Hossein Gohari, Bin Shi, M. H. Attia, Rachid M'Saoubi
This study introduces a new wear model that can predict tool life in the milling process of Ti6Al4V using a cemented carbide tool. The model uses a finite element (FE) simulation to predict crack growth in the tool material microstructure. The FE model evaluates the crack propagation rate based on the real microstructure of the tool material, which is captured from microscopic images. To determine the normal and tangential forces operating on the flank face, an experimental procedure was developed based on three different flank wear widths. The FE model utilizes the elastic and fracture properties of tungsten carbide, and the elastic-plastic and fracture characteristics of cobalt binder to determine crack growth under the applied cutting forces. The crack propagation information combined with cutting conditions and the initial wear level are used to estimate the tool wear state. The developed model can predict tool life under different cutting conditions, tool geometries, and microstructure properties. Analysis of results showed that the error for the straight cuts was less than 6%, while for the complex cuts, it reached up to 20%. The accuracy of the model can be improved by extending the calibration test to higher levels of flank wear.
{"title":"Fracture Mechanics-Based Modelling of Tool Wear in Machining Ti6Al4V Considering the Microstructure of Cemented Carbide Tools","authors":"Hossein Gohari, Bin Shi, M. H. Attia, Rachid M'Saoubi","doi":"10.36897/jme/189588","DOIUrl":"https://doi.org/10.36897/jme/189588","url":null,"abstract":"This study introduces a new wear model that can predict tool life in the milling process of Ti6Al4V using a cemented carbide tool. The model uses a finite element (FE) simulation to predict crack growth in the tool material microstructure. The FE model evaluates the crack propagation rate based on the real microstructure of the tool material, which is captured from microscopic images. To determine the normal and tangential forces operating on the flank face, an experimental procedure was developed based on three different flank wear widths. The FE model utilizes the elastic and fracture properties of tungsten carbide, and the elastic-plastic and fracture characteristics of cobalt binder to determine crack growth under the applied cutting forces. The crack propagation information combined with cutting conditions and the initial wear level are used to estimate the tool wear state. The developed model can predict tool life under different cutting conditions, tool geometries, and microstructure properties. Analysis of results showed that the error for the straight cuts was less than 6%, while for the complex cuts, it reached up to 20%. The accuracy of the model can be improved by extending the calibration test to higher levels of flank wear.","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":" 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141371495","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}
Anna Burduk, Dagmara Łapczyńska, Joanna Kochańska, K. Musiał, Jozef Husár
The article presents the application of fuzzy logic to risk assessment in assembly and forming production processes. The fuzzy FMEA method was used, enabling the assessment of risk parameters based on expert opinions. This resulted in the development of a system that allows for greater flexibility and increased resistance to errors associated with human factors, enabling risk assessment through the use of linguistic variables. This allows organisations to analyse and manage risk, improving the efficiency and safety of their operations. This article presents an analysis of the benefits of using fuzzy logic in risk assessment in production in conjunction with the FMEA method, which is one of the most widely used risk assessment methods in industry. It discusses how fuzzy logic can help capture uncertainties in production processes and provide a more flexible framework for their evaluation. A case study is also presented, in which fuzzy logic was applied to risk assessment, highlighting the benefits it brings to production efficiency and safety.
