Review
Engineering, Manufacturing
Wei Gao, Soichi Ibaraki, M. Alkan Donmez, Daisuke Kono, J. R. R. Mayer, Yuan -Liu Chen, Karoly Szipka, Andreas Archenti, Jean-Marc Linares, Norikazu Suzuki
Summary: This article presents advanced technologies for the calibration of machine tools. It categorizes kinematic errors into intra-axis errors, inter-axis errors, and volumetric errors. It addresses the measurement methods, modeling theories, and compensation strategies for machine tool errors as the major technological elements of machine tool calibration. The criteria for selecting a combination of technological elements for machine tool calibration are provided based on accuracy, complexity, and cost. Recent applications of artificial intelligence and machine learning in machine tool calibration are introduced and future trends in machine tool calibration are discussed.
INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE
(2023)
Article
Computer Science, Theory & Methods
Jose Mena, Oriol Pujol, Jordi Vitria
Summary: This study introduces the importance of uncertainty estimation in machine learning systems and analyzes how uncertainty can be measured in classification systems based on deep learning. The study also provides an overview of practical considerations in different applications and highlights the properties that should be considered when developing metrics.
ACM COMPUTING SURVEYS
(2022)
Article
Biochemistry & Molecular Biology
Rahul Semwal, Imlimaong Aier, Pankaj Tyagi, Pritish Kumar Varadwaj
Summary: With the advancement of high throughput techniques, the discovery rate of enzyme sequences has increased significantly. The proposed Deep Neural Network-based approach, DeEPn, can quantify enzymes corresponding to all seven functional classes with high precision and accuracy, outperforming ECPred and SVM-Prot in terms of predictive quality. The DeEPn tool has been hosted as a web-based tool at https://bioserver.iiita.ac.in/DeEPn/.
JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS
(2021)
Article
Automation & Control Systems
Ugonna Loveday Adizue, Amanuel Diriba Tura, Elly Ogutu Isaya, Balazs Zsolt Farkas, Marton Takacs
Summary: High-quality machining is crucial in modern manufacturing technology, especially in industries such as aerospace, automobile, and medical sectors that require precision machining. This research focuses on developing a predictive model for surface roughness and optimizing process parameters for ultra-precision hard-turning finishing operation.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2023)
Article
Engineering, Chemical
Surbhi Sharma, Priyanka Devi Pantula, Srinivas Soumitri Miriyala, Kishalay Mitra
Summary: This study focuses on multi-objective optimization of an integrated grinding circuit considering various sources of uncertainties, using Chance constrained programming. A novel Data based Intelligent Sampling strategies for CCP has been proposed, combining machine learning techniques with a Fuzzy C-means algorithm to address sparse uncertain parameter space. The proposed technique demonstrates significant improvements over conventional sampling techniques in optimizing conflicting objectives.
Article
Computer Science, Artificial Intelligence
Annabella Astorino, Massimo Di Francesco, Manlio Gaudioso, Enrico Gorgone, Benedetto Manca
Summary: The study explores the use of polyhedral separation of sets as a tool in supervised classification, focusing on the optimization model introduced by Astorino and Gaudioso. By reformulating it in the DC form and employing the DCA algorithm, the problem is successfully tackled as demonstrated on various benchmark classification datasets.
Review
Engineering, Chemical
Rui Wang, Dayong Yang, Wei Wang, Furui Wei, Yuwei Lu, Yuqi Li
Summary: Nickel-based superalloys are widely used in aerospace, petrochemical, and marine fields due to their excellent oxidation resistance, corrosion resistance, stability, and reliability at various temperatures. However, during the cutting process, the interaction between the tool and the workpiece generates a large amount of cutting heat. The low thermal conductivity of the workpiece causes the cutting heat to accumulate at the contact point, resulting in severe tool wear, reduced tool life, and increased production cost. This paper discusses the tool wear mechanisms in the machining process of nickel-based superalloys and summarizes the research status of failure mechanisms and tool wear optimization. It highlights the importance of controlling heat and improving the tool environment to prolong the service life of tools. The development prospects of tool wear prevention measures in the field of nickel-based alloy machining are also described.
