European Commission logo
English English
CORDIS - EU research results
Content archived on 2024-04-30

Product Quality state based fabrication in Global production enviro nment


Objectives and content
The objectives of the proposed research are the develop
an individual product process chain control technology
for high quality, welded structures, and at the same time
save considerable costs by eliminating the need of
corrective actions during the production cycle.
A unique approach is taken by integrating quality control
with on line process planing and control through out the
process chain of each individual part produced.
The production cycle in shipbuilding will in the future
take place at geographical separate production plants.
The increasing co-operation within European shipbuilding
to optimise the use of facilities and the developments
within the IT-area with respect of product models are the
basis for these developments.
In such an global production environment the information
generated on the state of quality in a reliable and
generic form, for each processing step is essential and
adds to the already existing needs for product quality
state exchange in a single production plant because of
the need for documentation of quality.
It is the aim to develop methods to locate, identify and
quantify, for each processing step, the deviations from
the specified output quality. The information generated
on the state of quality in one processing step is
forwarded to subsequent processing steps thus affecting
in a dynamically manner the production planning, process
execution, control and monitoring, and serves to update
the CAD input model to represent the real conditions.
For a heavy industry end-user such as the ship building
industry (large, welded structures) this approach is more
cost beneficial than working with close tolerances
requiring expensive and sophisticated machinery.
At the same time, this method will generate information
on the quality state of each component or product
emanating from the individual processing steps.
The proposed work addresses a big problem in heavy
industries, such as the ship building industry, as
illustrated by the following example. The largest single
cost in building a large steel ship is the effort of
compensating for loss of value through insufficient
quality associated with the individual manufacturing
steps. Build of a large product like a ship consists of
a hierarchy of several sub-assemblies and components, put
together in several process chains, introducing a high
risk of accumulated output errors. In order to reduce
the effects of such error accumulation, corrective
actions are necessary in every step off the process
chain, in order to compensate for value loss introduced
by the previous processes. Hence, an important parameter
in reducing the resource spending and the fabrication
period for steel assembly is the reduction of needed
extra processing time caused by insufficient output
quality of the previous processing steps. Insufficient
quality generates expensive repairs or additional work
for downstream assembly. In addition, non-scheduled work
has detrimental effect on the production planning and
necessitates the implementation of buffers.
It has been calculated that the cost of insufficient
quality, i.e. wasted effort, through out the building
process constitutes 28% of the labour cost, corresponding
to 25 MECU pr. year for a typical shipyard. It is the
objective of this project to reduce these costs
considerably by establishing the knowledge of each
component's or sub-assembly's output quality of each
manufacturing step, and use this information as a
prerequisite of the subsequent step. Basically, this
project enables us to determine the exact physical
properties of individual part and send this information
in advance to subsequent processing station which can
prepare operation according to the real conditions. The
effect on operation and planning of operation is evident.

Call for proposal

Data not available


Odense Steel Shipyard Ltd
EU contribution
No data

5100 Odense C

See on map

Total cost
No data

Participants (6)