Pub Date : 2025-12-03DOI: 10.1016/j.softx.2025.102459
Grover Enrique Castro Guzman , Diogo Ricardo da Costa , Eduardo Silva Lira , Suzana de Siqueira Santos , Taiane Coelho Ramos , Daniel Yasumasa Takahashi , Andre Fujita
The analysis of complex networks has traditionally relied on descriptive measures, such as centrality and clustering coefficients, as well as algorithms for detecting partitions and components. Additionally, a range of software packages has been designed for visualization and structural analysis. Although these approaches provide valuable information, they primarily focus on observable network features rather than their underlying generative mechanisms. We introduce statGraph, a nonparametric statistical framework for inferring properties of unobserved network generation mechanisms. At its core, statGraph leverages graph spectra, which intrinsically capture structural information and provide a robust basis for nonparametric inference. The package implements a range of methods, including graph entropy estimation, random graph parameter estimation, model selection procedures, statistical tests for comparing graphs, correlation analysis between sets of graphs, and graph clustering algorithms. By bridging graph theory and statistics via spectral analysis, statGraph provides a comprehensive toolkit for advancing the statistical analysis of complex networks.
{"title":"StatGraph: an R package for complex network statistical analyses based on spectrum","authors":"Grover Enrique Castro Guzman , Diogo Ricardo da Costa , Eduardo Silva Lira , Suzana de Siqueira Santos , Taiane Coelho Ramos , Daniel Yasumasa Takahashi , Andre Fujita","doi":"10.1016/j.softx.2025.102459","DOIUrl":"10.1016/j.softx.2025.102459","url":null,"abstract":"<div><div>The analysis of complex networks has traditionally relied on descriptive measures, such as centrality and clustering coefficients, as well as algorithms for detecting partitions and components. Additionally, a range of software packages has been designed for visualization and structural analysis. Although these approaches provide valuable information, they primarily focus on observable network features rather than their underlying generative mechanisms. We introduce <strong>statGraph</strong>, a nonparametric statistical framework for inferring properties of unobserved network generation mechanisms. At its core, <strong>statGraph</strong> leverages graph spectra, which intrinsically capture structural information and provide a robust basis for nonparametric inference. The package implements a range of methods, including graph entropy estimation, random graph parameter estimation, model selection procedures, statistical tests for comparing graphs, correlation analysis between sets of graphs, and graph clustering algorithms. By bridging graph theory and statistics via spectral analysis, <strong>statGraph</strong> provides a comprehensive toolkit for advancing the statistical analysis of complex networks.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102459"},"PeriodicalIF":2.4,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-03DOI: 10.1016/j.softx.2025.102471
Jeonghwan Kim , Hyunsoo Kim , Jeongku Yun , Jinkyoung Kim , Kwanjung Yee
As the demand for unmanned vehicle (UV) systems continues to grow across a wide range of industries, there is an increasing need for professionals equipped to carry out mission-specific, system-level design. However, traditional engineering education often lacks structured methods for system-level design and does not provide adequate environments for hands-on, collaborative design experiences. To address this gap, we present the Comprehensive Design Framework for Advanced Mobility (CoDeF)—a web-based collaborative platform tailored for early-stage UV system design and education. Built on systems engineering principles, CoDeF provides a structured design process and supports synchronized collaboration among multiple users through shared data and workflows. The platform offers high extensibility and configurability, allowing instructors to flexibly modify design stages and deliverables to meet specific educational objectives. CoDeF has been successfully implemented in multiple university courses, demonstrating its potential as a practical tool for bridging the gap between academic training and industry-oriented system design practice.
