This paper analyzes the process variable data from injection molding processes to identify the key process variables, which can be predicted by other process variables, which highlights the interdependence among diffe...
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This paper analyzes the process variable data from injection molding processes to identify the key process variables, which can be predicted by other process variables, which highlights the interdependence among different process variables in various production scenarios. The available data from injection molding machines provide information for the run-time, setup parameters of machines, and measurements of different process variables through sensors. For predictive modeling, we employed different linear regression models with the recursive backward feature selection and SVM regression models using a radial kernel to predict nonlinear process variables. We also applied the linear and SVM regression models for outlier data in the process variables assuming that the upper bound outliers represent the perturbed state of process variables during production. These perturbations are affected by material type, machine type (age and performance), regime changes, or other external effects and subsequently affect the predictability of process variables and production output. Such cases are different compared to the normal or controlled range of process variables. Our analysis shows that the predictability varies for different material types due to the interdependence of the associated process variables. We further highlight that various process variables exhibit nonlinear relationships and cannot be predicted using linear models. We additionally look for the interdependence of process variables used previously by three studies as input features to predict product quality. (C) 2021 The Authors. Published by Elsevier B.V.
This paper analyzes the process variable data from injection molding processes to identify the key process variables, which can be predicted by other process variables, which highlights the interdependence among diffe...
详细信息
This paper analyzes the process variable data from injection molding processes to identify the key process variables, which can be predicted by other process variables, which highlights the interdependence among different process variables in various production scenarios. The available data from injection molding machines provide information for the run-time, setup parameters of machines, and measurements of different process variables through sensors. For predictive modeling, we employed different linear regression models with the recursive backward feature selection and SVM regression models using a radial kernel to predict nonlinear process variables. We also applied the linear and SVM regression models for outlier data in the process variables assuming that the upper bound outliers represent the perturbed state of process variables during production. These perturbations are affected by material type, machine type (age and performance), regime changes, or other external effects and subsequently affect the predictability of process variables and production output. Such cases are different compared to the normal or controlled range of process variables. Our analysis shows that the predictability varies for different material types due to the interdependence of the associated process variables. We further highlight that various process variables exhibit nonlinear relationships and cannot be predicted using linear models. We additionally look for the interdependence of process variables used previously by three studies as input features to predict product quality.
Product and process quality is playing an increasingly important role in the competitive success of manufacturing companies. To ensure a high quality level of the produced parts, the appropriate selection of parameter...
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ISBN:
(数字)9783030266363
ISBN:
(纸本)9783030266356;9783030266363
Product and process quality is playing an increasingly important role in the competitive success of manufacturing companies. To ensure a high quality level of the produced parts, the appropriate selection of parameters in manufacturing processes plays in important role. Traditional approaches for parametersetting rely on rule-based schemes, expertise and domain knowledge of highly skilled workers or trial and error. Automated and real-time adjustment of critical processparameters, based on the individual properties of a part and its previous production conditions, have the potential to reduce scrap and increase the quality. Different machine learning methods can be applied for generating parameter estimation models based on experimental data. In this paper, we present a comparison of linear and symbolic regression methods for an adaptive parametersetting approach. Based on comprehensive real-world data, collected in a long-term study, multiple models are generated, evaluated and compared with regard to their applicability in the studied approach for parametersetting in manufacturing processes.
Purpose This paper aims to discuss additive manufacturing (AM) in the context of applications for musical instruments. It examines the main AM technologies used in musical instruments, goes through a history of musica...
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Purpose This paper aims to discuss additive manufacturing (AM) in the context of applications for musical instruments. It examines the main AM technologies used in musical instruments, goes through a history of musical applications of AM and raises the questions about the application of AM to create completely new wind instruments that would be impossible to produce with conventional manufacturing. Design/methodology/approach A literature research is presented which covers a historical application of AM to musical instruments and hypothesizes on some potential new applications. Findings AM has found extensive application to create conventional musical instruments with unique aesthetics designs. It's true potential to create entirely new sounds, however, remains largely untapped. Research limitations/implications More research is needed to truly assess the potential of additive manufacturing to create entirely new sounds for musical instrument. Practical implications The application of AM in music could herald an entirely new class of musical instruments with unique sounds. Originality/value This study highlights musical instruments as an unusual application of AM. It highlights the potential of AM to create entirely new sounds, which could create a whole new class of musical instruments.
Reactive ion etching (RIE) is a process in the fabrication of semiconductor devices. The ability to predict the influence of the processparameters of RIE is crucial in terms of machine performance as they may have a ...
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Reactive ion etching (RIE) is a process in the fabrication of semiconductor devices. The ability to predict the influence of the processparameters of RIE is crucial in terms of machine performance as they may have a serious impact on product quality as well as on the probability of machine failure. To address this issue, this correspondence paper presents a novel performance tradeoff function for evaluating the overall suitability of adopting the predicted control parameters suggested by domain experts, taking into full consideration their impact on the performance of the machine involved. An experiment using the RIE machine is provided to validate the practicability of the proposed approach.
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