17-4PH Stainless Steel is a mechanically high-performing alloy that is widely used across chemical and mechanical processing industries. The alloy is conventionally fabricated by cast methods, but emerging additive manufacturing techniques are presently offering an economic, efficient, and environmentally friendly alternative. Bound Powder Extrusion (BPE) is a relatively new additive manufacturing technique that is used to fabricate three-dimensional, free-form components. Investigation into the mechanical properties and behavior of 17-4PH stainless steel fabricated by BPE is vital to understanding whether this technique proposes a competitive substitute to the cast alloy within industry. Published literature has investigated the as-fabricated mechanical properties, microstructure, porosity, and post-processing heat treatment of the BPE alloy, with limited comparison evident among the papers. This paper, therefore, aims to review published findings on the mechanical properties of 17-4PH stainless steel produced by additive manufacturing techniques, with a key focus on BPE. It is important to highlight that this review study focuses on the MetalXTM 3D printer, manufactured by Markforged. This printer is among the widely utilized BPE 3D printers available in the market. The key results, together with the impact of post-heat treatments, were discussed and compared to provide a more comprehensive picture of the patterns that this alloy presents in terms of its microstructure and mechanical properties. This enables the manufacture of components relative to desired material performance, improving overall functionality. A comparison of yield strength, ultimate tensile strength (UTS), Young’s modulus, ductility, and hardness was made relative to microstructure, porosity, and density of published literature for the as-fabricated and post-heat-treated states, identifying areas for further research.
{"title":"A Review of the Mechanical Properties of 17-4PH Stainless Steel Produced by Bound Powder Extrusion","authors":"Jaidyn Jones, Ana Vafadar, Reza Hashemi","doi":"10.3390/jmmp7050162","DOIUrl":"https://doi.org/10.3390/jmmp7050162","url":null,"abstract":"17-4PH Stainless Steel is a mechanically high-performing alloy that is widely used across chemical and mechanical processing industries. The alloy is conventionally fabricated by cast methods, but emerging additive manufacturing techniques are presently offering an economic, efficient, and environmentally friendly alternative. Bound Powder Extrusion (BPE) is a relatively new additive manufacturing technique that is used to fabricate three-dimensional, free-form components. Investigation into the mechanical properties and behavior of 17-4PH stainless steel fabricated by BPE is vital to understanding whether this technique proposes a competitive substitute to the cast alloy within industry. Published literature has investigated the as-fabricated mechanical properties, microstructure, porosity, and post-processing heat treatment of the BPE alloy, with limited comparison evident among the papers. This paper, therefore, aims to review published findings on the mechanical properties of 17-4PH stainless steel produced by additive manufacturing techniques, with a key focus on BPE. It is important to highlight that this review study focuses on the MetalXTM 3D printer, manufactured by Markforged. This printer is among the widely utilized BPE 3D printers available in the market. The key results, together with the impact of post-heat treatments, were discussed and compared to provide a more comprehensive picture of the patterns that this alloy presents in terms of its microstructure and mechanical properties. This enables the manufacture of components relative to desired material performance, improving overall functionality. A comparison of yield strength, ultimate tensile strength (UTS), Young’s modulus, ductility, and hardness was made relative to microstructure, porosity, and density of published literature for the as-fabricated and post-heat-treated states, identifying areas for further research.","PeriodicalId":16319,"journal":{"name":"Journal of Manufacturing and Materials Processing","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43513971","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}
Micro-EDM is an unconventional technology used to machine every type of electrically conductive material regardless of its mechanical properties. Material removal occurs through electrical discharges between the workpiece and the electrode immersed in a dielectric fluid. In drilling operations, the technology is able to realise microholes with excellent quality in terms of precision, quality surface, roundness, and taper to the detriment of the machining time, which is less than other technologies. Several efforts are being made to improve different features related to the process performance that are severely affected by both the operative conditions, such as the electrode material or the type of dielectric, and process parameters. The typical indexes used to characterise the performance are the machining time, the material removal rate, and the geometric indexes. These indexes are very effective and are easily measurable, but they do not give information about the evolution of the drilling process, which could be irregular due to the different phenomena occurring during machining. The aim of this paper is the development of a method able to elaborate the motion law of the electrode during the micro-EDM drilling operation. In order to do this, a single hole was manufactured in several steps, recording both the machining time and