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Development of a method to improve the reliability and availabilityof machine tools

Objective


Task 1.1. Decomposition of the Machine Tool for R&A analysis, machine tool builders and end-users view on critical components and events.
Task 1.2. Definition of suitable information required for R&A design, Life Cycle Cost trade-off analysis, choice of critical components, failures and needed signals for condition monitoring.
Task 1.3. Development of the REACH database SW and the link between the database and existing CAD systems.
Task 2.1. Definition of sampling plans and procedures for the machine tool builders and end-users data collection, preliminary analysis of the necessary statistical algorithms for components R&A evaluation.
Task 2.2. Definition and development of two test benches for accelerated life tests on machine tools critical components.
Task 2.3. Development of the CNC data acquisition SW for condition monitoring, integrating control internal signals and external sensors.
Task 3.1. Preliminary evaluation of collected data, development of an integrated test and simulation model for R&A performance evaluation and design platform, development of new R&A design guidelines and methodologies.
Task 3.2. Development of the condition monitoring modules for improvement of the R&A performances of existing machine tools, development of the lifetime estimating algorithms based on load history and of the MMI extension for parameterization and display of the results. This is a running task.
Task 4.1. Verification of the new design methodology, the integrated test and simulation model for R&A performances, redesign of machine tool parts.
Task 4.2. Verification of the developed Condition Monitoring system in the partners machine tools.
High maintenance costs and production losses resulting from machine breakdowns are pressing problems for today's manufacturers. In order to increase the reliability and availability (R&A) of machine tools and manufacturing systems, two major problems must be solved: - How to take the right design decision for maximum reliability, starting from the early stages of machine development? - How to continuously obtain in-process data about the actual state of the machine and its components as a basis for targeted and highly efficient maintenance? Both issues are closely linked. Taking the proper design decisions requires information about the behaviour of the machine components under realistic loads. These loads are today widely unknown, except for laboratory data which does not reflect actual load cases, load history and interdependencies between components. Based on a powerful system for large-scale measurements of real operating loads new design methods and supporting software-systems can close the information gap between machine tool design and real process loads. On the other hand, such a system can be used as well to continuously monitor the actual machine conditions during operation. Thus machine downtime for unpredictable breakdowns can be prevented. It is the objective of this project to improve the R&A of machine tools by using control-internal signals for solving the mentioned two problems. At modern machine tools signals of digital drives are easily accessible and can be processed by control-intemal software applications. This makes it easy to apply such a software to many machines at little costs. Thus these control-internal signals will be used by the here to be developed system in two ways: - for new design methodologies taking reliability and availability into account. This enables a prediction and planning of R&A supported by here developed software tools; and - for a condition monitoring system being directly implemented into the control and thus enabling direct access to important information. This also contains the data acquisition for the design process. It is expected that with the help of this integrated two-fold approach the following results can be achieve within a period of two-three years after the end of the project: - Increase of the actual average machine tool Availability from 90-93% to 99% - Increase of the actual average production systems Availability from 75-80% to 90-95% - Reduction of about 20% (for the end-users) of the operating, support and lack of production total costs.

Coordinator

COMAU SPA
Address
Via Rivalta 30
10095 Grugliasco
Italy

Participants (7)

AACHEN UNIVERSITY OF TECHNOLOGY
Germany
Address
Steinbachstrasse 53B
52074 Aachen
Centro di Studi Industriali SAS di Taddei Ing. Franco
Italy
Address
Via Tintoretto
20093 Cologno Monzese
Doimak SA
Spain
Address
4,Poligono San Lorenzo
20870 Elgoibar
IVECO France S.A.
France
Address
1,Avenue Puzenat
71140 Bourbon Lancy
SIEMENS AG
Germany
Address
80,Frauenauracher Strasse 80
91056 Erlangen
SMT Machine AB
Sweden
Address
20,Bjoeunoevaegen
721 22 Västeräs
Scania CV ab
Sweden
Address

151 87 Sødertaelje