{"title":"Fuzzy Logic in Risk Assessment of Production Machines Failure in Forming and Assembly Processes","authors":"Anna Burduk, Dagmara Łapczyńska, Joanna Kochańska, K. Musiał, Jozef Husár","doi":"10.36897/jme/189667","DOIUrl":"https://doi.org/10.36897/jme/189667","url":null,"abstract":"The article presents the application of fuzzy logic to risk assessment in assembly and forming production processes. The fuzzy FMEA method was used, enabling the assessment of risk parameters based on expert opinions. This resulted in the development of a system that allows for greater flexibility and increased resistance to errors associated with human factors, enabling risk assessment through the use of linguistic variables. This allows organisations to analyse and manage risk, improving the efficiency and safety of their operations. This article presents an analysis of the benefits of using fuzzy logic in risk assessment in production in conjunction with the FMEA method, which is one of the most widely used risk assessment methods in industry. It discusses how fuzzy logic can help capture uncertainties in production processes and provide a more flexible framework for their evaluation. A case study is also presented, in which fuzzy logic was applied to risk assessment, highlighting the benefits it brings to production efficiency and safety.","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141372924","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 laser-based Directed Energy Deposition (DED-LB) process involves a complex thermal history which strongly de-pends on the geometry of the deposited structure and substrate. The thermal mechanisms of the process are highly influenced by key process parameters like laser power, powder mass flow and scanning speed. Additionally, the size of the substrate influences the cooling behavior. The cooling behavior can be externally influenced and controlled by tempering the substrate, for example using a laser preheating method. The control of the cooling rate is crucial to ensure consistent properties and maintain constant conditions for subsequent finishing processes, irrespective of the size and geometry of the deposited structure and substrate. In this work, the influence of the substrate size on the cooling behavior and the properties of DED-LB manufactured structures is determined. The deposition of a cube with an edge length of 30 mm on different sized substrates and different cooling rates was simulated and executed. The impact of the different cooling behavior is evident in the hardness and the residual stresses of the deposited structures. Furthermore, the effect can be observed during a subsequent milling process. This work enables the creation of a model for the determination of the cooling rate and part properties depending on the substrate size.
{"title":"Influence of the Substrate Size on the Cooling Behavior and Properties of the DED-LB Process","authors":"Fabian Bieg, Clemens Maucher, H. Möhring","doi":"10.36897/jme/189582","DOIUrl":"https://doi.org/10.36897/jme/189582","url":null,"abstract":"The laser-based Directed Energy Deposition (DED-LB) process involves a complex thermal history which strongly de-pends on the geometry of the deposited structure and substrate. The thermal mechanisms of the process are highly influenced by key process parameters like laser power, powder mass flow and scanning speed. Additionally, the size of the substrate influences the cooling behavior. The cooling behavior can be externally influenced and controlled by tempering the substrate, for example using a laser preheating method. The control of the cooling rate is crucial to ensure consistent properties and maintain constant conditions for subsequent finishing processes, irrespective of the size and geometry of the deposited structure and substrate. In this work, the influence of the substrate size on the cooling behavior and the properties of DED-LB manufactured structures is determined. The deposition of a cube with an edge length of 30 mm on different sized substrates and different cooling rates was simulated and executed. The impact of the different cooling behavior is evident in the hardness and the residual stresses of the deposited structures. Furthermore, the effect can be observed during a subsequent milling process. This work enables the creation of a model for the determination of the cooling rate and part properties depending on the substrate size.","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":"28 28","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141378910","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}
Martin Erler, Feyzi Emrah Başar, Alexander Brosius
This paper delves into a critical aspect of Computer-Aided Production Planning (CAPP): the automated detection of the main rotational axis in turning parts within Computer-Aided Designs (CAD). The identification of the principal turning axis in CAD models presents numerous opportunities in the field of CAPP. In this study, the authors employ advanced surface segmentation techniques to analyse the surface geometry, pinpointing rotational surfaces within the CAD model. Subsequently, significant features are extracted from these identified rotational surfaces, and the necessary data for rotational centers are gathered. By fine-tuning the weighting of the data gathered, the approach can be tailored to suit various planning strategies. This approach has the potential to significantly enhance both the efficiency and accuracy of the automated production planning process for turning parts in CAPP.
