Detección temprana del desgaste de herramientas en brochadora electromecánica a través de la monitorización del movimiento principal de los servomotores

El objetivo de este proyecto es demostrar que es posible controlar el desgaste de las herramientas de brochado mediante la monitorización de las variables de máquina sin sensores externos en un entorno de producción real. La supervisión del desgaste de las herramientas es una actividad cuyo objetivo es reducir los errores de producción, mejorar la calidad de los productos y conseguir una fabricación sin defectos. Además, el brochado es un proceso de alto valor añadido por lo que es fundamental mejorar la calidad tanto de los productos como de los procesos de fabricación. Por otro lado, el brochado puede ser un proceso muy costoso, por lo que es necesario controlar en todo momento el estado de la herramienta de brochado, para evitar un uso inadecuado de la herramienta o dañar la pieza de trabajo. Todo ello adquiere mayor relevancia en la fabricación aeronáutica, donde las piezas tienen gran valor añadido y se desarrolla el caso experimental que se presenta en este documento. El resultado de esta investigación se alcanza gracias a la correlación de lecturas de variables de servomotores de una brochadora, como la potencia o el par, con el estado de las herramientas de brochado. Los resultados se obtienen utilizando datos de dos ensayos diferentes en los que se han empleado herramientas similares. A continuación, los datos se utilizan para entrenar una serie de modelos que estiman el desgaste de las herramientas en brochado. Previo a los resultados, se presenta el marco experimental, detallando los equipos y herramientas utilizados en los ensayos y el método empleado para el brochado, incluyendo el sistema de captación de imágenes diseñado para este proyecto. Todos los modelos presentados en este documento predicen con precisión el desgaste de la herramienta de brochado con un coeficiente de determinación superior a 0,9.Proiektu honen helburua da frogatzea posible dela brotxaketa erreminten higadura kontrolatzea, kanpoko sentsorerik gabeko makinaren aldagaiak monitorizatuz, benetako produkzio-inguru batean. Erreminten higaduraren gainbegiratzea, ekoizpen-akatsak murriztea , produktuen kalitatea hobetzea eta akatsik gabeko fabrikazioa lortzea helburu duen jarduera da. Gainera, brotxaketa balio erantsi handiko prozesua da, eta, beraz, funtsezkoa da produktuen zein fabrikazio-prozesuen kalitatea hobetzea. Bestalde, brotxatzea oso prozesu garestia izan daiteke, eta, beraz, brotxaketa-erremintaren egoera kontrolatu behar da une oro, erremintaren erabilera desegokia saihesteko edo lan-pieza ez kaltetzeko. Horrek guztiak garrantzi handiagoa hartzen du fabrikazio aeronautikoan, non piezek balio erantsi handia duten eta dokumentu honetan aurkezten den kasu esperimentala garatzen den. Ikerketa honen emaitza brotxaketa makina baten serbomotorren aldagaien irakurketen korrelazioari esker lortzen da, hala nola potentzia edo parea, brotxaketa erreminten egoerarekin. Emaitzak lortzeko, antzeko erremintak erabili diren bi saiakuntza desberdinetako datuak erabiltzen dira. Ondoren, datuak brotxaketa tresnen higadura estimatzen duten eredu batzuk entrenatzeko erabiltzen dira. Emaitzak lortu aurretik, esparru esperimentala aurkezten da, entseguetan erabilitako tresneria eta erremintak eta, brotxatzeko erabilitako metodoa zehaztuz, proiektu honetarako diseinatutako irudiak hartzeko sistema barne. Proiektu honetan aurkeztutako eredu guztiek zehaztasunez aurreikusten dute 0,9tik gorako determinazio-koefizientearekin brotxaketa erremintaren higadura.The objective of this project is to demonstrate that it is possible to control broaching tool wear by monitoring machine variables without external sensors in a real production environment. Tool wear monitoring is an activity aimed at reducing production errors, improving product quality and achieving zero-defect manufacturing. In addition, broaching is a high value-added process so it is essential to improve the quality of both products and manufacturing processes. On the other hand, broaching can be a very costly process, so it is necessary to control the condition of the broaching tool at all times, to avoid improper use of the tool or damage to the workpiece. All this becomes more relevant in aeronautical manufacturing, where the parts have great added value and the experimental case presented in this paper is developed. The result of this research is achieved thanks to the correlation of signals of servomotor variables of a broaching machine, such as power or torque, with the state of the broaching tools. The results are obtained using data from two different trials in which similar tools were used. The data is then used to train a series of models that estimate broaching tool wear. Prior to the results, the experimental framework is presented, detailing the equipment and tools used in the trials and the method employed for broaching, including the image capture system designed for this project. All the models presented in this work accurately predict broaching tool wear with a coefficient of determination greater than 0.9

