In paper also includes further increasing the level of

In
industrial areas, time-to-market for product development and time-to-customer
for order fulfillment are key challenges faced by manufacturing companies. These
challenges hold for single part production as well as batch production. In this
paper, process planning, CAPP approaches, existing approaches for automated discovery
of planning knowledge, feature technology, automated process planning along
with its illustration using a feedback data are discussed. Process planning
relates the activities between stages of product design and its manufacturing.
This paper depicted that various automation approaches for process planning
problems exist with the assumption of a structured database with process
planning knowledge in the form of Fuzzy rules of if-then relations. Efforts for
continuous analysis and to update these databases are required to enable
process planning according to latest information. An opportunity to reduce the costs
and efforts required for setting up and updating databases for process planning
rules are presented in KDD (Knowledge Discovery in Databases) approach. This
paper also depicted that focusing on machining parameters and typical process
routes, no KDD-based approaches exist, that enable a comprehensive deduction of
process planning rules for all four planning problems on macro planning level.
The main theme of the paper is to present comprehensive approach pertaining
five steps to deduce process planning rules for CAPP systems automatically. The
integrated approach presented in this paper also includes further increasing
the level of accuracy and conciseness of the initial process sheet information,
as well as updating the planning rules and assumptions following a control loop 

First step
contained the component features which were to be used in the statistical analysis.
In second step, the selected component features and the
associated feedback data from previous production orders are transformed and
pre-processed. In step three, data mining, statistical interdependencies
between the component features and process elements are identified. To address
the four planning problems, a sequence of data mining stages along with the
deduction of planning rules from the information discovered were outlined and
the steps of the approach have been illustrated using real production data.
Following three stages are structured to address the data mining and
interpretation activities of the KDD 

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Deduction of
process planning rules is done in next step i.e. the interdependencies
identified in the data mining stage were transformed into planning rules to use
them for CAPP planning algorithms. Based on a
comparison of planned and actual process information, the planning information is
updated in step five. At the end, this paper applied the described approach to
a data set of a case study at the “Laboratory
for Machine Tools and Production Engineering”. The
component features selected for analysis are weight, internal and external diameters,
length as well as number and position of drillings. To determine the decision
tables for resource allocation, classification trees were calculated for each
of the data set resources. The paper concludes with an aim for future work that
research will be carried out to cope with the remaining planning problems of
step three by selecting and adjusting effective data mining approaches.

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