{"title":"Automatic Detection of Axes for Turning Parts","authors":"Martin Erler, Feyzi Emrah Başar, Alexander Brosius","doi":"10.36897/jme/188803","DOIUrl":"https://doi.org/10.36897/jme/188803","url":null,"abstract":"This paper delves into a critical aspect of Computer-Aided Production Planning (CAPP): the automated detection of the main rotational axis in turning parts within Computer-Aided Designs (CAD). The identification of the principal turning axis in CAD models presents numerous opportunities in the field of CAPP. In this study, the authors employ advanced surface segmentation techniques to analyse the surface geometry, pinpointing rotational surfaces within the CAD model. Subsequently, significant features are extracted from these identified rotational surfaces, and the necessary data for rotational centers are gathered. By fine-tuning the weighting of the data gathered, the approach can be tailored to suit various planning strategies. This approach has the potential to significantly enhance both the efficiency and accuracy of the automated production planning process for turning parts in CAPP.","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":"59 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141111993","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}
Christian Friedrich, Stefan Vogt, Franziska Rudolph, Paul Patolla, Jossy Milagros Grützmann, Orlando Hohmeier, Martin Richter, Ken Wenzel, Dirk Reichelt, Steffen Ihlenfeldt
Value creation in production is based on collaboration of different stakeholders and requires the secure and sovereign exchange of knowledge. Today, knowledge has mostly been built up individually and is only exchanged in a proprietary manner. This paper presents an exemplary pipeline for federated services in cross-domain and cross-company value creation networks for cognitive production. On the example of collaboratively training of a federated machine learning model, machine tool lifetime is predicted in industrial manufacturing for high-end operating resources (high-quality cutting tools). From the shop floor to the cloud, all service relevant information is structured using existing digital twin standards and a linked data approach. In particular, the Industry 4.0 Asset Administration Shell (AAS) and OPC UA are used for collecting and referencing operational and engineering data. GAIA-X connectors transfer the service relevant data through a shared data space. The solution enables intelligent analysis and decision-making under the prioritization of data sovereignty and transparency and, therefore, acts as an enabler for future collaborative, data-driven manufacturing applications.
生产中的价值创造基于不同利益相关者的合作，需要安全和自主的知识交流。如今，知识大多是单独积累的，只能以专有方式进行交流。本文介绍了跨领域和跨公司认知生产价值创造网络中联合服务的示范管道。以协作训练联合机器学习模型为例，预测了工业制造中高端操作资源（高质量切削工具）的机床寿命。从车间到云端，所有与服务相关的信息都采用现有的数字孪生标准和链接数据方法进行结构化处理。特别是，工业 4.0 资产管理外壳（AAS）和 OPC UA 被用于收集和引用操作和工程数据。GAIA-X 连接器通过共享数据空间传输服务相关数据。该解决方案可在数据主权和透明度优先的原则下实现智能分析和决策，因此是未来协作式数据驱动型制造应用的推动者。
{"title":"Enabling Federated Learning Services Using OPC UA, Linked Data and GAIA-X in Cognitive Production","authors":"Christian Friedrich, Stefan Vogt, Franziska Rudolph, Paul Patolla, Jossy Milagros Grützmann, Orlando Hohmeier, Martin Richter, Ken Wenzel, Dirk Reichelt, Steffen Ihlenfeldt","doi":"10.36897/jme/188618","DOIUrl":"https://doi.org/10.36897/jme/188618","url":null,"abstract":"Value creation in production is based on collaboration of different stakeholders and requires the secure and sovereign exchange of knowledge. Today, knowledge has mostly been built up individually and is only exchanged in a proprietary manner. This paper presents an exemplary pipeline for federated services in cross-domain and cross-company value creation networks for cognitive production. On the example of collaboratively training of a federated machine learning model, machine tool lifetime is predicted in industrial manufacturing for high-end operating resources (high-quality cutting tools). From the shop floor to the cloud, all service relevant information is structured using existing digital twin standards and a linked data approach. In particular, the Industry 4.0 Asset Administration Shell (AAS) and OPC UA are used for collecting and referencing operational and engineering data. GAIA-X connectors transfer the service relevant data through a shared data space. The solution enables intelligent analysis and decision-making under the prioritization of data sovereignty and transparency and, therefore, acts as an enabler for future collaborative, data-driven manufacturing applications.","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":"111 29","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141124934","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}
André Jaquemod, Marijana Palalić, Kamil Güzel, H. Möhring
Additively manufactured components often show insufficient component quality due to the formation of different defects. Defects such as porosity result in material inhomogeneity and structural integrity issues. The integration of in-process monitoring in machining processes facilitates the identification of inhomogeneity characteristics in manufacturing, which is crucial for process adaptation. The incorporation of artificial defects in components has the potential to mimic and study the behaviour of real-world defects in a more controlled way. This study highlights the potential benefits of cutting force and vibration monitoring during machining operations with the goal of providing insights into the machining behaviours and the effects of the artificially introduced defects on the process. Detection of anomalies relies on identifying changes in force profiles or vibration patterns that might indicate the interaction between the tool and the defect. Machine learning algorithms were used to process and interpret the collected data. The algorithms are trained to recognize patterns, anomalies, or deviations from expected behaviours, which can aid in evaluating the effect of detected defects on the machining process and the resultant component quality. The main objective of this study is to contribute to enhancing quality control of machining processes for inhomogeneous materials.