Article
Computer Science, Artificial Intelligence
Lei Guo, Zhengcong Duan, Wanjin Guo, Kai Ding, Chul-Hee Lee, Felix T. S. Chan
Summary: This study presents a novel Hunter-Prey Optimization (HPO)-optimized Otsu algorithm for tool wear assessment and machining process quality control. The algorithm shows superior performance in wear identification and image segmentation, with lower average runtime and mean squared error compared to conventional methods.
JOURNAL OF INTELLIGENT MANUFACTURING
(2023)
Article
Chemistry, Analytical
Ayman Mohamed, Mahmoud Hassan, Rachid M'Saoubi, Helmi Attia
Summary: This article reviews the latest technologies and components of TCM systems, with a focus on analyzing the advantages and limitations of wireless tool-embedded sensor nodes. It also provides a comprehensive review of dimensionality reduction techniques. Finally, it discusses attempts to generalize and enhance TCM systems and offers recommendations for future research directions.
Article
Engineering, Mechanical
Yan Lv, Congbo Li, Jixiang He, Wei Li, Xinyu Li, Juan Li
Summary: This study addresses the problem of energy consumption in the machining unit of a hobbing machine tool and develops a comprehensive function and surrogate models. An integrated optimization model is used to achieve the goals of reducing energy consumption and tool displacement.
FRONTIERS OF MECHANICAL ENGINEERING
(2022)
Article
Engineering, Industrial
Amr Salem, Hussien Hegab, Shahryar Rahnamayan, Hossam A. Kishawy
Summary: The study proposes a novel knowledge discovery approach to optimize sustainable machining processes, validated through a case study. Genetic Programming (GP) and Non-dominated Sorting Genetic Algorithm (NSGA-II) were utilized for modeling and optimization, clustering optimal cutting conditions into seven clusters offering five different desirability levels.
JOURNAL OF MANUFACTURING SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Julia Vazquez-Escobar, J. M. Hernandez, Miguel Cardenas-Montes
Summary: Particle physics experiments require processing complex data to detect rare signals, and machine learning algorithms help automate event classification and produce suitable datasets for physics research. In this study, three methods for estimating uncertainties in machine learning algorithm predictions were compared, demonstrating their ability to provide precise and robust predictions.
COMPUTER PHYSICS COMMUNICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Afshar Shamsi, Hamzeh Asgharnezhad, Ziba Bouchani, Khadijeh Jahanian, Morteza Saberi, Xianzhi Wang, Imran Razzak, Roohallah Alizadehsani, Arash Mohammadi, Hamid Alinejad-Rokny
Summary: Skin cancer, resulting from abnormal growth of skin cells, is becoming increasingly common worldwide. Early detection is crucial for increasing survival rate, but diagnosis remains challenging. Machine learning, particularly deep neural networks, are being explored to assist medical experts. However, it is important to develop models with uncertainty-awareness to provide confidence in predictions.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Lassana Coulibaly, Cheick Abdoul Kadir A. Kounta, Bernard Kamsu-Foguem, Fana Tangara
Summary: This paper proposes a learning method using Deep Gaussian Processes for probabilistic deep learning to analyze numerical prediction models, and an optimizer based on geometric transformation to transform simulated data to measured data. The method is tested on measurements of global radiation parameter and achieves satisfactory results with a significant reduction in error margin.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Automation & Control Systems
Cody A. Nizinski, Cuong Ly, Clement Vachet, Alex Hagen, Tolga Tasdizen, Luther W. McDonald
Summary: This paper explores the application of convolutional neural networks for determining the origins of uranium ore concentrates. By training models and identifying perturbations, significant shortcomings in the current training data and techniques used to develop models are discovered, providing insights for improvement.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Noel P. Greis, Monica L. Nogueira, Sambit Bhattacharya, Catherine Spooner, Tony Schmitz
Summary: Physics-guided machine learning (PGML) offers a new approach to stability modeling during machining by leveraging experimental data and combining theoretical process modeling efforts. This research explores strategies for updating the machine learning model with experimental data, aiming to achieve a useful approximation of the true stability model while reducing the number of required measurements.