electrode wear for each step. In this way, the actual position of the electrode during the drilling can be measured without the use of a predictive model for electrode wear. It was tested to confirm that the multistep procedure did not introduce new phenomena, in contrast to the traditional drilling operation. This method was used to study the effects of the electrode diameter, the type of electrode, the length of the electrode out of the spindle, and the entity of the run-out on the process performance. The tests were executed on titanium alloy sheets using a tungsten carbide electrode and hydrocarbon oil as the dielectric. It was found that the descent of the electrode into the workpiece was not regular, but it depended on the level of debris concentration in the machining zone. The debris concentration was influenced by the type and diameter of the electrode, its length out of the spindle, and, to a lesser extent, the run-out. This method was found to be a useful method for an in-depth analysis of the micro-EDM drilling process, contributing to a better understanding of the physical aspects of the process.
{"title":"Study of the Law Motion of the Micro-EDM Drilling Process","authors":"Giuseppe Pellegrini, Chiara Ravasio","doi":"10.3390/jmmp7050165","DOIUrl":"https://doi.org/10.3390/jmmp7050165","url":null,"abstract":"Micro-EDM is an unconventional technology used to machine every type of electrically conductive material regardless of its mechanical properties. Material removal occurs through electrical discharges between the workpiece and the electrode immersed in a dielectric fluid. In drilling operations, the technology is able to realise microholes with excellent quality in terms of precision, quality surface, roundness, and taper to the detriment of the machining time, which is less than other technologies. Several efforts are being made to improve different features related to the process performance that are severely affected by both the operative conditions, such as the electrode material or the type of dielectric, and process parameters. The typical indexes used to characterise the performance are the machining time, the material removal rate, and the geometric indexes. These indexes are very effective and are easily measurable, but they do not give information about the evolution of the drilling process, which could be irregular due to the different phenomena occurring during machining. The aim of this paper is the development of a method able to elaborate the motion law of the electrode during the micro-EDM drilling operation. In order to do this, a single hole was manufactured in several steps, recording both the machining time and electrode wear for each step. In this way, the actual position of the electrode during the drilling can be measured without the use of a predictive model for electrode wear. It was tested to confirm that the multistep procedure did not introduce new phenomena, in contrast to the traditional drilling operation. This method was used to study the effects of the electrode diameter, the type of electrode, the length of the electrode out of the spindle, and the entity of the run-out on the process performance. The tests were executed on titanium alloy sheets using a tungsten carbide electrode and hydrocarbon oil as the dielectric. It was found that the descent of the electrode into the workpiece was not regular, but it depended on the level of debris concentration in the machining zone. The debris concentration was influenced by the type and diameter of the electrode, its length out of the spindle, and, to a lesser extent, the run-out. This method was found to be a useful method for an in-depth analysis of the micro-EDM drilling process, contributing to a better understanding of the physical aspects of the process.","PeriodicalId":16319,"journal":{"name":"Journal of Manufacturing and Materials Processing","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136362214","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}
Md Mushfiqur Rahman, M. A. Farahani, Thorsten Wuest
In recent years, the advancement of Industry 4.0 and smart manufacturing has made a large amount of industrial process data attainable with the use of sensors installed on machines. This paper proposes an experimental predictive maintenance framework for an industrial drying hopper so that it can detect any unusual event in the hopper, which reduces the risk of erroneous fault diagnosis in the manufacturing shop floor. The experimental framework uses Deep Learning (DL) algorithms to classify Multivariate Time-Series (MTS) data into two categories—failure or unusual events and regular events—thus formulating the problem as a binary classification. The raw data extracted from the sensors contained missing values, suffered from imbalancedness, and were not labeled. Therefore, necessary preprocessing is performed to make them usable for DL algorithms and the dataset is self-labeled after defining the two categories precisely. To tackle the imbalanced data issue, data balancing techniques like ensemble learning with undersampling and Synthetic Minority Oversampling Technique (SMOTE) are used. Moreover, along with DL algorithms like Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), Machine Learning (ML) algorithms like Support Vector Machine (SVM) and K-nearest neighbor (KNN) have also been used to perform a comparative analysis on the results obtained from these algorithms. The result shows that CNN is arguably the best algorithm for classifying this dataset into two categories and outperforms other traditional approaches as well as deep learning algorithms.