{"title":"CoDeF: A web-based education platform for system-level design of unmanned vehicles","authors":"Jeonghwan Kim , Hyunsoo Kim , Jeongku Yun , Jinkyoung Kim , Kwanjung Yee","doi":"10.1016/j.softx.2025.102471","DOIUrl":"10.1016/j.softx.2025.102471","url":null,"abstract":"<div><div>As the demand for unmanned vehicle (UV) systems continues to grow across a wide range of industries, there is an increasing need for professionals equipped to carry out mission-specific, system-level design. However, traditional engineering education often lacks structured methods for system-level design and does not provide adequate environments for hands-on, collaborative design experiences. To address this gap, we present the Comprehensive Design Framework for Advanced Mobility (CoDeF)—a web-based collaborative platform tailored for early-stage UV system design and education. Built on systems engineering principles, CoDeF provides a structured design process and supports synchronized collaboration among multiple users through shared data and workflows. The platform offers high extensibility and configurability, allowing instructors to flexibly modify design stages and deliverables to meet specific educational objectives. CoDeF has been successfully implemented in multiple university courses, demonstrating its potential as a practical tool for bridging the gap between academic training and industry-oriented system design practice.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102471"},"PeriodicalIF":2.4,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-02DOI: 10.1016/j.softx.2025.102460
Jing Liu , Saber Elsayed , Daryl Essam , Kyle Harrison , Ruhul Sarker
This paper presents an open-source software tool, PPSSolver, which is developed for academic research and practical decision-making in the Project Portfolio Selection and Scheduling Problem (PPSSP). PPSSolver provides an integrated platform for generating PPSSP instances with different complexities, optimizing PPSSPs using various algorithms, handling dynamic changes, and analyzing results. The software is designed to support researchers in algorithm development by allowing them to integrate customized solvers and evaluate them across benchmark instances. Additionally, PPSSolver provides a graphical user interface for ease of use, thereby lowering the technical barrier for practitioners.
{"title":"PPSSolver: An open-source software tool for Project Portfolio Selection and Scheduling Problems","authors":"Jing Liu , Saber Elsayed , Daryl Essam , Kyle Harrison , Ruhul Sarker","doi":"10.1016/j.softx.2025.102460","DOIUrl":"10.1016/j.softx.2025.102460","url":null,"abstract":"<div><div>This paper presents an open-source software tool, PPSSolver, which is developed for academic research and practical decision-making in the Project Portfolio Selection and Scheduling Problem (PPSSP). PPSSolver provides an integrated platform for generating PPSSP instances with different complexities, optimizing PPSSPs using various algorithms, handling dynamic changes, and analyzing results. The software is designed to support researchers in algorithm development by allowing them to integrate customized solvers and evaluate them across benchmark instances. Additionally, PPSSolver provides a graphical user interface for ease of use, thereby lowering the technical barrier for practitioners.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102460"},"PeriodicalIF":2.4,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-02DOI: 10.1016/j.softx.2025.102469
Antonio J. Hidalgo-Marín , Antonio J. Nebro , José García-Nieto
ParetoInvest is an advanced software tool that facilitates the application of bio-inspired optimization algorithms to the multi-objective portfolio selection problem. Built on top of the widely used jMetal framework, ParetoInvest supports a range of meta-heuristics, including multi-objective evolutionary algorithms (MOEAs), to model and solve complex asset allocation tasks. A distinguishing feature of the platform is its integration with real-time financial data sources, providing up-to-date information on U.S. market assets and enabling simulations that accurately reflect current market conditions. The tool also includes a reliable data management system for downloading, storing, and manipulating financial datasets, with support for exporting data in various formats for external analysis. By combining real-time data access, advanced optimization techniques, and flexible data handling, ParetoInvest offers a powerful environment for researchers, finance professionals, and developers seeking innovative solutions for portfolio optimization using bio-inspired methods.