{"title":"In-Process Monitoring of Inhomogeneous Material Characteristics Based on Machine Learning for Future Application in Additive Manufacturing","authors":"André Jaquemod, Marijana Palalić, Kamil Güzel, H. Möhring","doi":"10.36897/jme/187872","DOIUrl":"https://doi.org/10.36897/jme/187872","url":null,"abstract":"Additively manufactured components often show insufficient component quality due to the formation of different defects. Defects such as porosity result in material inhomogeneity and structural integrity issues. The integration of in-process monitoring in machining processes facilitates the identification of inhomogeneity characteristics in manufacturing, which is crucial for process adaptation. The incorporation of artificial defects in components has the potential to mimic and study the behaviour of real-world defects in a more controlled way. This study highlights the potential benefits of cutting force and vibration monitoring during machining operations with the goal of providing insights into the machining behaviours and the effects of the artificially introduced defects on the process. Detection of anomalies relies on identifying changes in force profiles or vibration patterns that might indicate the interaction between the tool and the defect. Machine learning algorithms were used to process and interpret the collected data. The algorithms are trained to recognize patterns, anomalies, or deviations from expected behaviours, which can aid in evaluating the effect of detected defects on the machining process and the resultant component quality. The main objective of this study is to contribute to enhancing quality control of machining processes for inhomogeneous materials.","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":"123 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140985441","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}
Although machine geometry measurements are an important part of mechanical engineering, they alone do not deliver enough information to set up or verify a CNC machine. The behaviour of the machine controller and its drive control settings usually need to be at least checked and in many situations corrected. In this article, on the basis of a developed machine error model, we show that it is sufficient to use a laser interferometer with a straightness measurement module to gather enough information in a single measurement to verify axis geometry and, at the same time, proper settings of machine servo loop gain. The results obtained during dynamic diagonal measurement can then be used to directly amend the servo settings. We prove our assumption in a series of real-world measurements.
{"title":"The Use of 3-DOF Laser Interferometer for Rapid Estimation of CNC Drives Settings","authors":"G. Budzyń, J. Rzepka","doi":"10.36897/jme/188203","DOIUrl":"https://doi.org/10.36897/jme/188203","url":null,"abstract":"Although machine geometry measurements are an important part of mechanical engineering, they alone do not deliver enough information to set up or verify a CNC machine. The behaviour of the machine controller and its drive control settings usually need to be at least checked and in many situations corrected. In this article, on the basis of a developed machine error model, we show that it is sufficient to use a laser interferometer with a straightness measurement module to gather enough information in a single measurement to verify axis geometry and, at the same time, proper settings of machine servo loop gain. The results obtained during dynamic diagonal measurement can then be used to directly amend the servo settings. We prove our assumption in a series of real-world measurements.","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140985502","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}
Alizée Remy, U. Nwankpa, M. Rauch, J. Hascoët, G. Ruckert
Metal Additive Manufacturing (MAM) is one of the innovative industrial technologies of the last decade, which presents some benefits as compared to traditional manufacturing techniques. MAM is faster, less expensive, and allow the manufacturing of large, complex components than casting, foundry etc. Understanding the influence of process parameters on the deposited matter and material characteristics is essential for the manufacturing of industrial parts. Current research concentrates on the impact of parameters on the fabricated structure geometry, microstructure and mechanical properties. There are limited number of studies, that focus on the possibility of Wire Feed Speed (WFS) parameter variation during deposition. In this work, a series of trials were realised with Cold Metal Transfer. The results showed that the quantity of material deposited was lesser than the theoretical value. The variation obtained was explained by the difference between the inputted WFS on the generator and the actual WFS output. Hence, the result on the influence of the variation of WFS on bead geometry was applied to a thermofluid model with Ti-6Al-4V alloy to confirm the sensitivity of this parameter in the quantity and geometry of the material deposited.
