Pub Date : 2022-08-24DOI: 10.1109/IAI55780.2022.9976699
Fan Wu, Lei Hao, Hongfeng Wang
Compared to traditional material yards with simple supply requirements and centralized material storage, intelligent material yards can significantly reduce storage space, improve material pickup efficiency, and reduce additional costs due to material mutual contamination. However, the current material delivery process is still dominated by a manual decision-making model, which is difficult to adapt to the complex and changing supply requirements. To this end, an integrated scheduling problem of material pickup and delivery considering multi-factory order requirements is proposed in this paper, which originates from a real-world scenario of Binxin intelligent material yard. By introducing the concept of spatio-temporal network flow, a discrete time-based integer linear programming model is established and then the CPLEX solver is used to solve the model. Compared with the traditional continuous-time based model, the established model shows significant advantages in terms of both solution quality and solution time, which can greatly improve the overall efficiency of the Binxin intelligent material yard.
{"title":"Joint Scheduling of Material Pickup and Delivery Towards Intelligent Material Yard","authors":"Fan Wu, Lei Hao, Hongfeng Wang","doi":"10.1109/IAI55780.2022.9976699","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976699","url":null,"abstract":"Compared to traditional material yards with simple supply requirements and centralized material storage, intelligent material yards can significantly reduce storage space, improve material pickup efficiency, and reduce additional costs due to material mutual contamination. However, the current material delivery process is still dominated by a manual decision-making model, which is difficult to adapt to the complex and changing supply requirements. To this end, an integrated scheduling problem of material pickup and delivery considering multi-factory order requirements is proposed in this paper, which originates from a real-world scenario of Binxin intelligent material yard. By introducing the concept of spatio-temporal network flow, a discrete time-based integer linear programming model is established and then the CPLEX solver is used to solve the model. Compared with the traditional continuous-time based model, the established model shows significant advantages in terms of both solution quality and solution time, which can greatly improve the overall efficiency of the Binxin intelligent material yard.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128297240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-24DOI: 10.1109/IAI55780.2022.9976589
Junguo Song, Jin‐Xi Zhang
This paper designs an output tracking controller for a class of uncertain second-order nonlinear systems with input quantization to solve the prescribed performance control problem. The performance function restrains the convergence rate and precision of the output tracking error. The barrier function is used to confine this error. A simple input quantizer is specially designed for the controller. The resulting control strategy ensures that the prescribed output tracking performance is achieved and all the closed-loop signals are bounded. The control strategy is verified through the simulation result.
{"title":"Quantized Prescribed Performance Control for Second-Order Nonlinear Systems","authors":"Junguo Song, Jin‐Xi Zhang","doi":"10.1109/IAI55780.2022.9976589","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976589","url":null,"abstract":"This paper designs an output tracking controller for a class of uncertain second-order nonlinear systems with input quantization to solve the prescribed performance control problem. The performance function restrains the convergence rate and precision of the output tracking error. The barrier function is used to confine this error. A simple input quantizer is specially designed for the controller. The resulting control strategy ensures that the prescribed output tracking performance is achieved and all the closed-loop signals are bounded. The control strategy is verified through the simulation result.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125296760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-24DOI: 10.1109/IAI55780.2022.9976704
Yichen Zhong, Zhe Zhang, Gaochang Wu
Fused magnesium furnace (FMF) is an important equipment for producing magnesium oxide, which is prone to occurring the semi-molten abnormal condition during the production. If the abnormal condition is not predicted in time, the furnace shell will be burned through, endangering the personal safety of the staff on site. Therefore, it is necessary to predict the semi-molten abnormal condition in time and accurately. Existing machine learning-based methods adopt static models for recognizing and predicting anomaly. However, the model accuracy will degrade as data features shifting over time and melting processes. To address the above problems, this paper proposes a dynamic prediction method for semi-molten abnormal condition of multiple FMFs based on semi-supervised learning. We introduce a consistent regularization strategy and dynamically update the model weights by learning multiple FMF smelting process video data with a sparse set of condition labels. The algorithm is able to dynamically adapt to the shifted data features for accurate anomaly prediction. The proposed algorithm can predict the semi-molten abnormal condition in real time and accurately under the condition of only 1% label, enabling the safe and reliable operation of FMF.