JOURNAL OF INTELLIGENT MANUFACTURING
(2023)
Review
Engineering, Multidisciplinary
Tony Schmitz, Emma Betters, Erhan Budak, Esra Yuksel, Simon Park, Yusuf Altintas
Summary: This paper provides a chronological review of publications on the implementation and advancement of the receptance coupling substructure analysis (RCSA) approach. The review covers topics related to the RCSA approach, including tool-holder receptance modeling, connection modeling, spindle-machine receptances, and applications. It summarizes the contributions of multiple international authors (198 papers) to these topics and serves as a comprehensive resource for those starting their investigation into RCSA.
PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY
(2023)
Article
Engineering, Manufacturing
Gregory Corson, Jaydeep Karandikar, Tony Schmitz
Summary: This paper presents a case study on physics-informed Bayesian machine learning (PIBML) approach. The PIBML method utilizes three physics-based models to establish initial beliefs and determine the probability of milling stability through testing. Comparison with manufacturer recommendations highlights the importance of implementing PIBML approaches to enhance machining productivity and efficiency.
JOURNAL OF MANUFACTURING PROCESSES
(2023)
Article
Engineering, Manufacturing
Tony Schmitz, Lino Costa, Brian K. Canfield, Joshua Kincaid, Ross Zameroski, Ryan Garcia, Curtis Frederick, Andres Marquez Rossy, Trevor M. Moeller
Summary: This paper presents an approach to authenticate parts produced by additive friction stir deposition (AFSD) using an embedded QR tag. The process involves producing a QR tag, depositing base layers using AFSD, machining a pocket in the top layer, inserting the QR tag, and embedding it within the part by depositing additional layers. Authentication is achieved by CT scanning the known location of the QR code within the AFSD part.
MANUFACTURING LETTERS
(2023)
Article
Engineering, Industrial
Tony Schmitz, Gregory Corson, David Olvera, Christopher Tyler, Scott Smith
Summary: This paper presents a method for optimizing preform design in hybrid additive-subtractive manufacturing. Traditionally, the choice of preform form and geometry has relied on intuition and experience, or trial and error. However, a more optimal preform can be determined based on target parameters such as stiffness, cost, or lead time. The authors propose a framework for preform optimization considering static stiffness and the combined cost of additive and subtractive manufacturing, while ensuring stable cutting conditions for the tool-part combination. A case study comparing three preform geometries for a thin wall is provided to illustrate the procedure.
CIRP ANNALS-MANUFACTURING TECHNOLOGY
(2023)
Article
Automation & Control Systems
Sam St John, Matthew Alberts, Jaydeep Karandikar, Jamie Coble, Bradley Jared, Tony Schmitz, Christoph Ramsauer, David Leitner, Anahita Khojandi
Summary: In this study, a Random Forest classifier with Recursive Feature Elimination is used to analyze machining audio collected by a single microphone during down-milling operations. The classifier can accurately predict the stability of the machining process without the need for additional sensors. This low-cost approach enables real-time visualization of the machining process and helps machinists avoid unstable processes.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2023)
Article
Engineering, Manufacturing
Jake Dvorak, Dustin Gilmer, Ross Zameroski, Aaron Cornelius, Tony Schmitz
Summary: This paper presents a hybrid manufacturing approach that combines binder jet additive manufacturing (BJAM) and machining to produce silicon carbide (SiC) freeform surfaces. While AM techniques allow for complex geometries, additional machining or grinding is often needed for surface finish and shape. Hybrid manufacturing provides a solution, but there are challenges in coordinating the different processes. This paper addresses these challenges using structured light scanning to create a stock model for machining, achieving a maximum deviation of approximately 70 μm from the planned geometry.
JOURNAL OF MANUFACTURING AND MATERIALS PROCESSING
(2023)
Article
Engineering, Manufacturing
Tony Schmitz
Summary: This paper presents a Type B analysis for stability uncertainty in milling, where the uncertainty is represented by offset boundaries based on user-selected uncertainties in spindle speed and axial depth. The geometric approach is demonstrated using a frequency domain stability solution for a selected milling system.
JOURNAL OF MANUFACTURING PROCESSES
(2023)
Article
Engineering, Manufacturing
Justin West, Emma Betters, Tony Schmitz
Summary: This paper presents a limited-constraint alternative to the traditional over-constrained build plate clamping in metal additive manufacturing. By designing a fixture that allows build plate thermal growth within its plane while restricting deformation perpendicular to that plane, the build plate distortion can be significantly reduced.
