Quality Prediction Framework Research Articles

Production quality prediction of multistage manufacturing systems using multi-task joint deep learning

A multistage manufacturing system with multiple manufacturing stages is the key and main production mode for enterprises to achieve lean production. Due to the variation propagation between stages and multiple related quality prediction tasks, it is difficult to accurately predict the quality of multistage manufacturing systems with multiple tasks. Traditional single stage and single task quality prediction methods permit the multi stages and multiple tasks separately, which ignore multi-stage effects or the quality-related relationship between multiple quality output indicators and reduce the efficiency of quality prediction. In this paper, a production quality prediction framework based on multi-task joint deep learning is proposed to simultaneously evaluate the multi-task quality of all stages in a multistage manufacturing system. To be specific, variation propagation cumulative impact between multiple manufacturing stages is innovatively expressed by designing a multi-scale convolutional neural network with control gates (MCNN-CG) to extract and propagate data features. Production quality with multi-tasks at all stages is jointly predicted by designing a multi-layer multi-gate mixture-of-experts multi-task (ML-MMoE) model with reducing multi-task predictive loss simultaneously. The soft parameter-sharing strategy and multi-gate attention strategy are separately designed to ensure information sharing while learning personalized features of tasks to improve quality prediction accuracy of each task. In addition, a loss function based on homoscedastic uncertainty and regularization is designed to automatically learn the weight between multi-stage and multi-task losses. Experiments on multistage assembly test data of an inertial navigation manufacturing system show that the proposed method performs better than traditional models. Compared to the single-stage model, the proposed multistage model has an average improvement of 8.5%, 20.0% and 23.3% in R2, MAE and RMSE respectively in the second stage. Compared with the traditional multi-stage model, the proposed model has an average improvement of 1.7%, 6.2% and 9.8% in R2, MAE and RMSE respectively in the second stage.

Correlation-split and Recombination-sort Interaction Networks for air quality forecasting

Air quality prediction is a crucial issue in air pollution control and plays a vital role in environmental preservation and the promotion of sustainable development. A novel air quality prediction framework, referred to as Correlation-split and Recombination-sort Interaction Networks (CRINet), is proposed in this paper. Firstly, the data is divided into smaller segments based on the Correlation-split strategy, and is followed by a convolution operation to enhance the capability of extracting essential features. Secondly, Recombination-sort strategy is used to facilitate the interaction between temporal features. The CRINet model utilizes a dual-layer CRINet network structure to extract internal dependencies and temporal features from air quality data for prediction. Finally, the Beijing PM2.5 dataset and the Beijing Shunyi-station Air Quality dataset are used for experimental evaluation. The outcomes demonstrate that the CRINet model surpasses other models in prediction accuracy, particularly in the prediction of multiple pollutant concentrations. This provides theoretical and methodological support for accurately assessing air quality and formulating treatment strategies for relevant departments.

Combining knowledge graph with deep adversarial network for water quality prediction.

Water quality prediction is an important research focus in smart water and can provide the support to control and reduce water pollution. However, existing water quality prediction models are mainly data-driven and only rely on various sensor data. This paper proposes a new water quality prediction modeling approach integrating data and knowledge. We develop a water quality prediction framework that combines knowledge graph and deep adversarial networks. The knowledge extraction and management compound extracts the water quality knowledge graph from different knowledge sources by using the deep adversarial joint model. The fusing parameter importance learning compound calculates the contribution of parameters in water quality prediction by taking into account both knowledge and data levels of correlation. Finally, a water quality prediction model combining weighted CNN-LSTM with adversarial learning predicts the values of total nitrogen based on real-time monitoring data. The experimental results on monitoring data from the Juhe River of China show that the proposed model can greatly improve the effect of water quality prediction.

An Alternative to Laboratory Testing: Random Forest-Based Water Quality Prediction Framework for Inland and Nearshore Water Bodies

Water quality monitoring plays a vital role in the water environment management, while efficient monitoring provides direction and verification of the effectiveness of water management. Traditional water quality monitoring for a variety of water parameters requires the placement of multiple sensors, and some water quality data (e.g., total nitrogen (TN)) requires testing instruments or laboratory analysis to obtain results, which takes longer than the sensors. In this paper, we designed a water quality prediction framework, which uses available water quality variables (e.g., temperature, pH, conductivity, etc.) to predict total nitrogen concentrations in inland water bodies. The framework was also used to predict nearshore seawater salinity and temperature using remote sensing bands. We conducted experiments on real water quality datasets and random forest was chosen to be the core algorithm of the framework by comparing and analyzing the performance of different machine learning algorithms. The results show that among all tested machine learning models, random forest performs the best. The data prediction error rate of the random forest model in predicting the total nitrogen concentration in inland rivers was 4.9%. Moreover, to explore the prediction effect of random forest algorithm when the independent variable is non-water quality data, we took the reflectance of remote sensing bands as the independent variables and successfully inverted the salinity distribution of Shenzhen Bay in the Google Earth Engine (GEE) platform. According to the experimental results, the random forest-based water quality prediction framework can achieve 92.94% accuracy in predicting the salinity of nearshore waters.