{"title":"A Semi-Supervised Learning-based Dynamic Prediction Method for Semi-molten Condition of Fused Magnesium Furnace","authors":"Yichen Zhong, Zhe Zhang, Gaochang Wu","doi":"10.1109/IAI55780.2022.9976704","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976704","url":null,"abstract":"Fused magnesium furnace (FMF) is an important equipment for producing magnesium oxide, which is prone to occurring the semi-molten abnormal condition during the production. If the abnormal condition is not predicted in time, the furnace shell will be burned through, endangering the personal safety of the staff on site. Therefore, it is necessary to predict the semi-molten abnormal condition in time and accurately. Existing machine learning-based methods adopt static models for recognizing and predicting anomaly. However, the model accuracy will degrade as data features shifting over time and melting processes. To address the above problems, this paper proposes a dynamic prediction method for semi-molten abnormal condition of multiple FMFs based on semi-supervised learning. We introduce a consistent regularization strategy and dynamically update the model weights by learning multiple FMF smelting process video data with a sparse set of condition labels. The algorithm is able to dynamically adapt to the shifted data features for accurate anomaly prediction. The proposed algorithm can predict the semi-molten abnormal condition in real time and accurately under the condition of only 1% label, enabling the safe and reliable operation of FMF.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122522059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-24DOI: 10.1109/IAI55780.2022.9976809
J. Leventides, E. Melas, C. Poulios, A. Vardulakis
We present a method for linearizing control and stabilization of chaotic systems in finance. This method considers the deviation of some trajectory of the system from an ideal or desirable orbit. Using Koopman operators and EDMD, we model this deviation as a linear dynamical system. The linear system is necessarily defined in some augmented state space whose dimension is bigger than the dimension of the original state space. The linear system can then be used for control and stabilization properties. Namely, one may apply feedback control to drive the deviation to zero, which means that the trajectory is close to the desired one. This approach can also be applied to more than one trajectories. However, in order to maintain good approximation properties, the more trajectories we consider the larger the dimensions of the linear system will become and at some stage the method will not be computationally effective. For this reason, we do not take into consideration the whole set of trajectories, but we start with a smaller set of orbits. This is a realistic scenario, since in economic studies the macroeconomic variables (such as the gross domestic product) are not arbitrary numbers but depend on the data of the economy.
{"title":"Data arising from hyperchaotic financial systems. Control through Koopman operators and EDMD","authors":"J. Leventides, E. Melas, C. Poulios, A. Vardulakis","doi":"10.1109/IAI55780.2022.9976809","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976809","url":null,"abstract":"We present a method for linearizing control and stabilization of chaotic systems in finance. This method considers the deviation of some trajectory of the system from an ideal or desirable orbit. Using Koopman operators and EDMD, we model this deviation as a linear dynamical system. The linear system is necessarily defined in some augmented state space whose dimension is bigger than the dimension of the original state space. The linear system can then be used for control and stabilization properties. Namely, one may apply feedback control to drive the deviation to zero, which means that the trajectory is close to the desired one. This approach can also be applied to more than one trajectories. However, in order to maintain good approximation properties, the more trajectories we consider the larger the dimensions of the linear system will become and at some stage the method will not be computationally effective. For this reason, we do not take into consideration the whole set of trajectories, but we start with a smaller set of orbits. This is a realistic scenario, since in economic studies the macroeconomic variables (such as the gross domestic product) are not arbitrary numbers but depend on the data of the economy.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124163673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Short-term power load forecasting is an important guarantee to ensure the smooth and efficient operation of power systems, and an important basis for building new digital and intelligent power systems. Given that short-term power system load is affected by various factors (e.g., climate, time), power system load has strong randomness and volatility while being periodic. Hence, the traditional power load forecasting method is no longer applicable. To improve the accuracy of short-term power load forecasting, this paper proposes a support vector machine (SVM) short-term power load forecasting method based on grey relational analysis and K-means clustering. First, similar days in historical days are extracted by using the grey relational analysis method to form a rough set of similar days. Second, the rough set of similar days is classified by K-means clustering, and the final set of similar days is obtained. Third, SVM is trained to determine the final predicted daily load. Lastly, the proposed method is verified by the actual electricity consumption data of a city in China, and the results show the effectiveness of this method.