{"title":"Multivariate Time-Series Classification of Critical Events from Industrial Drying Hopper Operations: A Deep Learning Approach","authors":"Md Mushfiqur Rahman, M. A. Farahani, Thorsten Wuest","doi":"10.3390/jmmp7050164","DOIUrl":"https://doi.org/10.3390/jmmp7050164","url":null,"abstract":"In recent years, the advancement of Industry 4.0 and smart manufacturing has made a large amount of industrial process data attainable with the use of sensors installed on machines. This paper proposes an experimental predictive maintenance framework for an industrial drying hopper so that it can detect any unusual event in the hopper, which reduces the risk of erroneous fault diagnosis in the manufacturing shop floor. The experimental framework uses Deep Learning (DL) algorithms to classify Multivariate Time-Series (MTS) data into two categories—failure or unusual events and regular events—thus formulating the problem as a binary classification. The raw data extracted from the sensors contained missing values, suffered from imbalancedness, and were not labeled. Therefore, necessary preprocessing is performed to make them usable for DL algorithms and the dataset is self-labeled after defining the two categories precisely. To tackle the imbalanced data issue, data balancing techniques like ensemble learning with undersampling and Synthetic Minority Oversampling Technique (SMOTE) are used. Moreover, along with DL algorithms like Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), Machine Learning (ML) algorithms like Support Vector Machine (SVM) and K-nearest neighbor (KNN) have also been used to perform a comparative analysis on the results obtained from these algorithms. The result shows that CNN is arguably the best algorithm for classifying this dataset into two categories and outperforms other traditional approaches as well as deep learning algorithms.","PeriodicalId":16319,"journal":{"name":"Journal of Manufacturing and Materials Processing","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47924428","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 present study investigated the dissimilar metal combination of SUS303 stainless steel (SS) and pure copper C19210 by utilizing a fiber pulse laser to perform lap welding. The weld quality was evaluated through metallurgical and mechanical examinations, including scanning electron microscopy (SEM), optical microscopy (OM), energy dispersive spectroscopy (EDS), as well as tensile and shear tests. The cross-section of the weld joints was observed to examine the penetration inside the molten zone of the pulse laser welding. The incomplete weld penetration depth was confirmed by analyzing the molten pool geometry, which indicated that the penetration depth was proportional to the pulse heat energy input. EDS analysis demonstrated that interdiffusion and dissolution of Cu and SS occurred inside the weld pool, although only a limited amount of Cu was melted. Microhardness (MH) exploration revealed the hardness of the molten zone was lower than that of the heat-affected zone (HAZ) on the SS side, while the hardness on the Cu side, closer to the molten zone, was higher. The results of the tensile test indicated that the fracture occurred in the HAZ on the Cu side, displaying a dimpled fracture mode characteristic of ductile fracture.
