Explainable Artificial Intelligence for Manufacturing Cost Estimation and Machining Feature Visualization

Studies on manufacturing cost prediction based on deep learning have begun in recent years, but the cost prediction rationale cannot be explained because the models are still used as a black box. This study aims to propose a manufacturing cost prediction process for 3D computer-aided design (CAD) models using explainable artificial intelligence. The proposed process can visualize the machining features of the 3D CAD model that are influencing the increase in manufacturing costs. The proposed process consists of (1) data collection and pre-processing, (2) 3D deep learning architecture exploration, and (3) visualization to explain the prediction results. The proposed deep learning model shows high predictability of manufacturing cost for the computer numerical control (CNC) machined parts. In particular, using 3D gradient-weighted class activation mapping proves that the proposed model not only can detect the CNC machining features but also can differentiate the machining difficulty for the same feature. Using the proposed process, we can provide a design guidance to engineering designers in reducing manufacturing costs during the conceptual design phase. We can also provide real-time quotations and redesign proposals to online manufacturing platform customers

The Effect of Robo-taxi User Experience on User Acceptance: Field Test Data Analysis

With the advancement of self-driving technology, the commercialization of Robo-taxi services is just a matter of time. However, there is some skepticism regarding whether such taxi services will be successfully accepted by real customers due to perceived safety-related concerns; therefore, studies focused on user experience have become more crucial. Although many studies statistically analyze user experience data obtained by surveying individuals' perceptions of Robo-taxi or indirectly through simulators, there is a lack of research that statistically analyzes data obtained directly from actual Robo-taxi service experiences. Accordingly, based on the user experience data obtained by implementing a Robo-taxi service in the downtown of Seoul and Daejeon in South Korea, this study quantitatively analyzes the effect of user experience on user acceptance through structural equation modeling and path analysis. We also obtained balanced and highly valid insights by reanalyzing meaningful causal relationships obtained through statistical models based on in-depth interview results. Results revealed that the experience of the traveling stage had the greatest effect on user acceptance, and the cutting edge of the service and apprehension of technology were emotions that had a great effect on user acceptance. Based on these findings, we suggest guidelines for the design and marketing of future Robo-taxi services

Integrating Deep Learning into CAD/CAE System: Generative Design and Evaluation of 3D Conceptual Wheel

Engineering design research integrating artificial intelligence (AI) into computer-aided design (CAD) and computer-aided engineering (CAE) is actively being conducted. This study proposes a deep learning-based CAD/CAE framework in the conceptual design phase that automatically generates 3D CAD designs and evaluates their engineering performance. The proposed framework comprises seven stages: (1) 2D generative design, (2) dimensionality reduction, (3) design of experiment in latent space, (4) CAD automation, (5) CAE automation, (6) transfer learning, and (7) visualization and analysis. The proposed framework is demonstrated through a road wheel design case study and indicates that AI can be practically incorporated into an end-use product design project. Engineers and industrial designers can jointly review a large number of generated 3D CAD models by using this framework along with the engineering performance results estimated by AI and find conceptual design candidates for the subsequent detailed design stage

Aligning Large Language Models through Synthetic Feedback

Aligning large language models (LLMs) to human values has become increasingly important as it enables sophisticated steering of LLMs, e.g., making them follow given instructions while keeping them less toxic. However, it requires a significant amount of human demonstrations and feedback. Recently, open-sourced models have attempted to replicate the alignment learning process by distilling data from already aligned LLMs like InstructGPT or ChatGPT. While this process reduces human efforts, constructing these datasets has a heavy dependency on the teacher models. In this work, we propose a novel framework for alignment learning with almost no human labor and no dependency on pre-aligned LLMs. First, we perform reward modeling (RM) with synthetic feedback by contrasting responses from vanilla LLMs with various sizes and prompts. Then, we use the RM for simulating high-quality demonstrations to train a supervised policy and for further optimizing the model with reinforcement learning. Our resulting model, Aligned Language Model with Synthetic Training dataset (ALMoST), outperforms open-sourced models, including Alpaca, Dolly, and OpenAssistant, which are trained on the outputs of InstructGPT or human-annotated instructions. Our 7B-sized model outperforms the 12-13B models in the A/B tests using GPT-4 as the judge with about 75% winning rate on average.Comment: Preprint, 9 pages (with 10 pages of supplementary

Wheel Impact Test by Deep Learning: Prediction of Location and Magnitude of Maximum Stress

The impact performance of the wheel during wheel development must be ensured through a wheel impact test for vehicle safety. However, manufacturing and testing a real wheel take a significant amount of time and money because developing an optimal wheel design requires numerous iterative processes of modifying the wheel design and verifying the safety performance. Accordingly, the actual wheel impact test has been replaced by computer simulations, such as Finite Element Analysis (FEA), but it still requires high computational costs for modeling and analysis. Moreover, FEA experts are needed. This study presents an aluminum road wheel impact performance prediction model based on deep learning that replaces the computationally expensive and time-consuming 3D FEA. For this purpose, 2D disk-view wheel image data, 3D wheel voxel data, and barrier mass value used for wheel impact test are utilized as the inputs to predict the magnitude of maximum von Mises stress, corresponding location, and the stress distribution of 2D disk-view. The wheel impact performance prediction model can replace the impact test in the early wheel development stage by predicting the impact performance in real time and can be used without domain knowledge. The time required for the wheel development process can be shortened through this mechanism