{"title":"Short-term Power Load Forecasting Based on Grey Relational Analysis and Support Vector Machine","authors":"Wei Sun, Xinfu Pang, Wei Liu, Yibao Wang, Changfeng Luan","doi":"10.1109/IAI55780.2022.9976828","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976828","url":null,"abstract":"Short-term power load forecasting is an important guarantee to ensure the smooth and efficient operation of power systems, and an important basis for building new digital and intelligent power systems. Given that short-term power system load is affected by various factors (e.g., climate, time), power system load has strong randomness and volatility while being periodic. Hence, the traditional power load forecasting method is no longer applicable. To improve the accuracy of short-term power load forecasting, this paper proposes a support vector machine (SVM) short-term power load forecasting method based on grey relational analysis and K-means clustering. First, similar days in historical days are extracted by using the grey relational analysis method to form a rough set of similar days. Second, the rough set of similar days is classified by K-means clustering, and the final set of similar days is obtained. Third, SVM is trained to determine the final predicted daily load. Lastly, the proposed method is verified by the actual electricity consumption data of a city in China, and the results show the effectiveness of this method.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126351024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-24DOI: 10.1109/IAI55780.2022.9976758
Shupeng Yu, Xiang Li, Bin Yang, Y. Lei
The rolling bearing is essential for the rotating machinery and can be easily damaged in the real working conditions. It is very important to monitor the health status of rolling bearings. Aiming at this problem, fault diagnosis based on deep learning at present is popular, which automatically extracts features from raw data. However, the accuracy of fault diagnosis based on deep learning is dependent mostly on the quantity of data. In the real industries, a large amount of data may not be available, which largely deteriorates the performance of deep learning. To solve this problem, it is promising to exploit the features extracted with the expert knowledge for relaxing the limitations of deep learning. In this paper, a new hybrid intelligent method for rolling fault diagnosis is proposed, which is integrated with deep convolutional neural network and the expert knowledge. The features extracted with expert knowledge are used to improve the feature learning effect and efficiency of deep learning. The experiments on the Case Western Reserve University (CWRU) bearing data validate the effectiveness of the proposed hybrid rolling bearing fault diagnosis method.
{"title":"A Hybrid Intelligent Method for Rolling Bearing Fault Diagnosis Integrated with Expert Knowledge and Deep Learning","authors":"Shupeng Yu, Xiang Li, Bin Yang, Y. Lei","doi":"10.1109/IAI55780.2022.9976758","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976758","url":null,"abstract":"The rolling bearing is essential for the rotating machinery and can be easily damaged in the real working conditions. It is very important to monitor the health status of rolling bearings. Aiming at this problem, fault diagnosis based on deep learning at present is popular, which automatically extracts features from raw data. However, the accuracy of fault diagnosis based on deep learning is dependent mostly on the quantity of data. In the real industries, a large amount of data may not be available, which largely deteriorates the performance of deep learning. To solve this problem, it is promising to exploit the features extracted with the expert knowledge for relaxing the limitations of deep learning. In this paper, a new hybrid intelligent method for rolling fault diagnosis is proposed, which is integrated with deep convolutional neural network and the expert knowledge. The features extracted with expert knowledge are used to improve the feature learning effect and efficiency of deep learning. The experiments on the Case Western Reserve University (CWRU) bearing data validate the effectiveness of the proposed hybrid rolling bearing fault diagnosis method.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"279 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132833959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The fusion formation coefficient at the bottom of the weld bead is a key parameter to characterize the formation of a single-pass weld in ultra-narrow gap welding, and it is also an important content of welding quality control. Combined with the characteristics of the ultra-narrow gap welding method and the welding process, 14 characteristic parameters affecting the forming coefficient were extracted from the welding process signal and pre-welding preset parameters, and a convolutional neural network and a bidirectional long-short-term memory network (CNN-BILSTM-Attention) were established.) of the welding bead fusion forming coefficient prediction model, the results show that the model can effectively predict the welding bead fusion forming coefficient, and the mean square error of the prediction reaches 0.017, which provides a basis for further online control of welding quality.
{"title":"Research on prediction method of fusion forming coefficient at the bottom of ultra-narrow gap weld bead","authors":"Qian Ma, A. Zhang, Jing Ma, Yongqiang Ma, Yajun Zhang, Tingting Liang","doi":"10.1109/IAI55780.2022.9976604","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976604","url":null,"abstract":"The fusion formation coefficient at the bottom of the weld bead is a key parameter to characterize the formation of a single-pass weld in ultra-narrow gap welding, and it is also an important content of welding quality control. Combined with the characteristics of the ultra-narrow gap welding method and the welding process, 14 characteristic parameters affecting the forming coefficient were extracted from the welding process signal and pre-welding preset parameters, and a convolutional neural network and a bidirectional long-short-term memory network (CNN-BILSTM-Attention) were established.) of the welding bead fusion forming coefficient prediction model, the results show that the model can effectively predict the welding bead fusion forming coefficient, and the mean square error of the prediction reaches 0.017, which provides a basis for further online control of welding quality.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129068245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-24DOI: 10.1109/IAI55780.2022.9976643
Yijun Xiao, Hui Lv, Xing’an Wang
In this article, a special attention is paid to the biochemical controller synthesis for time delay systems and try to implement the well-established Smith predictor approach in the context of biochemical systems. Then, chemical reaction networks (CRNs) are adopted to construct a modified Smith predictor scheme (integrating Smith predictor and feedback compensation controllers) for the first time. Taking a delayed protein translation model as the background, the CRNs-based proposed scheme has access to a method that can solve the effect of co-translated mRNA decay in protein translation. In addition, considering that the decay of mRNA affects mRNA stability and protein production, the co-translated mRNA degradation is treated as an interference input of the protein translation process. Our results show that the impact of a disturbance input (mRNA degradation) is restrained by the modified control strategy. The CRNs-based modified Smith predictor makes the protein translation process more robust and achieves protein output quickly and stably.