{"title":"ParetoInvest: Integrating real-time financial data and multi-objective meta-heuristics for portfolio optimization","authors":"Antonio J. Hidalgo-Marín , Antonio J. Nebro , José García-Nieto","doi":"10.1016/j.softx.2025.102469","DOIUrl":"10.1016/j.softx.2025.102469","url":null,"abstract":"<div><div>ParetoInvest is an advanced software tool that facilitates the application of bio-inspired optimization algorithms to the multi-objective portfolio selection problem. Built on top of the widely used jMetal framework, ParetoInvest supports a range of meta-heuristics, including multi-objective evolutionary algorithms (MOEAs), to model and solve complex asset allocation tasks. A distinguishing feature of the platform is its integration with real-time financial data sources, providing up-to-date information on U.S. market assets and enabling simulations that accurately reflect current market conditions. The tool also includes a reliable data management system for downloading, storing, and manipulating financial datasets, with support for exporting data in various formats for external analysis. By combining real-time data access, advanced optimization techniques, and flexible data handling, ParetoInvest offers a powerful environment for researchers, finance professionals, and developers seeking innovative solutions for portfolio optimization using bio-inspired methods.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102469"},"PeriodicalIF":2.4,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.softx.2025.102444
Jiayun Liu , David González-Fernández , Manuel Castillo-Cara , Raúl García-Castro
Transforming tabular data into synthetic images enables the application of vision-based deep learning models – such as Convolutional Neural Networks and Vision Transformers – to non-visual tasks. This paper presents TINTOlib, the first Python library to unify a diverse set of tabular data into synthetic image transformation methods into a cohesive, extensible framework. TINTOlib unifies parametric and non-parametric tabular to synthetic image methods within a consistent interface, lowering the barrier to apply, compare, and extend these techniques. The generated images can be directly used with vision models or integrated into Hybrid Neural Networks that combine visual and tabular branches. By addressing reproducibility, scalability, and modularity, the library simplifies experimentation and deployment of deep learning pipelines on tabular data. Illustrative results show that the use of synthetic images can achieve competitive or superior performance compared to state-of-the-art classical models in both regression and classification tasks, with outcomes varying across transformation techniques and architectural backbones. This underscores the utility of TINTOlib in bridging tabular data with vision-based deep learning via synthetic image representations.
{"title":"TINTOlib: A Python library for transforming tabular data into synthetic images for deep neural networks","authors":"Jiayun Liu , David González-Fernández , Manuel Castillo-Cara , Raúl García-Castro","doi":"10.1016/j.softx.2025.102444","DOIUrl":"10.1016/j.softx.2025.102444","url":null,"abstract":"<div><div>Transforming tabular data into synthetic images enables the application of vision-based deep learning models – such as Convolutional Neural Networks and Vision Transformers – to non-visual tasks. This paper presents TINTOlib, the first Python library to unify a diverse set of tabular data into synthetic image transformation methods into a cohesive, extensible framework. TINTOlib unifies parametric and non-parametric tabular to synthetic image methods within a consistent interface, lowering the barrier to apply, compare, and extend these techniques. The generated images can be directly used with vision models or integrated into Hybrid Neural Networks that combine visual and tabular branches. By addressing reproducibility, scalability, and modularity, the library simplifies experimentation and deployment of deep learning pipelines on tabular data. Illustrative results show that the use of synthetic images can achieve competitive or superior performance compared to state-of-the-art classical models in both regression and classification tasks, with outcomes varying across transformation techniques and architectural backbones. This underscores the utility of TINTOlib in bridging tabular data with vision-based deep learning via synthetic image representations.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"32 ","pages":"Article 102444"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.softx.2025.102456
Li-Fang Chen
In response to the need for neutron spectrum unfolding based on activation reactions, this study presents SpecKit, a reproducible and user-friendly software workflow. The method models the problem as a linear system using an activation response matrix MA and an observed activity vector bA, and estimates the unknown flux vector through residual minimization combined with log-smoothness regularization. Uncertainty quantification is achieved via repeated fitting and Monte Carlo sampling, with resulting group-wise uncertainty bands provided. The software includes modules for cross-section and matrix preparation, group flux unfolding with uncertainty estimation, and results visualization, all integrated within a graphical user interface (GUI). Key output metrics include group-wise flux, total flux, relative error. Performance evaluation is carried out using MCNP-generated synthetic scenarios under systematically designed prior-group flux mismatches, with analysis of deviation across thermal, epithermal, and fast neutron energy regions. Results demonstrate that the proposed method consistently corrects prior bias, maintains low deviation across energy ranges, and provides well-calibrated uncertainty estimates under varying levels of prior shift and measurement noise. A minimal data package and reproduction scripts are released alongside the project to facilitate community validation and further development.