{"title":"Investigation of Single-Pulse Laser Welding of Dissimilar Metal Combination of Thin SUS303 SS and Cu","authors":"Ruining Huang, Xuehao Huang, Junqiang Feng","doi":"10.3390/jmmp7050161","DOIUrl":"https://doi.org/10.3390/jmmp7050161","url":null,"abstract":"The present study investigated the dissimilar metal combination of SUS303 stainless steel (SS) and pure copper C19210 by utilizing a fiber pulse laser to perform lap welding. The weld quality was evaluated through metallurgical and mechanical examinations, including scanning electron microscopy (SEM), optical microscopy (OM), energy dispersive spectroscopy (EDS), as well as tensile and shear tests. The cross-section of the weld joints was observed to examine the penetration inside the molten zone of the pulse laser welding. The incomplete weld penetration depth was confirmed by analyzing the molten pool geometry, which indicated that the penetration depth was proportional to the pulse heat energy input. EDS analysis demonstrated that interdiffusion and dissolution of Cu and SS occurred inside the weld pool, although only a limited amount of Cu was melted. Microhardness (MH) exploration revealed the hardness of the molten zone was lower than that of the heat-affected zone (HAZ) on the SS side, while the hardness on the Cu side, closer to the molten zone, was higher. The results of the tensile test indicated that the fracture occurred in the HAZ on the Cu side, displaying a dimpled fracture mode characteristic of ductile fracture.","PeriodicalId":16319,"journal":{"name":"Journal of Manufacturing and Materials Processing","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42634461","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}
A. T. Abbas, Neeraj Sharma, Essam A. Al-Bahkali, Vishal S. Sharma, Irfan Farooq, A. Elkaseer
Conventional mechanical machining of composite is a challenging task, and thus, electric discharge machining (EDM) was used for the processing of the developed material. The processing of developed composite using different electrodes on EDM generates different surface characteristics. In the current work, the effect of tool material on the surface characteristics, along with other input parameters, is investigated as per the experimental design. The experimental design followed is an RSM-based Box–Behnken design, and the input parameters in the current research are tool material, current, voltage, pulse-off time, and pulse-on time. Three levels of each parameter are selected, and 46 experiments are conducted. The surface roughness (Ra) is investigated for each experimental setting. The machine learning approach is used for the prediction of surface integrity by different techniques, namely Xgboost, random forest, and decision tree. Out of all the techniques, the Xgboost technique shows maximum accuracy as compared to other techniques. The analysis of variance of the predicted solutions is investigated. The empirical model is developed using RSM and is further solved with the help of a teaching learning-based algorithm (TLBO). The SR value predicted after RSM and integrated approach of RSM-ML-TLBO are 2.51 and 2.47 µm corresponding to Ton: 45 µs; Toff: 73 µs; SV:8V; I: 10A; tool: brass and Ton: 47 µs; Toff: 76 µs; SV:8V; I: 10A; tool: brass, respectively. The surface integrity at the optimized setting reveals the presence of microcracks, globules, deposited lumps, and sub-surface formation due to different amounts of discharge energy.
