As the need for efficient warehouse logistics has increased in manufacturing systems, the use of automated guided vehicles (AGVs) has also increased to reduce travel time. The AGVs are controlled by a system using laser sensors or floor-embedded wires to transport pallets and their loads. Because such control systems have only predefined palletizing strategies, AGVs may fail to engage incorrectly positioned pallets. In this study, we consider a vision sensor-based method to address this shortcoming by recognizing a pallet’s position. We propose a multi-task deep learning architecture that simultaneously predicts distances and rotation based on images obtained from a visionary sensor. These predictions complement each other in learning, allowing a multi-task model to learn and execute tasks impossible with single-task models. The proposed model can accurately predict the rotation and displacement of the pallets to derive information necessary for the control system. This information can be used to optimize a palletizing strategy. The superiority of the proposed model was verified by an experiment on images of stored pallets that were collected from a visionary sensor attached to an AGV.

Root cause analysis (RCA) methods for effectively identifying the critical causes of abnormal processes have attracted attention because manufacturing processes have grown in scale and complexity. However, the existing methods for building automatic RCA models suffer from the disadvantage of typically requiring expert knowledge. In addition, without a dataset representing the causal relationship of multivariate processes, it is difficult to provide useful information for RCA. Although data-driven RCA methods have been proposed, most are based on classification models. Given that product quality is defined as a continuous variable in many manufacturing industries, classification models are limited in deriving root causes affecting the product quality level. In this article, we propose a regression model-based RCA method, which we call quality-discriminative localization, consisting of a convolutional neural network (CNN)-based activation mapping of multisensor signal data. In our proposed method, the CNN predicts the product quality of a continuous variable. Activation mapping then extracts causal maps that highlight significant sensor signals for each product. To identify the root causes, we generate a root cause map from the weighted sum of quality and causal maps. We consider root causes as locations of abnormal processes and processing times from localized activation scores on the root cause map. We experimentally demonstrate the usefulness of the proposed method with simulated data and real process data from a steel manufacturing process. Our results show that the proposed method successfully identifies root causes with distinct sensor signal patterns.

Classification of multisensor signals is an important problem in maintaining stable process operations, particularly in advancing predictive modeling for early detection of abnormal states. Self-supervised learning methods, one of the representation learnings, have been widely studied. However, they have focused on using unlabeled data. In this study, we aim to address the challenge of effectively utilizing fully labeled data for modeling multisensor signals. We introduce supervised contrastive learning (SCL) for the classification of multisensor signals. Our training framework involves a two-step process: SCL for encoder pretraining with time-series data augmentations, and classifier training with the pretrained encoder. Our method exhibits superior performance, outperforming traditional supervised learning approaches by a substantial margin. Furthermore, we demonstrate the practical applicability of our approach for early prediction problems through experiments conducted with real-process data obtained from automobile engine manufacturing. Our work offers a promising method for multisensor signal analysis and early fault detection in manufacturing industries.

The identification of germline BRCA1/2 (BRCA) mutations plays an important role in the treatment planning of high-risk breast cancer patients, but genetic testing may be costly or unavailable. The multiparametric breast MRI (mpMRI) features offer noninvasive imaging biomarkers that could support BRCA mutation prediction. In this study, we investigate whether mpMRI features can predict BRCA mutation status in high-risk breast cancer patients. We collected data from 231 consecutive patients (82 BRCA-positive, 149 BRCA-negative) who underwent BRCA mutation testing and preoperative MRI between 2013 and 2019. We used the mpMRI features, including computer-aided diagnosis (CAD)-derived kinetic features, morphologic features, and apparent diffusion coefficient (ADC) values from diffusion-weighted imaging (DWI). In the univariate analysis, higher CAD-derived washout component and peak enhancement, larger tumor size and angio-volume, peritumoral edema on T2-weighted imaging, axillary adenopathy, and minimal or mild background parenchymal enhancement (BPE) were significantly associated with BRCA mutation, while ADC values showed no significant differences. In the multivariate analysis, three significant predictors were washout component ≥ 19.5% (odds ratio [OR] = 3.89, p < 0.001), minimal or mild BPE (OR = 2.57, p = 0.004), and tumor size ≥ 2.5 cm (OR = 2.41, p = 0.004). Using these predictors, we compared the predictive performance of 13 ML models through 30 repeated runs and achieved the highest performance (AUC = 0.72). In conclusion, ML models integrating mpMRI features demonstrated good performance for predicting BRCA mutations in high-risk patients. This noninvasive approach may aid personalized treatment planning and genetic counseling.

In the tire manufacturing industry, factor analysis plays a crucial role in deriving design candidates that can increase tire performance by deriving design factors, including variable importance and directions of design change. Typically, expert knowledge and finite element analysis (FEA) are necessary to derive design factors. However, they required a high computation load. Although several studies have been performed using machine learning and variable selection methods, they have limitations in recommending design change directions for each variable. In this study, we propose a predictive model coupled with Shapley additive explanations (SHAP) for tire design factor analysis. We first construct a predictive model using tire design data, and then applied SHAP for deriving important variables and directions of design change. To demonstrate the effectiveness of our methodology, we evaluate the design candidates using FEA. Results show that our methodology is effective for a tire factor analysis.