The influence of cutting edge microgeometry on the broaching of Inconel 718 slots

In aero-engine production, the dovetails (firtrees) of turbine discs are manufactured by broaching. Introducing innovative micro-geometry modifications to broaching tools can significantly influence cutting force, energy consumption, tool wear, and cutting edge temperature during broaching. Therefore, this study aims to study this influence by treating the cutting edge by brushing with ceramic bristles. The results reveal that the increase in cutting edge radius significantly influences the cutting force, particularly its component in the forward direction, equating it to the tangential component. Furthermore, during the experimental tests, considerable wear was observed on the cutting edge, which generated strong vibrations detected through the force signals, accounting to poor surface quality and a higher coefficient of friction close to 1. The 2D simulations generated information on temperature distribution along the cutting edge profile. On the other hand, was observed the presence of subsurface damage characterized by distorted grain boundaries aligned with the cutting direction, along with the formation of uninterrupted non-serrated chips due to thermoplastic deformation. Further, 12 µm cutting edge radius exhibits the best performance in terms of cutting force, temperature, and surface quality.“This research was funded by the Ministry of Mineco Grant PID2019-109340RB-I00 and PDC2021-121792-I00 funded by MCIN/AEI/ https://doi.org/10.13039/501100011033. Thanks, are also due to European commission by H2020 project n. 958,357 InterQ Interlinked Process, Product and Data Quality framework for Zero-Defects Manufacturing. Experiments were performed by help of project (QUOLINK TED2021-130044B-I00) Ministerio de Ciencia e Innovación 2021. In aspects related with modelling, support from the University Excellency groups grant by Basque Government IT 1573–22”

Early detection of tool wear in electromechanical broaching machines by monitoring main stroke servomotors

This paper aims to provide researchers and engineers with evidence that sensorless machine variable monitoring can achieve tool wear monitoring in broaching in real production environments, reducing production errors, enhancing product quality, and facilitating zero-defect manufacturing. Additionally, broaching plays a crucial role in improving the quality of manufacturing products and processes. These aspects are especially pertinent in aeronautical manufacturing, which serves as the experimental case in this study. The research presents findings that establish a correlation between the variables of a broaching machine’s servomotors and the condition of the broaching tools. The authors propose an effective method for measuring broaching tool wear without external sensors and provide a detailed explanation of the methodology, enabling reproducibility of similar results. The results stem from three trials conducted on an electromechanical vertical broaching machine, utilizing cemented carbide grade broaching tools to broach a superalloy Inconel 718 test piece. The machine data collected facilitated the training of a set of machine learning models, accurately estimating tool wear on the broaches. Each model demonstrates high predictive accuracy, with a coefficient of determination surpassing 0.9.Thanks are addressed to MCIN/AEI/10.13039/501100011033/and European Union NextGenerationEU/ PRTR” - Proyectos de Transición Ecológica y Transición Digital , Quolink: A new way to assess quality in manufacturing processes by merging process data in high connected production systems in aeroturbines, Ref TED2021-130044B-I00. Thanks are also addressed to Basque, Spain for the support of University research groups, grant IT1573-22. Thanks are also due to European commission by H2020 project n. 958357, and it is an initiative of the Factories-of-the-Future (FoF) Public Private Partnership, project InterQ Interlinked Process, Product and Data Quality Framework for Zero-Defects Manufacturing. Results were analyzed by models developed in Project KK-2022/0065 Lanverso and Hatasu. This work was also partially supported by the Spanish Ministerio de Asuntos Económicos y Transformación Digital and the European Union NextGenerationEU through the project LocoForge: Mimbres instantiation for railways and Industry 5.0 vertical sectors (grant TSI-063000- 2021-47), funded by the Plan for Recovery, Transformation and Resilience