{"title":"A Machine Learning Perspective to the Investigation of Surface Integrity of Al/SiC/Gr Composite on EDM","authors":"A. T. Abbas, Neeraj Sharma, Essam A. Al-Bahkali, Vishal S. Sharma, Irfan Farooq, A. Elkaseer","doi":"10.3390/jmmp7050163","DOIUrl":"https://doi.org/10.3390/jmmp7050163","url":null,"abstract":"Conventional mechanical machining of composite is a challenging task, and thus, electric discharge machining (EDM) was used for the processing of the developed material. The processing of developed composite using different electrodes on EDM generates different surface characteristics. In the current work, the effect of tool material on the surface characteristics, along with other input parameters, is investigated as per the experimental design. The experimental design followed is an RSM-based Box–Behnken design, and the input parameters in the current research are tool material, current, voltage, pulse-off time, and pulse-on time. Three levels of each parameter are selected, and 46 experiments are conducted. The surface roughness (Ra) is investigated for each experimental setting. The machine learning approach is used for the prediction of surface integrity by different techniques, namely Xgboost, random forest, and decision tree. Out of all the techniques, the Xgboost technique shows maximum accuracy as compared to other techniques. The analysis of variance of the predicted solutions is investigated. The empirical model is developed using RSM and is further solved with the help of a teaching learning-based algorithm (TLBO). The SR value predicted after RSM and integrated approach of RSM-ML-TLBO are 2.51 and 2.47 µm corresponding to Ton: 45 µs; Toff: 73 µs; SV:8V; I: 10A; tool: brass and Ton: 47 µs; Toff: 76 µs; SV:8V; I: 10A; tool: brass, respectively. The surface integrity at the optimized setting reveals the presence of microcracks, globules, deposited lumps, and sub-surface formation due to different amounts of discharge energy.","PeriodicalId":16319,"journal":{"name":"Journal of Manufacturing and Materials Processing","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43271367","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}
U. Bhandari, Yehong Chen, H. Ding, Congyuan Zeng, Selami Emanet, P. Gradl, Shengmin Guo
Thermal conductivity (TC) is greatly influenced by the working temperature, microstructures, thermal processing (heat treatment) history and the composition of alloys. Due to computational costs and lengthy experimental procedures, obtaining the thermal conductivity for novel alloys, particularly parts made with additive manufacturing, is difficult and it is almost impossible to optimize the compositional space for an absolute targeted value of thermal conductivity. To address these difficulties, a machine learning method is explored to predict the TC of additive manufactured alloys. To accomplish this, an extensive thermal conductivity dataset for additively manufactured alloys was generated for several AM alloy families (nickel, copper, iron, cobalt-based) over various temperatures (300–1273 K). This unique dataset was used in training and validating machine learning models. Among the five different regression machine learning models trained with the dataset, extreme gradient boosting performs the best as compared with other models with an R2 score of 0.99. Furthermore, the accuracy of this model was tested using Inconel 718 and GRCop-42 fabricated with laser powder bed fusion-based additive manufacture, which have never been observed by the extreme gradient boosting model, and a good match between the experimental results and machine learning prediction was observed. The average mean error in predicting the thermal conductivity of Inconel 718 and GRCop-42 at different temperatures was 3.9% and 2.08%, respectively. This paper demonstrates that the thermal conductivity of novel AM alloys could be predicted quickly based on the dataset and the ML model.
{"title":"Machine-Learning-Based Thermal Conductivity Prediction for Additively Manufactured Alloys","authors":"U. Bhandari, Yehong Chen, H. Ding, Congyuan Zeng, Selami Emanet, P. Gradl, Shengmin Guo","doi":"10.3390/jmmp7050160","DOIUrl":"https://doi.org/10.3390/jmmp7050160","url":null,"abstract":"Thermal conductivity (TC) is greatly influenced by the working temperature, microstructures, thermal processing (heat treatment) history and the composition of alloys. Due to computational costs and lengthy experimental procedures, obtaining the thermal conductivity for novel alloys, particularly parts made with additive manufacturing, is difficult and it is almost impossible to optimize the compositional space for an absolute targeted value of thermal conductivity. To address these difficulties, a machine learning method is explored to predict the TC of additive manufactured alloys. To accomplish this, an extensive thermal conductivity dataset for additively manufactured