Water Quality Prediction Method Based on Multi-Source Transfer Learning for Water Environmental IoT System

Water environmental Internet of Things (IoT) system, which is composed of multiple monitoring points equipped with various water quality IoT devices, provides the possibility for accurate water quality prediction. In the same water area, water flows and exchanges between multiple monitoring points, resulting in an adjacency effect in the water quality information. However, traditional water quality prediction methods only use the water quality information of one monitoring point, ignoring the information of nearby monitoring points. In this paper, we propose a water quality prediction method based on multi-source transfer learning for a water environmental IoT system, in order to effectively use the water quality information of nearby monitoring points to improve the prediction accuracy. First, a water quality prediction framework based on multi-source transfer learning is constructed. Specifically, the common features in water quality samples of multiple nearby monitoring points and target monitoring points are extracted and then aligned. According to the aligned features of water quality samples, the water quality prediction models based on an echo state network at multiple nearby monitoring points are established with distributed computing, and then the prediction results of distributed water quality prediction models are integrated. Second, the prediction parameters of multi-source transfer learning are optimized. Specifically, the back propagates population deviation based on multiple iterations, reducing the feature alignment bias and the model alignment bias to improve the prediction accuracy. Finally, the proposed method is applied in the actual water quality dataset of Hong Kong. The experimental results demonstrate that the proposed method can make full use of the water quality information of multiple nearby monitoring points to train several water quality prediction models and reduce the prediction bias.

A Generalized Wine Quality Prediction Framework by Evolutionary Algorithms

A Generalized Wine Quality Prediction Framework by Evolutionary Algorithms

Quality prediction and control of cable harness wiring using extension theory and a backpropagation neural network

Given that the cable harness wiring quality (CHWQ) of complex mechatronic products is affected by multiple factors and it is difficult to solve the resulting control problems, this paper proposes a quality prediction method for cable harness wiring using extension theory and a backpropagation (BP) neural network. First, a quality prediction framework is designed based on the factors influencing the composition and analysis of the CHWQ. Second, we establish a quality evaluation index system based on five aspects and design a first-level factor set and a second-level factor set. Based on the single factor index for various parameters and evaluation of data after dimensionless parameter processing, we use extension theory and the entropy weight method to determine the membership matrix and the fuzzy weight vector for the first-level evaluation. Additionally, the single factor and the synthetic fuzzy weight vector are determined using the entropy weight method and the analytic hierarchy process (AHP), respectively, and quality grade evaluation based on the fuzzy synthetic evaluation (FSE) method is completed. Finally, we use a three-layer feedforward neural network to predict the quality status of a specific phased array radar system. The results demonstrate that the proposed method can produce increased prediction accuracy.

An adversarial bidirectional serial–parallel LSTM-based QTD framework for product quality prediction

In order to capture temporal interactions among processes in manufacturing and assembly processes, an end-to-end unified product quality prediction framework called QTD is proposed in this paper. It consists of three modules: quality embedding model pool, temporal-interactive model, and decoding model. Besides, to handle the information transfer and integration problems in the time direction of parallel processes, a novel bidirectional serial–parallel LSTM (Bi-SP-LSTM) is devised as an instantiated model of temporal-interactive model. Bi-SP-LSTM is an extension of bidirectional long short-term memory. Moreover, an unsupervised task and a loss function named adversarial focal loss have been designed to give the framework the ability to assess heteroscedastic uncertainty in classification task due to intrinsic uncertainty in data. Furthermore, experiments are devised based on a subset of a public dataset from Kaggle competition to demonstrate the validity of the proposed framework. Compared with other latest methods, the proposed framework is verified to be more accurate and robust. Taking Matthews correlation coefficient as an example, the adversarial Bi-SP-LSTM-based QTD framework is superior to the best existing methods with 95% confidence interval in most cases, and its mean MCC is 4.88% higher than the best existing method. The results suggest that the proposed framework has a broad application prospect for quality prediction in manufacturing and assembly processes.

A New Air Quality Prediction Framework for Airports Developed with a Hybrid Supervised Learning Method

In order to reduce the air pollution impacts by aircraft operations around airports, a fast and accurate prediction of air quality related to aircraft operations is an essential prerequisite. This article proposes a new framework with a combination of the standard assessment procedure and machine learning methods for fast and accurate prediction of air quality in airports. Instead of taking some specific pollutant as concerned metric, we introduce the air quality index (AQI) for the first time to evaluate the air quality in airports. Then, following the standard assessment procedure proposed by International Civil Aviation Organization (ICAO), the airports AQIs in different scenarios are classified with consideration of the airport configuration, actual flight operations, aircraft performance, and related meteorological data. Taking the AQI classification results as sample data, several popular supervised learning methods are investigated for accurately predicting air quality in airports. The numerical tests implicate that the accuracy rate of prediction could reach more than 95% with only 0.022 sec; the proposed framework and the results could be used as the foundation for improving air quality impacts around airports.