MANUFACTURING LETTERS
(2023)
Article
Engineering, Manufacturing
Joshua Kincaid, Elijah Charles, Ryan Garcia, Jake Dvorak, Timothy No, Scott Smith, Tony Schmitz
Summary: Additive friction stir deposition (AFSD) is a solid-state metal deposition method that relies on kinetic energy and plastic flow instead of local melting and solidification. This study combines AFSD with structured light scanning, turning, and milling to produce metal components while considering the unique requirements of the hybrid manufacturing process sequences. Two demonstrations are presented, including coordinate system transfer between deposition and turning using a cylindrical build plate, and intermittent deposition-machining operations with structured light scanning to fabricate a two-sided hexagon-cylinder geometry.
MANUFACTURING LETTERS
(2023)
Article
Engineering, Manufacturing
Gregory Corson, Christopher Tyler, Jake Dvorak, Tony Schmitz
Summary: This paper presents a new mathematical framework for optimal preform design in hybrid manufacturing. The framework aims to minimize the combined cost of deposition and machining, taking into account the constraint of machining stability. A case study is provided to demonstrate the application of this approach.
MANUFACTURING LETTERS
(2024)
Article
Engineering, Manufacturing
Leah Jacobs, Jake Dvorak, Aaron Cornelius, Ross Zameroski, Tim No, Tony Schmitz
Summary: This paper describes the repeatability and reproducibility analyses of a commercially-available structured light scanning system and measurement artifact. It evaluates the stability and repeatability of the system by conducting repeated scans at different measurement positions. It also compares the measurements from structured light scanning with those from a coordinate measuring machine to validate the accuracy of the former.
MANUFACTURING LETTERS
(2023)
Article
Engineering, Industrial
Abhijit Bhattacharyya, Tony L. Schmitz, Scott W. T. Payne, Palash Roy Choudhury, John K. Schueller
Summary: This article introduces the method of error compensation and presents a homogeneous transformation matrix model for quantifying geometric errors in machine tools. The model is applied to a three-axis milling machine to illustrate the concept and is developed from first principles, eliminating the need for empirical relationships. Three solved numerical problems demonstrate the practical application of the model. The information provided can serve as a template for teaching this subject at the undergraduate level.
ADVANCES IN INDUSTRIAL AND MANUFACTURING ENGINEERING
(2022)
Proceedings Paper
Automation & Control Systems
Tony Schmitz, Jose Nazario, Timothy No, Michael Gomez, Greg Corson
Summary: This paper describes the use of feed rate scheduling software to predict radial depth of cut variation in three-axis milling toolpaths, and the use of this radial depth profile in a time-domain simulation to predict dynamic cutting forces.
Article
Engineering, Manufacturing
Jake Dvorak, Aaron Cornelius, Greg Corson, Ross Zameroski, Leah Jacobs, Joshua Penney, Tony Schmitz
Summary: This paper presents a digital twin for CNC machining of a wire arc additively manufactured preform, highlighting its key functionalities.
MANUFACTURING LETTERS
(2022)
Article
Engineering, Industrial
Xiaoliang Yan, Reed Williams, Elena Arvanitis, Shreyes Melkote
Summary: This paper extends prior work by developing a semantic segmentation approach for machinable volume decomposition using pre-trained generative process capability models, providing manufacturability feedback and labels of candidate machining operations for query 3D parts.
JOURNAL OF MANUFACTURING SYSTEMS
(2024)
Article
Engineering, Industrial
Jing Huang, Zhifen Zhang, Rui Qin, Yanlong Yu, Guangrui Wen, Wei Cheng, Xuefeng Chen
Summary: In this study, a deep learning framework that combines interpretability and feature fusion is proposed for real-time monitoring of pipeline leaks. The proposed method extracts abstract feature details of leak acoustic emission signals through multi-level dynamic receptive fields and optimizes the learning process of the network using a feature fusion module. Experimental results show that the proposed method can effectively extract distinguishing features of leak acoustic emission signals, achieving higher recognition accuracy compared to typical deep learning methods. Additionally, feature map visualization demonstrates the physical interpretability of the proposed method in abstract feature extraction.
JOURNAL OF MANUFACTURING SYSTEMS
(2024)