alloys was generated for several AM alloy families (nickel, copper, iron, cobalt-based) over various temperatures (300–1273 K). This unique dataset was used in training and validating machine learning models. Among the five different regression machine learning models trained with the dataset, extreme gradient boosting performs the best as compared with other models with an R2 score of 0.99. Furthermore, the accuracy of this model was tested using Inconel 718 and GRCop-42 fabricated with laser powder bed fusion-based additive manufacture, which have never been observed by the extreme gradient boosting model, and a good match between the experimental results and machine learning prediction was observed. The average mean error in predicting the thermal conductivity of Inconel 718 and GRCop-42 at different temperatures was 3.9% and 2.08%, respectively. This paper demonstrates that the thermal conductivity of novel AM alloys could be predicted quickly based on the dataset and the ML model.","PeriodicalId":16319,"journal":{"name":"Journal of Manufacturing and Materials Processing","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41762038","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}
Yaroslav Frolov, O. Bobukh, A. Samsonenko, Florian Nürnberger
Efficient roll bonding for the manufacturing of clad strips not only requires surface activation but also is improved by a surface patterning to reduce the initial contact area. This increases contact stresses and facilitates a joining without an increasing rolling force. Experiments to pattern surfaces with deformable inlays during cold rolling for a subsequent bonding in low-oxygen atmosphere were carried out using two types of rolling mills, two types of inlays and two types of assemblies. Digital twins of selected experiments were created by means of the FE simulation software QForm UK 10.2.4. The main set of rolling parameters, which play a significant role during formation of the pattern shape considering deformation of the patterning tool, were investigated. The pilot roll bonding of patterned components under vacuum conditions, provided using vacuum sealer bags, allowed for an experimental realization of this approach. The concept technological chain of roll bonding in a low-oxygen or oxygen-free environment comprises the following stages: roll patterning; surface activation and sealing of the strips in a vacuum bag; subsequent roll bonding of the prepared strips inside the protective bag. The difference between the shape of the pattern created and the initial shape of the mesh insert can be quantitatively described by the change of its angle. This difference reaches maximum values when smaller rolls are used with increased rolling reductions. This maximum value is limited by the springback of the deformed insert; the limit is reached more easily if the inlay is not positioned on the rolling plane.
有效的轧制粘接不仅需要表面活化，而且还需要通过表面图案来减少初始接触面积。这增加了接触应力，有利于接合而不增加轧制力。采用两种类型的轧机、两种类型的嵌体和两种类型的组件，在低氧气氛下对冷轧过程中可变形嵌体表面进行了图案化实验。采用有限元模拟软件QForm UK 10.2.4对所选实验进行数字孪生。研究了在成形过程中对成形刀具变形有重要影响的主要轧制参数。在真空条件下，使用真空密封袋进行图案组件的试验辊粘合，允许这种方法的实验实现。低氧或无氧环境下轧辊粘合的概念技术链包括以下几个阶段:轧辊成型;在真空袋中对带材进行表面活化和密封;随后将准备好的带材卷接在保护袋内。生成的图案形状与网格插入的初始形状之间的差异可以通过其角度的变化来定量描述。当使用较小的轧辊并增加轧制压差时，这种差异达到最大值。这个最大值受到变形插入件回弹的限制;如果嵌体不在轧制平面上，则更容易达到极限。
{"title":"Patterning of Surfaces for Subsequent Roll Bonding in a Low-Oxygen Environment Using Deformable Mesh Inlays","authors":"Yaroslav Frolov, O. Bobukh, A. Samsonenko, Florian Nürnberger","doi":"10.3390/jmmp7050158","DOIUrl":"https://doi.org/10.3390/jmmp7050158","url":null,"abstract":"Efficient roll bonding for the manufacturing of clad strips not only requires surface activation but also is improved by a surface patterning to reduce the initial contact area. This increases contact stresses and facilitates a joining without an increasing rolling force. Experiments to pattern surfaces with deformable inlays during cold rolling for a subsequent bonding in low-oxygen atmosphere were carried out using two types of rolling mills, two types of inlays and two types of assemblies. Digital twins of selected experiments were created by means of the FE simulation software QForm UK 10.2.4. The main set of rolling parameters, which play a significant role during formation of the pattern shape considering deformation of the patterning tool, were investigated. The pilot roll bonding of patterned components under vacuum conditions, provided using vacuum sealer bags, allowed for an experimental realization of this approach. The concept technological chain of roll bonding in a low-oxygen or oxygen-free environment comprises the following stages: roll patterning; surface activation and sealing of the strips in a vacuum bag; subsequent roll bonding of the prepared strips inside the protective bag. The difference between the shape of the pattern created and the initial shape of the mesh insert can be quantitatively described by the change of its angle. This difference reaches maximum values when smaller rolls are used with increased rolling reductions. This maximum value is limited by the springback of the deformed insert; the limit is reached more easily if the inlay is not positioned on the rolling plane.","PeriodicalId":16319,"journal":{"name":"Journal of Manufacturing and Materials Processing","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46928432","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}
