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Tool Life and Cutting Data Modelling in Metal Cutting : Testing, Modelling and Cost Performance

Johansson, Daniel LU (2019)
Abstract
One of the most important production processes in industry is metal cutting. If a product is not a machined metal part, it is likely that the mould, die and tools used to produce the product or parts of the product are machined. The tools, machines and time spent add to the cost of the finished product and both industry and academia spend considerable effort in increasing efficacy and minimizing the environmental impact of these processes.
Models are often referred to both by scientists and industry. These models can help understanding and also predict the outcome of a process and the outcome of intended improvement measures. Models can also be used to minimize empirical testing and “rule of thumb,” thus allowing for shorter lead times... (More)
One of the most important production processes in industry is metal cutting. If a product is not a machined metal part, it is likely that the mould, die and tools used to produce the product or parts of the product are machined. The tools, machines and time spent add to the cost of the finished product and both industry and academia spend considerable effort in increasing efficacy and minimizing the environmental impact of these processes.
Models are often referred to both by scientists and industry. These models can help understanding and also predict the outcome of a process and the outcome of intended improvement measures. Models can also be used to minimize empirical testing and “rule of thumb,” thus allowing for shorter lead times and a more reliable production system.
One area of modelling in metal cutting is tool life and wear modelling. Today, tool providers support customers with digital software, suggesting tools for a given operation, process data and expected tool life. To facilitate this support tool life models are used, mainly those based on the Taylor equation and the Colding equation.
This research aims to investigate how one should model tool life for varying cutting data. Empirical data and modern computational power have been used to validate and optimize the process of modelling tool life. Commonly used tool life models have been investigated and the Colding model is suggested for tool life modelling. The process of collecting empirical input data to minimize the time and material consumed have also been investigated.
The author also presents a methodology based on a combination of tool life models and cost modelling as decision support for the selection of tools, workpiece material and process parameters. This approach can be used to minimize tool consumption, time consumption and reduce production costs. (Less)
Please use this url to cite or link to this publication:
author
supervisor
opponent
  • Professor Archenti, Andreas, KTH Royal Institute of Technology, Stockholm, Sweden.
organization
publishing date
type
Thesis
publication status
published
subject
keywords
metal cutting, tool life, tool wear, cutting data, process cost, Colding model, part cost
pages
186 pages
publisher
Department of Mechanical Engineering, Lund University
defense location
Lecture Hall M:E, M-Building, Ole Römers väg 1, Lund University, Faculty of Engineering LTH
defense date
2019-06-14 10:15:00
ISBN
978-91-7895-124-6
978-91-7895-123-9
language
English
LU publication?
yes
id
215d0914-76ce-4b4b-90df-003dff5ab10b
date added to LUP
2019-05-20 11:04:59
date last changed
2020-05-06 15:30:01
@phdthesis{215d0914-76ce-4b4b-90df-003dff5ab10b,
  abstract     = {{One of the most important production processes in industry is metal cutting. If a product is not a machined metal part, it is likely that the mould, die and tools used to produce the product or parts of the product are machined. The tools, machines and time spent add to the cost of the finished product and both industry and academia spend considerable effort in increasing efficacy and minimizing the environmental impact of these processes.<br/>Models are often referred to both by scientists and industry. These models can help understanding and also predict the outcome of a process and the outcome of intended improvement measures. Models can also be used to minimize empirical testing and “rule of thumb,” thus allowing for shorter lead times and a more reliable production system.<br/>One area of modelling in metal cutting is tool life and wear modelling. Today, tool providers support customers with digital software, suggesting tools for a given operation, process data and expected tool life. To facilitate this support tool life models are used, mainly those based on the Taylor equation and the Colding equation.<br/>This research aims to investigate how one should model tool life for varying cutting data. Empirical data and modern computational power have been used to validate and optimize the process of modelling tool life. Commonly used tool life models have been investigated and the Colding model is suggested for tool life modelling. The process of collecting empirical input data to minimize the time and material consumed have also been investigated.<br/>The author also presents a methodology based on a combination of tool life models and cost modelling as decision support for the selection of tools, workpiece material and process parameters. This approach can be used to minimize tool consumption, time consumption and reduce production costs.}},
  author       = {{Johansson, Daniel}},
  isbn         = {{978-91-7895-124-6}},
  keywords     = {{metal cutting; tool life; tool wear; cutting data; process cost; Colding model; part cost}},
  language     = {{eng}},
  publisher    = {{Department of Mechanical Engineering, Lund University}},
  school       = {{Lund University}},
  title        = {{Tool Life and Cutting Data Modelling in Metal Cutting : Testing, Modelling and Cost Performance}},
  url          = {{https://lup.lub.lu.se/search/files/64577539/Daniel_Johansson_Tool_Life_and_Cutting_Data_Modelling_KAPPAN.pdf}},
  year         = {{2019}},
}