Intelligent machine tool prototype operates like a 3D printer
January 7, 2016
Researchers at Kobe University Graduate School of Engineering, Japan, have developed a prototype machine tool that they state can manufacture metal components and operates like a 3D printer. The prototype, recently exhibited at Emo Milano 2015, could speed up the manufacture of custom-made products such as dental implants and artificial bones, potentially shortening production times and reducing costs.
The project, led by Professor Shirase Keiichi, is part of Kobe University’s ongoing research into intelligent machine tools. This is one of three Kobe University projects in the category of “Innovative design and manufacturing technologies” selected for the Strategic Innovation Promotion Program, a project headed by the Japanese Cabinet Office’s Council for Science, Technology and Innovation. In June 2015 Kobe University used funding from this program to establish the 3D Smart Manufacturing Centre, which will be used to pursue interdisciplinary research and business-academia collaborations.
Currently most machine tools for metal cutting follow instructions from a programme that is manually prepared in advance. However, in addition to the huge amount of labour required to create each programme, this method has potential issues, as the machines cannot make adjustments to the machining process or respond to unforeseen problems. Metal components can also be shaped using metal Additive Manufacturing, but this too has disadvantages in that the metal powder used can be expensive and the surface of the finished component is often poor quality.
The prototype created by Professor Shirase’s team marks a shift from providing machine tools with instructions to entrusting machine tools with the machining operation. If you prepare a 3D model and a material model of the component, the machine tool itself will determine the optimum machining process using a database of machining information and cutting conditions. This development, states Professor Shirase, could potentially pave the way for intelligent manufacturing systems, reduced costs, and faster production times.