This paper presents a systematic approach to developing big data analytics for manufacturing process-relevant decision-making activities from the perspective of smart manufacturing. The proposed analytics consist of five integrated system components: (1) Data Preparation System, (2) Data Exploration System, (3) Data Visualization System, (4) Data Analysis System, and (5) Knowledge Extraction System. The functional requirements of the integrated system components are elucidated. In addition, JAVA™- and spreadsheet-based systems are developed to realize the proposed system components. Finally, the efficacy of the analytics is demonstrated using a case study where the goal is to determine the optimal material removal conditions of a dry Electrical Discharge Machining operation. The analytics identified the variables (among voltage, current, pulse-off time, gas pressure, and rotational speed) that effectively maximize the material removal rate. It also identified the variables that do not contribute to the optimization process. The analytics also quantified the underlying uncertainty. In summary, the proposed approach results in transparent, big-data-inequality-free, and less resource-dependent data analytics, which is desirable for small and medium enterprises—the actual sites where machining is carried out.
{"title":"Towards Developing Big Data Analytics for Machining Decision-Making","authors":"Angkush Kumar Ghosh, Saman Fattahi, Sharifu Ura","doi":"10.3390/jmmp7050159","DOIUrl":"https://doi.org/10.3390/jmmp7050159","url":null,"abstract":"This paper presents a systematic approach to developing big data analytics for manufacturing process-relevant decision-making activities from the perspective of smart manufacturing. The proposed analytics consist of five integrated system components: (1) Data Preparation System, (2) Data Exploration System, (3) Data Visualization System, (4) Data Analysis System, and (5) Knowledge Extraction System. The functional requirements of the integrated system components are elucidated. In addition, JAVA™- and spreadsheet-based systems are developed to realize the proposed system components. Finally, the efficacy of the analytics is demonstrated using a case study where the goal is to determine the optimal material removal conditions of a dry Electrical Discharge Machining operation. The analytics identified the variables (among voltage, current, pulse-off time, gas pressure, and rotational speed) that effectively maximize the material removal rate. It also identified the variables that do not contribute to the optimization process. The analytics also quantified the underlying uncertainty. In summary, the proposed approach results in transparent, big-data-inequality-free, and less resource-dependent data analytics, which is desirable for small and medium enterprises—the actual sites where machining is carried out.","PeriodicalId":16319,"journal":{"name":"Journal of Manufacturing and Materials Processing","volume":"1 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41316669","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}
Marco Noack, Kris Rudolph, Richard Breimann, E. Kirchner
This paper focuses on the electrochemical joining of additively manufactured components using simulation-based and experimental methods. The study investigates the influence of cover screens on the filling behavior of the joining zone. Experimental methods involving additive manufacturing and electroplating are combined with simulation models to provide a realistic representation of the joining process. The results show a good agreement between the simulated and experimental findings, indicating the applicability of the simulation model. The parameter study reveals that higher cover factors result in a decrease in the excess material ratio, indicating reduced material deposition outside the joining zone. The filling time required to completely fill the joining zone is influenced by both the cover size and the opening angle of the joining zone. The optimal parameter combinations depend on whether the filling time or the excess material volume is to be minimized. Cavity formation within the joining zone was identified as a critical factor affecting the completeness of the filling. The study provides insights into the influence of cover screens on the electrochemical joining process and offers guidance for optimizing the design of the joining zone.
