Manufacturing processes
are inherently variable, which results in component
and assembly variance. Unless process capability,
variance and tolerancing are fully understood,
incorrect design tolerances may be applied, which
will lead to more expensive tooling, inflated
production costs, high reject rates, product recalls
and excessive warranty costs. A methodology is
described for correctly allocating tolerances and
performing appropriate analyses.
Effects of incorrect tolerances
Tolerances on component and assembly dimensions are
crucial to the success of a medical device.
Incorrectly specified tolerances can lead to more
expensive tooling, high reject rates, product
recalls and substantial warranty costs. However,
specifying tolerances correctly requires an
understanding of process variance and process
capability and this is not always available to the
engineer or draftsman working on the product or
process.
Process variation, typically the result of small
changes in the tooling, process parameters and
materials, means there will always be variation in
the measured dimensions of components. Similarly,
assemblies exhibit variance, partly because of the
variance they inherit from the constituent
components and partly because of variance in
assembly processes.
A medical device’s performance can be strongly
influenced by component and assembly variance, thus
every functionally critical component and assembly
dimension is toleranced. The intention is that every
device will assemble and function as intended,
providing the tolerances are not exceeded because of
the process variance.
However, an incomplete understanding of tolerancing
and the related subject of process variance can lead
to inappropriate tolerances being applied. Even if
undertaken with the best of intentions, a subsequent
tolerance analysis will give misleading results and
wastes resources (remember the adage: garbage in,
garbage out). Furthermore, tolerance analyses can be
poorly executed on sound tolerances. One of the most
common errors is to calculate the worst case
tolerance stacks and then revise the component
tolerances so that the assembly will “always fit
together.” This can result in extremely tight
tolerances on the component dimensions, which
increases tooling and production costs. Indeed, the
processes may be inherently incapable of
consistently producing parts or assemblies within
the stated tolerances. Although 100% inspection can
identify failed assemblies, this is costly,
time-consuming, and the lost production can be
hugely expensive. In some cases, the problem may not
be apparent immediately, which will lead to failures
in the field and, ultimately, product recalls and
substantial warranty costs.
Performing a worst case tolerance analysis can
therefore be a costly mistake. The underlying
reasoning is that manufacturing processes produce
parts with part-to-part and batch-to-batch variance,
and it is extremely unlikely that assemblies will be
produced that consist entirely of components in
their worst-case condition. This may sound like
common sense, but worst-case tolerance stack
analyses are still undertaken on numerous occasions
and these commonly result in poorly allocated
tolerances that cause significant quality-related
production problems.
Optimum timing
Variance- and tolerance-related problems often
result in high reject rates that erode profit
margins. In some cases this occurs during scale-up,
but it can also emerge after a device has been in
full production for a period of several years. It
is, therefore, prudent for companies undertaking due
diligence or risk management exercises to include an
assessment of the capability of a medical device’s
design to ensure there are no potential variance- or
tolerance-related problems.
These exercises are highly worthwhile, yet the best
time to pay attention to variances and tolerances is
when developing new designs or processes. In fact,
they can be considered as early as the conceptual
design phase, when risks can be evaluated prior to
further investment being made. Applying a rigorous
method to allow an informed decision to be made is
much more likely to yield long-term benefits than
taking decisions based on experience or
“guesstimates.” It should not be forgotten that most
of a product’s ongoing quality-related costs are
fixed during the earliest stages of a device’s
development.
Variance and tolerancing clearly have to be
considered together, yet variance also goes
hand-in-hand with process capability. A detailed
discussion of process capability is outside the
scope of this article, but it must be appreciated
that component and assembly dimensions cannot be
properly toleranced without taking into account
process capability.
How to optimise tolerances
A methodology has been developed for allocating and
optimising tolerances and performing appropriate
analyses (Figure 1). The methodology, which makes
use of mathematical modelling and commercially
available software, builds on a thorough knowledge
of toolmaking, manufacturing processes and the
principles of process capability. Clearly, the
methodology is not applied to noncritical dimensions
and assemblies. When it has been used, no toolmaker
or moulder has raised any objections or been unable
to achieve the given tolerances.
The methodology takes a statistical approach, based
on predicted process capability, in which the
component and assembly tolerances are allocated in
accordance with a realistic representation of the
manufacturing processes involved. Commercial
software packages are used: one “as is” to allocate
process-capable tolerances and others as tool kits
for building mathematical models that enable
statistical analyses and optimisations to be
performed.