{"title":"Impact of a Variation in Wire Feed Speed on Deposits from the Wire Arc Additive Manufacturing (WAAM)","authors":"Alizée Remy, U. Nwankpa, M. Rauch, J. Hascoët, G. Ruckert","doi":"10.36897/jme/188308","DOIUrl":"https://doi.org/10.36897/jme/188308","url":null,"abstract":"Metal Additive Manufacturing (MAM) is one of the innovative industrial technologies of the last decade, which presents some benefits as compared to traditional manufacturing techniques. MAM is faster, less expensive, and allow the manufacturing of large, complex components than casting, foundry etc. Understanding the influence of process parameters on the deposited matter and material characteristics is essential for the manufacturing of industrial parts. Current research concentrates on the impact of parameters on the fabricated structure geometry, microstructure and mechanical properties. There are limited number of studies, that focus on the possibility of Wire Feed Speed (WFS) parameter variation during deposition. In this work, a series of trials were realised with Cold Metal Transfer. The results showed that the quantity of material deposited was lesser than the theoretical value. The variation obtained was explained by the difference between the inputted WFS on the generator and the actual WFS output. Hence, the result on the influence of the variation of WFS on bead geometry was applied to a thermofluid model with Ti-6Al-4V alloy to confirm the sensitivity of this parameter in the quantity and geometry of the material deposited.","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":"81 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140983329","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}
Additive manufacturing (AM) is used in metal part forming for its innovative character but its potential for sustainability is uncertain. The energy and material consumption required for manufacturing are significant. Thus, the research question of this article is: “What are the current uses of AM that present a real potential for reducing environmental impact?”. The WAAM (Wire Arc Additive Manufacturing) process appears to be the most energy-efficient in comparison to other AM processes. A process parameters study shows that deposition rate has a substantial impact on energy consumption. This parameter represents the amount of material deposited in a unit of time and is directly linked to productivity. It appears that an increase of the deposition rate leads to a reduction in energy consumption. Experiments on WAAM with a high deposition rate permits to create a database of energy and material consumption. This database is then used to identify cases of parts made with WAAM that offer a significant impact reduction compared with conventional manufacturing processes.