{"title":"Influence of Sheet Covers on Filling Behavior in Electrochemical Joining of Additively Manufactured Components","authors":"Marco Noack, Kris Rudolph, Richard Breimann, E. Kirchner","doi":"10.3390/jmmp7050157","DOIUrl":"https://doi.org/10.3390/jmmp7050157","url":null,"abstract":"This paper focuses on the electrochemical joining of additively manufactured components using simulation-based and experimental methods. The study investigates the influence of cover screens on the filling behavior of the joining zone. Experimental methods involving additive manufacturing and electroplating are combined with simulation models to provide a realistic representation of the joining process. The results show a good agreement between the simulated and experimental findings, indicating the applicability of the simulation model. The parameter study reveals that higher cover factors result in a decrease in the excess material ratio, indicating reduced material deposition outside the joining zone. The filling time required to completely fill the joining zone is influenced by both the cover size and the opening angle of the joining zone. The optimal parameter combinations depend on whether the filling time or the excess material volume is to be minimized. Cavity formation within the joining zone was identified as a critical factor affecting the completeness of the filling. The study provides insights into the influence of cover screens on the electrochemical joining process and offers guidance for optimizing the design of the joining zone.","PeriodicalId":16319,"journal":{"name":"Journal of Manufacturing and Materials Processing","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43160808","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}
There has been significant interest in additively manufactured lattice structures in recent years due to their enhanced mechanical and multi-physics properties, making them suitable candidates for various applications. This study presents a multi-parameter implicit equation model for designing body-centred cubic (BCC) lattice structures. The model is used in conjunction with a multi-objective genetic algorithm (MOGA) approach to maximise the stiffness of the BCC lattice structure while minimising von-Mises stress within the structure under a specific loading condition. The selected design from the MOGA at a specific lattice density is compared with the classical BCC lattice structure and the designs generated by a single-objective genetic algorithm, which focuses on maximising stiffness or minimising von-Mises stress alone. By conducting a finite element analysis on the optimised samples and performing mechanical testing on the corresponding 3D-printed specimens, it was observed that the optimised lattice structures exhibited a substantial improvement in mechanical performance compared to the classical BCC model. The suitability of multi-objective and single-objective optimisation approaches for designing lattice structures was further investigated by comparing the corresponding designs in terms of their stiffness and maximum von-Mises stress values. The results from the numerical analysis and experimental testing demonstrate the significance of the application of an appropriate optimisation strategy for designing lattice structures for additive manufacturing.
{"title":"Multi-Objective Parametric Shape Optimisation of Body-Centred Cubic Lattice Structures for Additive Manufacturing","authors":"Hafiz M A Ali, M. Abdi","doi":"10.3390/jmmp7050156","DOIUrl":"https://doi.org/10.3390/jmmp7050156","url":null,"abstract":"There has been significant interest in additively manufactured lattice structures in recent years due to their enhanced mechanical and multi-physics properties, making them suitable candidates for various applications. This study presents a multi-parameter implicit equation model for designing body-centred cubic (BCC) lattice structures. The model is used in conjunction with a multi-objective genetic algorithm (MOGA) approach to maximise the stiffness of the BCC lattice structure while minimising von-Mises stress within the structure under a specific loading condition. The selected design from the MOGA at a specific lattice density is compared with the classical BCC lattice structure and the designs generated by a single-objective genetic algorithm, which focuses on maximising stiffness or minimising von-Mises stress alone. By conducting a finite element analysis on the optimised samples and performing mechanical testing on the corresponding 3D-printed specimens, it was observed that the optimised lattice structures exhibited a substantial improvement in mechanical performance compared to the classical BCC model. The suitability of multi-objective and single-objective optimisation approaches for designing lattice structures was further investigated by comparing the corresponding designs in terms of their stiffness and maximum von-Mises stress values. The results from the numerical analysis and experimental testing demonstrate the significance of the application of an appropriate optimisation strategy for designing lattice structures for additive manufacturing.","PeriodicalId":16319,"journal":{"name":"Journal of Manufacturing and Materials Processing","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43306159","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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