Exogenous 8-hydroxydeoxyguanosine attenuates doxorubicin-induced cardiotoxicity by decreasing pyroptosis in H9c2 cardiomyocytes

Doxorubicin (DOX), which is widely used in cancer treatment, can induce cardiomyopathy. One of the main mechanisms whereby DOX induces cardiotoxicity involves pyroptosis through the NLR family pyrin domain containing 3 (NLRP3) inflammasome and gasdermin D (GSDMD). Increased NAPDH oxidase (NOX) and oxidative stress trigger pyroptosis. Exogenous 8-hydroxydeoxyguanosine (8-OHdG) decreases reactive oxygen species (ROS) production by inactivating NOX. Here, we examined whether 8-OHdG treatment can attenuate DOX-induced pyroptosis in H9c2 cardiomyocytes. Exposure to DOX increased the peroxidative glutathione redox status and NOX1/2/4, toll-like receptor (TLR)2/4, and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) expression, while an additional 8-OHdG treatment attenuated these effects. Furthermore, DOX induced higher expression of NLRP3 inflammasome components, including NLRP3, apoptosis-associated speck-like protein containing a c-terminal caspase recruitment domain (ASC), and pro-caspase-1. Moreover, it increased caspase-1 activity, a marker of pyroptosis, and interleukin (IL)-1β expression. All these effects were attenuated by 8-OHdG treatment. In addition, the expression of the cardiotoxicity markers, atrial natriuretic peptide (ANP) and brain natriuretic peptide (BNP) was increased by DOX, whereas the increase of ANP and BNP induced by DOX treatment was reversed by 8-OHdG. In conclusion, exogenous 8-OHdG attenuated DOX-induced pyroptosis by decreasing the expression of NOX1/2/3, TLR2/4, and NF-κB. Thus, 8-OHdG may attenuate DOX-induced cardiotoxicity through the inhibition of pyroptosis.This research was funded by a grant from the National Research Foundation of Korea (NRF) grant (2020R1A2C200652811) (to K.H.S.) and Korea Environment Industry & Technology Institute (KEITI) through ‑Core Technology Development Project for Environmental Diseases Prevention and Management Program, funded by Korea Ministry of Environment (MOE) (2021003310006) (to K.H.S.

Quantitative prediction of oral bioavailability of a lipophilic antineoplastic drug bexarotene administered in lipidic formulation using a combined in vitro lipolysis/microsomal metabolism approach

For performance assessment of the lipid-based drug delivery systems (LBDDS), in vitro lipolysis is commonly applied because traditional dissolution tests do not reflect the complicated in vivo micellar formation and solubilisation processes. Much of previous research on in vitro lipolysis have mostly focused on rank-ordering formulations for their predicted performances. In this study, we have incorporated in vitro lipolysis with microsomal stability to quantitatively predict the oral bioavailability of a lipophilic antineoplastic drug bexarotene (BEX) administered in LBDDS. Two types of LBDDS were applied: lipid solution and lipid suspension. The predicted oral bioavailability values (Foral,predicted) of BEX from linking in vitro lipolysis with microsomal stability for lipid solution and lipid suspension were 34.2 1.6% and 36.2 2.6%, respectively, while the in vivo oral bioavailability (Foral) of BEX was tested as 31.5 13.4% and 31.4 5.2%, respectively. The Foral,predicted corresponded well with the Foral for both formulations, demonstrating that the combination of in vitro lipolysis and microsomal stability can quantitatively predict oral bioavailability of BEX. In vivo intestinal lymphatic uptake was also assessed for the formulations and resulted in [less than] 1% of the dose, which confirmed that liver microsomal stability was necessary for correct prediction of the bioavailability

An innovative strategy for standardized, structured, and interoperable results in ophthalmic examinations

Background Although ophthalmic devices have made remarkable progress and are widely used, most lack standardization of both image review and results reporting systems, making interoperability unachievable. We developed and validated new software for extracting, transforming, and storing information from report images produced by ophthalmic examination devices to generate standardized, structured, and interoperable information to assist ophthalmologists in eye clinics. Results We selected report images derived from optical coherence tomography (OCT). The new software consists of three parts: (1) The Area Explorer, which determines whether the designated area in the configuration file contains numeric values or tomographic images; (2) The Value Reader, which converts images to text according to ophthalmic measurements; and (3) The Finding Classifier, which classifies pathologic findings from tomographic images included in the report. After assessment of Value Reader accuracy by human experts, all report images were converted and stored in a database. We applied the Value Reader, which achieved 99.67% accuracy, to a total of 433,175 OCT report images acquired in a single tertiary hospital from 07/04/2006 to 08/31/2019. The Finding Classifier provided pathologic findings (e.g., macular edema and subretinal fluid) and disease activity. Patient longitudinal data could be easily reviewed to document changes in measurements over time. The final results were loaded into a common data model (CDM), and the cropped tomographic images were loaded into the Picture Archive Communication System. Conclusions The newly developed software extracts valuable information from OCT images and may be extended to other types of report image files produced by medical devices. Furthermore, powerful databases such as the CDM may be implemented or augmented by adding the information captured through our program.This research was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (Grant No.HI19C0373). The publication cost of this article was funded by KHIDI and it had no role in the design or conduct of this researc