Machine tool geometric inaccuracies are frequently corrected through the use of compensation tables available in machine tool controllers. Each compensation table contains a set of values that determine the incremental change in the commanded position of an axis given the current positions of the axes. While a five-axis machine tool, for example, can have at most 25 compensation tables, most machine tool controllers limit the number of compensation tables that can be implemented and provide constraints on the combinations of compensation tables that can be utilized. This work presents an artificial intelligence-based methodology to select and populate the optimal set of machine tool compensation tables when these limitations and constraints exist. Using data from an industrial five-axis machine tool to construct a kinematic error model, simulation results for the proposed methodology and a heuristic based on the impact of individual compensation tables when selecting six compensation tables are compared, and the proposed methodology is found to outperform the heuristic. The proposed methodology and a solution based on a full set of compensation tables are experimentally implemented on the machine tool and the mean volumetric error resulting from the proposed methodology is found to be only 25 μm less than the volumetric error resulting from the full set of tables. The proposed methodology is then implemented in two more simulation studies where constraints are imposed on which combination of compensation tables could be used and which type of compensation tables could not be utilized. The resulting mean volumetric error was 7.0 and 28.3 μm greater, respectively, than the unconstrained solution.
The International Journal of Advanced Manufacturing Technology – Springer Journals
Published: Mar 10, 2017
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera