiers that produce stable perovskite nanocrystals (NCs) and allow efficient charge extraction in polar solvents are critical for perovskite photocatalysis. We designed a multifunctional bolaamphiphilic ligand (NKE-12) with multidentate ionic groups at both ends, which significantly increases the colloidal stability of CsPbBr3 NCs in an aqueous medium without affecting their structural integrity and catalytic attributes. Ligand deconstruction via K and E fragmented ligands revealed synergistic actions of the cationic and anionic functionalities in surface passivation, phase separation, and water localization away from the surface of NKE-12-modified CsPbBr3 NCs. Multidimensional nuclear magnetic resonance experiments and contact angle measurements further suggested surface interactions and improved hydrophilicity via ionic terminal groups that account for water stability. Photogenerated hole-transfer dynamics of CsPbBr3/NKE-12 NCs to a probe molecule 6,7-dihydroxycoumarin was studied using transient absorption spectroscopy. Overall, this study paves the way for ligand design principles for surface engineering to develop water-stable perovskite photocatalysts.

The Correlation between Mechanobiology and Fracture Healing; Osteosynthesis, Biomaterial Optimization and TRIZ Design Principles to Develop Ameliorative Implants for Reconstruction of Median Osteochondrotomy of the Sternum.

The application of stainless-steel wire is still the "standard of care" and is believed to be the "gold standard" after trans-sternal thoracotomy. To overcome postoperative instability and surgical wound infection there had been the development of a variety of circumferential, Hemi-circular, and surface on-lay implant designs to enhance bone healing of the sternum particularly in compromised patients. This fundamental descriptive theoretical research study probes into biology and effects of mechanical environment on fracture healing in general and various types of ossifications that may occur during healing of the sternum. Following surgical anatomy of the sternum, the biology of fracture (osteotomy) healing, an update on the conventional and newer biomaterials, and role of 3D printing in custom additive manufacturing of the surgical implants have been discussed in detail. There is discussion on design principles and structural optimization in-line with patient-specific and patient-appropriate osteosynthesis. In support, the Teorija Rezhenija Izobretatelskikh Zadatch engineering principles have been applied to improve implant design in the face of the current strategies to relieve some of the recalcitrant deficiencies underlying the mechanics of the most favored implant for the reconstruction of the sternum. Several scientific domains of the engineering design principles and fracture healing processes have been connected leading to four newly conceptualized prototype designs for the reconstruction of the sternum. In conclusion, despite increased knowledge of the fracture healing process there are limited means to mitigate the adverse mechanical environment experienced by the healing sternum. There are uncertainties how to transfer the well-known facts of tissue strain during healing from the experimental platform to the operating table at the time of fracture fixation and reconstruction of the sternum for its optimal healing.

Polymeric engineering of AIEgens for NIR-II fluorescence imaging and detection of abdominal metastases of ovarian cancer in vivo.

A polymeric engineering design principle is proposed for the construction of small-sized (∼20 nm) NIR-II AIEgen-doped nanodots (AIEdots) with high brightness and prolonged circulation time in blood vessels. With the utilization of the as-designed NIR-II AIEdots, the successful achievement of high-resolution NIR-II fluorescence imaging of tumor vessels and precise detection of abdominal metastases of ovarian cancer has been attained.

Flat bands arising from spin-orbit assisted orbital frustration

We present general design principles for engineering and discovering periodic systems with flat bands. Our paradigm exploits spin-orbit assisted orbital frustration on a lattice to produce band structures that contain multiplets of narrowly dispersing bands whose bandwidth is smaller than all other energy scales of the problem including the band gap surrounding the flat bands. We present a series of models in 1D and 2D on various lattices with different intracellular spin-orbit like potentials that hybridize the degrees of freedom in the unit cell. As an alternative to machine learning based exhaustive searches, these design principles and models can be used to search for flat band systems in a variety of physical settings and can be used to investigate the role of weakly dispersing highly orbitally frustrated degrees of freedom in systems where the interactions dominate over the kinetic energy scales of the system.

Artificial Intelligence for Power Electronics in Electric Vehicles: Challenges and Opportunities

Abstract Progress in the field of power electronics within electric vehicles has generally been driven by conventional engineering design principles and experiential learning. Power electronics is inherently a multidomain field where semiconductor physics and electrical, thermal, and mechanical design knowledge converge to achieve a practical realization of desired targets in the form of conversion efficiency, power density, and reliability. Due to the promising nature of artificial intelligence in delivering rapid results, engineers are starting to explore the ways in which it can contribute to making power electronics more compact and reliable. Here, we conduct a brief review of the foray of artificial intelligence in three distinct subtechnologies within a power electronics system in the context of electric vehicles: semiconductor devices, power electronics module design and prognostics, and thermal management design. The intent is not to report an exhaustive literature review, but to identify the state of the art and opportunities for artificial intelligence to play a meaningful role in power electronics design from a mechanical and thermal standpoint, as well as to discuss a few promising future research directions.

A Methodology to Support the Development of a New State Vision for the U.S. Nuclear Industry

Recent changes in natural gas prices combined with reduced capital costs for solar and wind systems has created challenges for the continued operation of existing nuclear power plants (NPPs) in the United States. A new strategy in the way in which U.S. NPPs are operated, maintained, and supported is needed. One such strategy is to transform the NPP operating model through a business-driven approach that leverages technology to enable new capabilities that improve performance and reduce costs. This paper presents a methodology for developing an achievable yet transformative new state vision that ensures the continued safe and efficient operations of the U.S. NPP fleet. This work builds on existing guidance and leverages previous research to comprehensively address both utility needs and high-level human factors engineering design principles when developing a new state vision. The proposed methodology is intended to provide industry-wide guidance for developing a new state vision that leverages both the selected vendor’s capabilities in a way that meets the utility’s modernization goals while ensuring state-of-the-art systems engineering and human factors engineering principles are applied that promote overall plant safety, performance, and efficiency.

Assessment of Student Learning Through Reflection on Doing Using the Latent Dirichlet Algorithm

AbstractCan we provide evidence-based guidance to instructors to improve the delivery of the course based on students’ reflection on doing? Over three years at the University of Oklahoma, Norman, USA, we have collected about 18,000 Take-aways from almost 400 students who participated in an undergraduate design, build, and test course. In this paper, we illustrate the efficacy of using the Latent Dirichlet Algorithm to respond to the question posed above. We describe a method to analyze the Take-aways using a Latent Dirichlet Allocation (LDA) algorithm to extract topics from the Take-away data and then relate the extracted topics to instructors’ expectations using text similarity. The advantage of the LDA algorithm is anchored in that it provides a means for summarizing large amount of take-away data into several key topics so that instructors can eliminate the labor-intensive evaluation of it. By connecting and comparing what students learned (embodied in Take-aways) and what instructors expected the students to learn (embodied in stated Principles of Engineering Design), we provide evidence-based guidance to instructors on how to improve the delivery of AME4163: Principles of Engineering Design. Our objective in this paper is to introduce a method for quantifying text data to facilitate an instructor to modify the content and delivery of the next version of the course. The proposed method can be extended to other courses patterned after AME4163 to generate similar data sets covering student learning and instructor expectations, and the LDA algorithm can be used for dealing with the large amount of textual data embodied in students’ Take-aways.