Optimal design of the machining setup in terms of installed machines, cutting tools and process parameters is of paramount importance for every manufacturing company. In most of the metal cutting companies, all choices related to machine eligibility and cutting parameters selection typically come from heuristic approaches and follow supplier indications or base on the skill of experienced machine operators. More advanced solutions, such as model-based and virtual approaches, are adopted less frequently mainly due to the lack of these techniques in grasping the underlying knowledge successfully. Aim of this work is to introduce a synthetic graphical representation of machining centers and cutting tools capabilities, to provide an accessible way to evaluate the feasibility and close-to-limit conditions of the cutting process. Taking inspiration from previous scientific works from the measurement engineering field, a set of 2D and 3D graphs are presented to map machine, tools and process capabilities, as well as their obtainable manufacturing performances and expectable tool life. This approach synthesizes the nominal data coming from different sources (catalogues, database, tool model geometries etc.) and the real cutting tools parameters used during the production phase. Some examples are provided to show the potential of this graphical evaluation in supporting process planning and decision-making and in formalizing the machining setup knowledge. Further developments are devoted to extend the method to other manufacturing processes, including hybrid processes. At the same time, an in-process data gathering software will be integrated for building a solid database that can be used by an autonomous multi-technological process selector, as well as by a pre-process condition advisor in an Industry 4.0 oriented way.

A graphical method for performance mapping of machines and milling tools

Parenti, Paolo;Cacciatore, Francesco;Ratti, Andrea;Annoni, Massimiliano
2018

Abstract

Optimal design of the machining setup in terms of installed machines, cutting tools and process parameters is of paramount importance for every manufacturing company. In most of the metal cutting companies, all choices related to machine eligibility and cutting parameters selection typically come from heuristic approaches and follow supplier indications or base on the skill of experienced machine operators. More advanced solutions, such as model-based and virtual approaches, are adopted less frequently mainly due to the lack of these techniques in grasping the underlying knowledge successfully. Aim of this work is to introduce a synthetic graphical representation of machining centers and cutting tools capabilities, to provide an accessible way to evaluate the feasibility and close-to-limit conditions of the cutting process. Taking inspiration from previous scientific works from the measurement engineering field, a set of 2D and 3D graphs are presented to map machine, tools and process capabilities, as well as their obtainable manufacturing performances and expectable tool life. This approach synthesizes the nominal data coming from different sources (catalogues, database, tool model geometries etc.) and the real cutting tools parameters used during the production phase. Some examples are provided to show the potential of this graphical evaluation in supporting process planning and decision-making and in formalizing the machining setup knowledge. Further developments are devoted to extend the method to other manufacturing processes, including hybrid processes. At the same time, an in-process data gathering software will be integrated for building a solid database that can be used by an autonomous multi-technological process selector, as well as by a pre-process condition advisor in an Industry 4.0 oriented way.
46th SME North American Manufacturing Research Conference, NAMRC 2018
Machine capability; milling; optimization; performance mapping; predictive manufacturability; production planning; tool mapping; Industrial and Manufacturing Engineering; Artificial Intelligence
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1064643
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