Spatio-temporal learning in predicting ambient particulate matter concentration by multi-layer perceptron

In this work, a novel spatio-temporal air quality prediction framework is proposed, and its development and efficacy as a predictive tool are described. The framework exploits data from the suite of models of the Copernicus Atmosphere Monitoring Service (CAMS), fed to an artificial neural network for the removal of bias. The method inherently considers spatial and temporal correlations, because it is applied simultaneously to all monitoring stations of a given region, using past observations and past and future forecasts. The methodology is tested on twelve months of CAMS forecasts of daily surface particulate matter (PM10) in 2017 and is verified against observations measured at 413 monitoring stations from the Italian air quality network.The raw data from the CAMS system, although they contain valuable information, show very poor performance, due to a large negative bias that does not allow the correct prediction of critical conditions. The model bias is found to have a strong seasonal dependency, with a large positive bias in winter and a small negative bias during summer months. The correction applied through the neural model allows to correct the original predictions and to practically eliminate the bias, increasing the performance forecast even to four days ahead. It is concluded that neural networks can be used to develop reliable air quality early-warning systems based on a network of automated monitoring stations and real-time ensemble predictions from deterministic models.

Local and global sparse representation for no-reference quality assessment of stereoscopic images

No-reference/blind quality assessment of stereoscopic 3D images is much more challenging than 2D images due to the poor understanding of binocular vision. In this paper, we propose a BLind Quality Evaluator for stereoscopic 3D images by learning Local and Global Sparse Representations (BLQELGSR). Specifically, at the training stage, we first construct a large-scale training set by simulating some common distortions that are likely encountered by stereoscopic images, and propose a multi-modal sparse representation framework to characterize the relationship between the feature and quality spaces for all sources of information from left, right and cyclopean views in local and global manners. At the testing stage, based on the derived 3D quality prediction framework, the local and global quality scores from different sources are predicted and combined to drive a final 3D quality score. Experimental results on three 3D image quality databases show that in comparison with the existing methods, the devised BLQELGSR can achieve better prediction performance to be in line with subjective assessment.

제조 온톨로지 기반 품질예측 프레임워크 및 시스템 개발 : 사출성형공정 사례

Today, many manufacturing companies realize that collaboration is crucial for their survival. Especially, in the perspective of quality, the importance of collaboration is emphasized because economic loss increases exponentially while defective parts go through the process in supply chain. However, the manufacturing companies are facing two main difficulties in implementing collaborative relationships with their suppliers. First, it is difficult for the suppliers to produce reliable products due to their obsolete facilities. The problem gets worse for second- or third-tire vendors. Second, the companies experience the lack of universally understandable set of terminology and effective methodologies for knowledge representation. Ontology is one of the best approaches to expressing and processing a domain knowledge. In this paper, we propose the manufacturing ontology-based quality prediction framework to represent and share the knowledge of industrial environment and to predict product quality in manufacturing processes. In addition, we develop the ontology-based quality prediction system based on the proposed framework. We carried out a series of experiments for an injection molding process at an automotive part supplier. The experimental results demonstrated that the proposed framework and system can be successfully applicable in manufacturing industry.

Improved Batch Process Monitoring and Quality Prediction Based on Multiphase Statistical Analysis

An integrated online process monitoring, fault diagnosis, and quality prediction framework based on multiple models is developed for multiphase batch processes. Two types of different-level PLS models are designed for each phase. In the first level, multiple PLS models based on variable unfolding, termed MVPLS, are employed for online monitoring and revealing the dynamic progressing direction that the quality will take at each time. In each phase, MVPLS model is established by performing PLS on variablewise data rearrangement, which can focus on the process information more relevant to the final quality from an overall phase-specific point of view. Moreover, control limits of quality prediction are explored and, thus, employed in process monitoring. Combined with conventional Hotelling-T2 and SPE statistics, they can enhance the process understanding and monitoring performance. Once an abnormality is detected, the contributions of process variables to quality prediction are calculated in combination with ...

A Quality Prediction Framework for Multistage Machining Processes Driven by an Engineering Model and Variation Propagation Model

This paper proposes a comprehensive quality prediction framework for multistage machining processes, connecting engineering design with the activities of quality modeling, variation propagation modeling and calculation, dimensional variation evaluation, dimensional variation analysis, and quality feedback. Presented is an integrated information model utilizing a hybrid (feature/point-based) dimensional accuracy and variation quality modeling approach that incorporates Monte Carlo simulation, variation propagation, and regression modeling algorithms. Two important variations (kinematic and static) for the workpiece, machine tool, fixture, and machining processes are considered. The objective of the framework is to support the development of a quality prediction and analysis software tool that is efficient in predicting part dimensional quality in a multistage machining system (serial, parallel, or hybrid) from station level to system level.