{"title":"Implementing a modified Smith predictor using chemical reaction networks and its application to protein translation","authors":"Yijun Xiao, Hui Lv, Xing’an Wang","doi":"10.1109/IAI55780.2022.9976643","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976643","url":null,"abstract":"In this article, a special attention is paid to the biochemical controller synthesis for time delay systems and try to implement the well-established Smith predictor approach in the context of biochemical systems. Then, chemical reaction networks (CRNs) are adopted to construct a modified Smith predictor scheme (integrating Smith predictor and feedback compensation controllers) for the first time. Taking a delayed protein translation model as the background, the CRNs-based proposed scheme has access to a method that can solve the effect of co-translated mRNA decay in protein translation. In addition, considering that the decay of mRNA affects mRNA stability and protein production, the co-translated mRNA degradation is treated as an interference input of the protein translation process. Our results show that the impact of a disturbance input (mRNA degradation) is restrained by the modified control strategy. The CRNs-based modified Smith predictor makes the protein translation process more robust and achieves protein output quickly and stably.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131568068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-24DOI: 10.1109/IAI55780.2022.9976837
J. Leventides, E. Melas, C. Poulios
In this paper, we consider bilinear compartmental models. Using the Koopman operator in connection with the Extended Dynamic Mode Decomposition (EDMD), we try to obtain a linear approximation of the original system in a vector space whose dimension is bigger than the original state space. This approach is based on the choice of a dictionary of observables. In the case of bilinear compartmental models there is a natural choice of observables. We present this choice and we examine the efficiency of the method. Especially, we focus on the SIR model which is used to describe the transmission of a disease through some population.
{"title":"EDMD methods for analysis and prediction of bilinear compartmental models","authors":"J. Leventides, E. Melas, C. Poulios","doi":"10.1109/IAI55780.2022.9976837","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976837","url":null,"abstract":"In this paper, we consider bilinear compartmental models. Using the Koopman operator in connection with the Extended Dynamic Mode Decomposition (EDMD), we try to obtain a linear approximation of the original system in a vector space whose dimension is bigger than the original state space. This approach is based on the choice of a dictionary of observables. In the case of bilinear compartmental models there is a natural choice of observables. We present this choice and we examine the efficiency of the method. Especially, we focus on the SIR model which is used to describe the transmission of a disease through some population.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124565599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-24DOI: 10.1109/IAI55780.2022.9976740
Zhuocheng Wang, Cuimei Bo, Zheng Sun, Jun Li, F. Gao
In plastic injection molding (PIM), the process parameters affect the quality and productivity of molded parts. In this paper, we use orthogonal experiment design, numerical simulation, and metamodeling method to analyze the quality defect of process. The orthogonal experiment is to generate sampling points from the design space at different parameter levels and to determine key factors that affect product quality. For the sampling points, the numerical simulation is implemented to calculate the objective responses. Based on the sampling points and their corresponding response, a gradient enhanced Kriging (GEK) surrogate model strategy is applied to construct the response predictors to calculate the objective responses in the global design space. Last, we can analyze the surrogate model to look for available process parameters to improve product quality and production efficiency.
{"title":"Quality defect analysis of injection molding based on gradient enhanced Kriging model","authors":"Zhuocheng Wang, Cuimei Bo, Zheng Sun, Jun Li, F. Gao","doi":"10.1109/IAI55780.2022.9976740","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976740","url":null,"abstract":"In plastic injection molding (PIM), the process parameters affect the quality and productivity of molded parts. In this paper, we use orthogonal experiment design, numerical simulation, and metamodeling method to analyze the quality defect of process. The orthogonal experiment is to generate sampling points from the design space at different parameter levels and to determine key factors that affect product quality. For the sampling points, the numerical simulation is implemented to calculate the objective responses. Based on the sampling points and their corresponding response, a gradient enhanced Kriging (GEK) surrogate model strategy is applied to construct the response predictors to calculate the objective responses in the global design space. Last, we can analyze the surrogate model to look for available process parameters to improve product quality and production efficiency.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114581229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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