快速成型制造（AM）因其创新性而被用于金属零件成型，但其可持续发展的潜力尚不确定。制造过程中需要消耗大量能源和材料。因此，本文的研究问题是"目前有哪些 AM 用途具有减少环境影响的真正潜力？与其他 AM 工艺相比，WAAM（线弧快速成型）工艺似乎是最节能的。工艺参数研究表明，沉积率对能耗有很大影响。该参数表示单位时间内沉积的材料量，与生产率直接相关。提高沉积率似乎可以降低能耗。通过在高沉积率的 WAAM 上进行实验，可以建立一个能源和材料消耗数据库。然后，利用该数据库确定采用 WAAM 制造的零件的情况，与传统制造工艺相比，WAAM 可显著减少影响。
{"title":"The Potential of Additive Manufacturing of Metal Components to Reduce Environmental Impacts","authors":"Antoine Balidas, O. Kerbrat, J. Hascoet","doi":"10.36897/jme/186988","DOIUrl":"https://doi.org/10.36897/jme/186988","url":null,"abstract":"Additive manufacturing (AM) is used in metal part forming for its innovative character but its potential for sustainability is uncertain. The energy and material consumption required for manufacturing are significant. Thus, the research question of this article is: “What are the current uses of AM that present a real potential for reducing environmental impact?”. The WAAM (Wire Arc Additive Manufacturing) process appears to be the most energy-efficient in comparison to other AM processes. A process parameters study shows that deposition rate has a substantial impact on energy consumption. This parameter represents the amount of material deposited in a unit of time and is directly linked to productivity. It appears that an increase of the deposition rate leads to a reduction in energy consumption. Experiments on WAAM with a high deposition rate permits to create a database of energy and material consumption. This database is then used to identify cases of parts made with WAAM that offer a significant impact reduction compared with conventional manufacturing processes.","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":"88 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140702468","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}
Measurement of the process force in milling is usually conducted by using piezo-electric dynamometers which are costly and reduce the stiffness of the system. A less invasive alternative is an indirect estimation of cutting forces based on the power of the servo drives. However, a correction of frictional effects from the transmission system is necessary to achieve accurate results. Most machine tools are equipped with ball-screw drives whose friction behavior is highly nonlinear due to dependency on both velocity and position. This study provides a novel approach to consider all frictional and inertial effects in transmission behavior of ball-screw drives by utilizing the well-established generalized M AXWELL slip (GMS) model and combines it with a data-based approach, namely support vector regression (SVR). The approach acquires the internal states of the GMS model and uses them as an addition-nal input for the SVR. The model is validated on different experiments conducted on a five-axis machining center and compared to established friction models, as well as a sole SVR. The results show the model to have errors between 7% and 12% over the full working range of the x-and y-axes, respectively, outperforming the benchmark models significantly.
铣削过程中的加工力测量通常使用压电测力计，这种仪器成本高，而且会降低系统的刚度。另一种侵入性较小的方法是根据伺服驱动器的功率间接估算切削力。不过，要获得准确的结果，必须对传动系统的摩擦效应进行修正。大多数机床都配备有滚珠丝杠驱动装置，其摩擦行为因与速度和位置相关而高度非线性。本研究提供了一种新方法，通过利用成熟的广义 M AXWELL 滑移（GMS）模型，并将其与基于数据的方法（即支持向量回归（SVR））相结合，来考虑滚珠丝杠传动行为中的所有摩擦和惯性效应。该方法获取 GMS 模型的内部状态，并将其作为 SVR 的附加输入。该模型通过在五轴加工中心上进行的不同实验进行了验证，并与已建立的摩擦模型以及唯一的 SVR 进行了比较。结果表明，在 x 轴和 y 轴的整个工作范围内，该模型的误差分别在 7% 和 12% 之间，明显优于基准模型。
{"title":"A New Grey Box Approach for Friction Modelling of Machine Tool Drives","authors":"A. Rüppel, M. Meurer, Thomas Bergs","doi":"10.36897/jme/186269","DOIUrl":"https://doi.org/10.36897/jme/186269","url":null,"abstract":"Measurement of the process force in milling is usually conducted by using piezo-electric dynamometers which are costly and reduce the stiffness of the system. A less invasive alternative is an indirect estimation of cutting forces based on the power of the servo drives. However, a correction of frictional effects from the transmission system is necessary to achieve accurate results. Most machine tools are equipped with ball-screw drives whose friction behavior is highly nonlinear due to dependency on both velocity and position. This study provides a novel approach to consider all frictional and inertial effects in transmission behavior of ball-screw drives by utilizing the well-established generalized M AXWELL slip (GMS) model and combines it with a data-based approach, namely support vector regression (SVR). The approach acquires the internal states of the GMS model and uses them as an addition-nal input for the SVR. The model is validated on different experiments conducted on a five-axis machining center and compared to established friction models, as well as a sole SVR. The results show the model to have errors between 7% and 12% over the full working range of the x-and y-axes, respectively, outperforming the benchmark models significantly.","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":"28 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140226727","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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