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.
The most important improvement in metal the cutting industry is the continuous utilization of cutting tools and tool condition monitoring system. In the metal cutting process, the tool condition has to be administered either by operators or by online condition monitoring systems to prevent damage to both machine tools and workpiece. Online tool condition monitoring system is highly essential in modern manufacturing industries for the rising requirements of cost reduction and quality improvement. This paper summaries various monitoring methods for tool condition monitoring in the milling process that have been practiced and described in the literature.
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We thank J. Delgado for all image analysis measurements; J. Cammack, K. Landry, C. Henry, P. Roberts, J. Bonnette, H. Hoffpauir, C. Conner, and J. Vawter for all milling determinations; N. Gipson for amylose content determinations; L. Murray for consultation on linear models; E. Christensen and S. Simpson for genotypic analysis; I. Roughton for fissuring determinations; and F.-M. Xie for providing the MY1 mapping population. Support for this work has been provided in part by US Department of Agriculture Cooperative State Research, Education and Extension ServiceNational Research InitiativeApplied Plant Genomics Program grant 2004-35317-14867 entitled RiceCAP: A coordinated research, education, and extension project for the application of genomic discoveries to improve rice in the United States. This is contribution 09-002-J from the Kansas Agriculture Experiment Station.
Nelson, J.C., McClung, A.M., Fjellstrom, R.G. et al. Mapping QTL main and interaction influences on milling quality in elite US rice germplasm. Theor Appl Genet 122, 291309 (2011). https://doi.org/10.1007/s00122-010-1445-z
Milling properties, protein content, and flour color are important factors in rice. A marker-based genetic analysis of these traits was carried out in this study using recombinant inbred lines (RILs) derived from an elite hybrid cross Shanyou 63, the most-widely grown rice hybrid in production in China. Correlation analysis shows that the traits were inter-correlated, though the coefficients were generally small. Quantitative trait locus (QTL) analysis with both interval mapping (IM) and composite interval mapping (CIM) revealed that the milling properties were controlled by the same few loci that are responsible for grain shape. The QTL located in the interval of RM42-C734b was the major locus for brown rice yield, and the QTL located in the interval of C1087-RZ403 was the major locus for head rice yield. These two QTLs are the loci for grain width and length, respectively. The Wx gene plays a major role in determining protein content and flour color, and is modified by several QTLs with minor effect. The implications of the results in rice breeding were discussed.
Tan, Y., Sun, M., Xing, Y. et al. Mapping quantitative trait loci for milling quality, protein content and color characteristics of rice using a recombinant inbred line population derived from an elite rice hybrid. Theor Appl Genet 103, 10371045 (2001). https://doi.org/10.1007/s001220100665