{"title":"SpecKit: An integrated toolkit for neutron spectrum unfolding using activation reactions","authors":"Li-Fang Chen","doi":"10.1016/j.softx.2025.102456","DOIUrl":"10.1016/j.softx.2025.102456","url":null,"abstract":"<div><div>In response to the need for neutron spectrum unfolding based on activation reactions, this study presents <strong>SpecKit</strong>, a reproducible and user-friendly software workflow. The method models the problem as a linear system using an activation response matrix M<sub>A</sub> and an observed activity vector b<sub>A</sub>, and estimates the unknown flux vector through residual minimization combined with log-smoothness regularization. Uncertainty quantification is achieved via repeated fitting and Monte Carlo sampling, with resulting group-wise uncertainty bands provided. The software includes modules for cross-section and matrix preparation, group flux unfolding with uncertainty estimation, and results visualization, all integrated within a graphical user interface (GUI). Key output metrics include group-wise flux, total flux, relative error. Performance evaluation is carried out using <strong>MCNP</strong>-generated synthetic scenarios under systematically designed prior-group flux mismatches, with analysis of deviation across thermal, epithermal, and fast neutron energy regions. Results demonstrate that the proposed method consistently corrects prior bias, maintains low deviation across energy ranges, and provides well-calibrated uncertainty estimates under varying levels of prior shift and measurement noise. A minimal data package and reproduction scripts are released alongside the project to facilitate community validation and further development.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"32 ","pages":"Article 102456"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.softx.2025.102458
Rubén Martínez Amodia, Cristina Tirnauca, Marta Zorrilla
Fine-tuning Retrieval-Augmented Generation (RAG) chatbots is challenging due to the many interdependent parameters affecting performance. RAGBOT CLI is a terminal-based Python tool built atop the LangChain framework that enables systematic experimentation with RAG configurations and automated evaluation using both quantitative metrics (BLEU, ROUGE-L, semantic similarity) and qualitative ones (contextual relevance, response relevance, factual fidelity). Unlike existing frameworks, RAGBOT CLI offers a modular, project-oriented architecture and supports hybrid evaluation strategies, making it suitable for academic and professional use. This paper describes the architecture, functionalities, and practical applications, showcasing its potential impact on the development and evaluation of RAG-based chatbots.
{"title":"RAGBOT CLI: a Python library for running and evaluating retrieval-augmented generation chatbots","authors":"Rubén Martínez Amodia, Cristina Tirnauca, Marta Zorrilla","doi":"10.1016/j.softx.2025.102458","DOIUrl":"10.1016/j.softx.2025.102458","url":null,"abstract":"<div><div>Fine-tuning Retrieval-Augmented Generation (RAG) chatbots is challenging due to the many interdependent parameters affecting performance. RAGBOT CLI is a terminal-based Python tool built atop the LangChain framework that enables systematic experimentation with RAG configurations and automated evaluation using both quantitative metrics (BLEU, ROUGE-L, semantic similarity) and qualitative ones (contextual relevance, response relevance, factual fidelity). Unlike existing frameworks, RAGBOT CLI offers a modular, project-oriented architecture and supports hybrid evaluation strategies, making it suitable for academic and professional use. This paper describes the architecture, functionalities, and practical applications, showcasing its potential impact on the development and evaluation of RAG-based chatbots.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"32 ","pages":"Article 102458"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.softx.2025.102451
Vanessa H. Silupú-Ortega, Ricardo Velezmoro-León, Manuel H. García-Saba, Eder Escobar-Gómez, Robert Ipanaqué-Chero
We present a symbolic and geometric software framework, implemented in Mathematica, for constructing and interpolating curves intrinsically embedded in the four-dimensional hypersphere () using stereographic projection and its inverse. The framework addresses the challenge of preserving hyperspherical constraints during interpolation by projecting curve data to the three-dimensional hyperplane , performing classical Lagrange interpolation in , and lifting the result back to through an exact inverse mapping. This approach ensures that interpolated curves remain entirely intrinsic to the hypersphere, avoiding deviations that occur in direct interpolation. The software provides explicit symbolic implementations for the projection, inverse projection, and interpolation procedures, along with visualization tools based on fixed immersions from to . Illustrative examples demonstrate the framework’s accuracy and its ability to handle both curve and surface constructions, highlighting its potential for applications in high-dimensional geometric modeling, theoretical physics, and computational differential geometry. The full source code and examples are available in a public repository for reproducibility and further development.
{"title":"Software framework for intrinsic curve interpolation in the 4D hypersphere using stereographic projection","authors":"Vanessa H. Silupú-Ortega, Ricardo Velezmoro-León, Manuel H. García-Saba, Eder Escobar-Gómez, Robert Ipanaqué-Chero","doi":"10.1016/j.softx.2025.102451","DOIUrl":"10.1016/j.softx.2025.102451","url":null,"abstract":"<div><div>We present a symbolic and geometric software framework, implemented in <em>Mathematica</em>, for constructing and interpolating curves intrinsically embedded in the four-dimensional hypersphere (<span><math><msup><mi>S</mi><mn>3</mn></msup><mo>⊂</mo><msup><mrow><mi>E</mi></mrow><mn>4</mn></msup></math></span>) using stereographic projection and its inverse. The framework addresses the challenge of preserving hyperspherical constraints during interpolation by projecting curve data to the three-dimensional hyperplane <span><math><mi>W</mi><mo>=</mo><mn>0</mn></math></span>, performing classical Lagrange interpolation in <span><math><msup><mrow><mi>E</mi></mrow><mn>3</mn></msup></math></span>, and lifting the result back to <span><math><msup><mi>S</mi><mn>3</mn></msup></math></span> through an exact inverse mapping. This approach ensures that interpolated curves remain entirely intrinsic to the hypersphere, avoiding deviations that occur in direct <span><math><msup><mrow><mi>E</mi></mrow><mn>4</mn></msup></math></span> interpolation. The software provides explicit symbolic implementations for the projection, inverse projection, and interpolation procedures, along with visualization tools based on fixed immersions from <span><math><msup><mrow><mi>E</mi></mrow><mn>4</mn></msup></math></span> to <span><math><msup><mrow><mi>E</mi></mrow><mn>3</mn></msup></math></span>. Illustrative examples demonstrate the framework’s accuracy and its ability to handle both curve and surface constructions, highlighting its potential for applications in high-dimensional geometric modeling, theoretical physics, and computational differential geometry. The full source code and examples are available in a public repository for reproducibility and further development.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"32 ","pages":"Article 102451"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.softx.2025.102401
Gorka Urbikain-Pelayo , Daniel Olvera-Trejo , Luis Norberto López de Lacalle , Alex Elías-Zúñiga
Turning and boring processes are widely used to machine shafts, tubes, casings, and rings across sectors from automotive to aerospace, yet productivity is often limited by regenerative chatter, which couples cutting mechanics with tool–workpiece dynamics, affecting surface finish, reducing tool life and leading to conservative process parameters. Turn+ addresses this gap with an open-source MATLAB application that unifies cutting-force prediction, dynamic-stability analysis, and surface-roughness estimation for external turning and boring. Through an intuitive interface, users specify tool geometry, cutting coefficients, and machine-tool modal data. Turn+ then integrates the regenerative delay-differential equation with a semi-implicit Euler scheme to predict time-domain forces and displacements. A built-in post-processor generates stability-gradient maps and reconstructs the tool-nose path to estimate average roughness, revealing how cutting parameters influence chatter and finish. Validation against classic analytical solutions and cutting tests shows agreement within 6 % for the critical depth of cut and 8 % for average roughness. A modular architecture separates the GUI from solver engines, enabling straightforward integration of new force models and advanced operations such as pinch or parallel turning. Turn+ provides an accessible, rigorous platform for education, research, and industrial process planning to improve productivity and repeatability.
