Joshua Priest , Sabino Ayvar-Soberanis , Javier Dominguez-Caballero , Peace Onawumi , Zekai Murat Kilic , David Curtis
{"title":"评估可变螺距和螺旋牛鼻子刀具的切削力系数识别方法和力模型","authors":"Joshua Priest , Sabino Ayvar-Soberanis , Javier Dominguez-Caballero , Peace Onawumi , Zekai Murat Kilic , David Curtis","doi":"10.1016/j.cirpj.2024.09.010","DOIUrl":null,"url":null,"abstract":"<div><div>The mechanistic approach is commonly implemented to predict and optimise the cutting forces in milling processes to prevent tool breakages, reduce tool wear, reduce form error, and improve surface quality. To implement this method, the cutting force coefficients (CFCs), that characterise the mechanics of the process, must be calculated. This study compares the accuracy of the predicted cutting forces for variable pitch and helix bull-nose milling tools using a rapid testing (RT) optimisation-based mechanistic CFC identification method that only requires a single angular cut with increasing radial engagement to the traditional mechanistic approach that requires several straight cuts. Along with developing a hybrid technique that combines variation in feed rate and radial engagement. The traditional radial, tangential, and axial (RTA) force model is also compared with the frictional and normal rake face (UV) force model that is independent of the local tool rake and inclination angles which is a necessary for bull nose tools. The RT and the developed hybrid CFC identification method with the UV force model predicted the average <span><math><msub><mrow><mi>F</mi></mrow><mrow><mi>x</mi></mrow></msub></math></span>, <span><math><msub><mrow><mi>F</mi></mrow><mrow><mi>y</mi></mrow></msub></math></span> and <span><math><msub><mrow><mi>F</mi></mrow><mrow><mi>z</mi></mrow></msub></math></span> cutting forces to within 7.1 %, 4.3 %, and 3.8 % error, respectively. These methods were slightly less accurate than the traditional method, however they have significant industrial benefits because they have can be used to identify CFCs with either a single cut, or from any tool-path with chip-load variation, respectively. The RTA force model predicted the average cutting forces similarly to the UV force model, however, the UV force model had lower errors using the rapid RT testing method at the extreme corners of the experimental design space.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Assessment of cutting force coefficient identification methods and force models for variable pitch and helix bull-nose tools\",\"authors\":\"Joshua Priest , Sabino Ayvar-Soberanis , Javier Dominguez-Caballero , Peace Onawumi , Zekai Murat Kilic , David Curtis\",\"doi\":\"10.1016/j.cirpj.2024.09.010\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The mechanistic approach is commonly implemented to predict and optimise the cutting forces in milling processes to prevent tool breakages, reduce tool wear, reduce form error, and improve surface quality. To implement this method, the cutting force coefficients (CFCs), that characterise the mechanics of the process, must be calculated. This study compares the accuracy of the predicted cutting forces for variable pitch and helix bull-nose milling tools using a rapid testing (RT) optimisation-based mechanistic CFC identification method that only requires a single angular cut with increasing radial engagement to the traditional mechanistic approach that requires several straight cuts. Along with developing a hybrid technique that combines variation in feed rate and radial engagement. The traditional radial, tangential, and axial (RTA) force model is also compared with the frictional and normal rake face (UV) force model that is independent of the local tool rake and inclination angles which is a necessary for bull nose tools. The RT and the developed hybrid CFC identification method with the UV force model predicted the average <span><math><msub><mrow><mi>F</mi></mrow><mrow><mi>x</mi></mrow></msub></math></span>, <span><math><msub><mrow><mi>F</mi></mrow><mrow><mi>y</mi></mrow></msub></math></span> and <span><math><msub><mrow><mi>F</mi></mrow><mrow><mi>z</mi></mrow></msub></math></span> cutting forces to within 7.1 %, 4.3 %, and 3.8 % error, respectively. These methods were slightly less accurate than the traditional method, however they have significant industrial benefits because they have can be used to identify CFCs with either a single cut, or from any tool-path with chip-load variation, respectively. The RTA force model predicted the average cutting forces similarly to the UV force model, however, the UV force model had lower errors using the rapid RT testing method at the extreme corners of the experimental design space.</div></div>\",\"PeriodicalId\":56011,\"journal\":{\"name\":\"CIRP Journal of Manufacturing Science and Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-10-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"CIRP Journal of Manufacturing Science and Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1755581724001500\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, MANUFACTURING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"CIRP Journal of Manufacturing Science and Technology","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1755581724001500","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
Assessment of cutting force coefficient identification methods and force models for variable pitch and helix bull-nose tools
The mechanistic approach is commonly implemented to predict and optimise the cutting forces in milling processes to prevent tool breakages, reduce tool wear, reduce form error, and improve surface quality. To implement this method, the cutting force coefficients (CFCs), that characterise the mechanics of the process, must be calculated. This study compares the accuracy of the predicted cutting forces for variable pitch and helix bull-nose milling tools using a rapid testing (RT) optimisation-based mechanistic CFC identification method that only requires a single angular cut with increasing radial engagement to the traditional mechanistic approach that requires several straight cuts. Along with developing a hybrid technique that combines variation in feed rate and radial engagement. The traditional radial, tangential, and axial (RTA) force model is also compared with the frictional and normal rake face (UV) force model that is independent of the local tool rake and inclination angles which is a necessary for bull nose tools. The RT and the developed hybrid CFC identification method with the UV force model predicted the average , and cutting forces to within 7.1 %, 4.3 %, and 3.8 % error, respectively. These methods were slightly less accurate than the traditional method, however they have significant industrial benefits because they have can be used to identify CFCs with either a single cut, or from any tool-path with chip-load variation, respectively. The RTA force model predicted the average cutting forces similarly to the UV force model, however, the UV force model had lower errors using the rapid RT testing method at the extreme corners of the experimental design space.
期刊介绍:
The CIRP Journal of Manufacturing Science and Technology (CIRP-JMST) publishes fundamental papers on manufacturing processes, production equipment and automation, product design, manufacturing systems and production organisations up to the level of the production networks, including all the related technical, human and economic factors. Preference is given to contributions describing research results whose feasibility has been demonstrated either in a laboratory or in the industrial praxis. Case studies and review papers on specific issues in manufacturing science and technology are equally encouraged.