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.
- Bushlya, Volodymyr, handledare
- Ståhl, Jan-Eric, Biträdande handledare
|Tilldelningsdatum||2019 juni 14|
|Status||Published - 2019|
Place: Lecture Hall M:E, M-Building, Ole Römers väg 1, Lund University, Faculty of Engineering LTH
Name: Archenti, Andreas
Affiliation: KTH Royal Institute of Technology, Stockholm, Sweden.
- Produktionsteknik, arbetsvetenskap och ergonomi