{"title":"Turn+: A MATLAB-based software for dynamic turning, chatter analysis, and surface roughness prediction","authors":"Gorka Urbikain-Pelayo , Daniel Olvera-Trejo , Luis Norberto López de Lacalle , Alex Elías-Zúñiga","doi":"10.1016/j.softx.2025.102401","DOIUrl":"10.1016/j.softx.2025.102401","url":null,"abstract":"<div><div>Turning and boring processes are widely used to machine shafts, tubes, casings, and rings across sectors from automotive to aerospace, yet productivity is often limited by regenerative chatter, which couples cutting mechanics with tool–workpiece dynamics, affecting surface finish, reducing tool life and leading to conservative process parameters. <em>Turn</em>+ addresses this gap with an open-source MATLAB application that unifies cutting-force prediction, dynamic-stability analysis, and surface-roughness estimation for external turning and boring. Through an intuitive interface, users specify tool geometry, cutting coefficients, and machine-tool modal data. <em>Turn</em>+ then integrates the regenerative delay-differential equation with a semi-implicit Euler scheme to predict time-domain forces and displacements. A built-in post-processor generates stability-gradient maps and reconstructs the tool-nose path to estimate average roughness, revealing how cutting parameters influence chatter and finish. Validation against classic analytical solutions and cutting tests shows agreement within 6 % for the critical depth of cut and 8 % for average roughness. A modular architecture separates the GUI from solver engines, enabling straightforward integration of new force models and advanced operations such as pinch or parallel turning. <em>Turn</em>+ provides an accessible, rigorous platform for education, research, and industrial process planning to improve productivity and repeatability.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"32 ","pages":"Article 102401"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145680918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.softx.2025.102453
T. Reiter, L. Filipovic
ViennaPS is an open-source software framework for simulating fabrication processes in semiconductor manufacturing, with a focus on topography evolution during etching and deposition. It uses a high-performance level-set method with a hierarchical run-length encoding data structure for efficient and fast geometry representation and evolution. ViennaPS supports both analytical and physics-based process models, including Monte Carlo ray tracing for flux calculation at the feature scale. Designed for flexibility and extensibility, it enables users to prototype new models or apply pre-configured ones. ViennaPS provides a customizable platform for researchers and engineers developing advanced process simulations in both academic and industrial settings.
{"title":"ViennaPS: A flexible framework for semiconductor process simulation","authors":"T. Reiter, L. Filipovic","doi":"10.1016/j.softx.2025.102453","DOIUrl":"10.1016/j.softx.2025.102453","url":null,"abstract":"<div><div>ViennaPS is an open-source software framework for simulating fabrication processes in semiconductor manufacturing, with a focus on topography evolution during etching and deposition. It uses a high-performance level-set method with a hierarchical run-length encoding data structure for efficient and fast geometry representation and evolution. ViennaPS supports both analytical and physics-based process models, including Monte Carlo ray tracing for flux calculation at the feature scale. Designed for flexibility and extensibility, it enables users to prototype new models or apply pre-configured ones. ViennaPS provides a customizable platform for researchers and engineers developing advanced process simulations in both academic and industrial settings.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"32 ","pages":"Article 102453"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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