Starting with the identification of the performance
objectives of the critical assemblies, and therefore
the critical dimensions, rational mathematical
models are developed, based on the nominal
dimensions and tolerances.
Next, using Tolerance Capability Expert1 (TCE), an
expert system software package (Capra Technology,
Walkington, UK), the user selects the proposed
production process, material and design
characteristic to predict the likely process
capability for a given dimension and tolerance.
Importantly, the software manufacturer says analysis
shows a 98% correlation between the predicted
results and data from statistical process control
records.
After using TCE to confirm the original or revised
tolerances, mathematical modelling and analysis are
performed in Excel2 or Mathcad.3 If the overall
predicted process capability is inadequate with
respect to the stated objectives, one or more
aspects of the design or process will need to be
reconsidered before revised data can be analysed.
Excel spreadsheets are used for relatively simple
models and have the advantage that the models can be
easily shared among the project team. Mathcad is
used for more complex models or scenarios; again,
the files can be shared, but this software is not as
widely used as Excel. Depending on the analysis
required, it may be that one or more models,
simulations and analyses are necessary. Both Excel
and Mathcad can be used for performing statistically
based tolerance analyses such as Root Sum Squared4
based on process capability data, as well as
assessments such as variance sensitivity, process
capability and tolerance bands.
Mathcad’s greater capability also supports the
programming of high-level equations such as for
three-dimensional, time-based, spring-loaded or
power-dependent systems, and it enables Monte Carlo
simulations5 to be performed for multi-dimensional
tolerance analyses. Complex assemblies, systems and
processes can therefore be investigated, and
multi-dimensional assessments performed such as
process capability for normal and nonnormal
distributions.
Once a mathematical model has been created, it
provides opportunities for optimisation without
reverting to unrealistically tight tolerances. This
enables selected parameters to be varied with the
aim of achieving the specified goal. Each iteration
is calculated automatically by the PC-based software
and enables a “better” product to be designed,
whether the goal relates to an aspect of
performance, weight, cost or any other parameter
that is built into the model.
Extending uses
The methodology described above has been used, where
appropriate, in the design and development of
medical devices. It is also applicable throughout
the lifecycle of a medical device. It can be applied
during the conceptual design and proof of principle
phases (typically to establish a design’s
manufacturability) and the detailed design phase,
through scale-up and during full production, often
as part of a due diligence exercise or to
investigate quality-related problems.
Quality-orientated programmes such as Six Sigma,
Lean Manufacturing and the Process Analytical
Technologies initiative from the United States Food
and Drug Administration are currently important
topics for manufacturers of medical devices.
Regardless of which of these are adopted, an
understanding of variance and tolerancing will be
highly beneficial.
Furthermore, the approach is not restricted to the
dimensions of components and assemblies; it can be
applied to almost anything that can be modelled
mathematically. For example, Mathcad has been used
to analyse variance in the inhalation airflow
resistance of a medical device, and to investigate
the ranges of thermal comfort experienced in a clean
room. The methodology could also be applied to
processes such as drug production, for example, as
part of a risk analysis or troubleshooting exercise.
The author’s company has invested resources in
developing the Excel and Mathcad tools; other
organisations could replicate the work using these
or similar software packages. TCE is readily
available, but currently it is not widely used in
medical device development. It should also be noted
that commercial packages exist for tolerance
analysis, either as standalone tools or as add-on
modules within suites of computer-aided design
software, although experience has shown these to be
restrictive compared with bespoke mathematical
modelling. Moreover, they do not all assist with the
allocation of process capable tolerances. Without
this starting point, there is a significant risk
that considerable effort will be wasted in the
pursuit of results that turn out to be meaningless
or misleading. All of which underlines the
importance of having a sound understanding of
process capability, variance and tolerancing to
improve quality and reduce cost.
Stuart Kay is Senior Engineering Consultant at Team
Consulting Ltd, Abbey Barns, Duxford Road, Ickleton,
Cambridge CB10 1SX, UK, tel. +44 1799 532 700,
e-mail: stuart.kay@team-consulting.com;
www.team-consulting.com
Copyright 1997 – 2008, Tec-Ease, Inc. All rights reserved.
©2007 Medical Device Technology |
