Simulating manufacturing Essay

The ability to accurately simulate on a computer the operation
robots, transfer machines, conveyors, machine tools, welding machines,
and controls under actual operating conditions can be a valuable tool
for analyzing manufacturing operations. To be able to then integrate
these elements into a simulation of an entire manufacturing system
offers significant economic benefits.

Manufacturing simulation can be used to identify and solve
mechanical problems before machines are built, optimize plant efficiency
by investigating alternative approaches, select the “least
capable” (least expensive) machine for a given task, and
demonstrate the integration of an entire facility.

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Such a simulation capability has been developed by Mechanical
Dynamics Inc. Two programs, Automatic Dynamic Analysis of Mechanical
Systems (ADAMS) and Dynamic Response of Articulated Machinery (DRAM),
have shown their ability to simulate the large displacement of
mechanical systems.

These programs determine displacements and reaction forces of
mechanisms under actual operating conditions. This includes the
application of large forces that drive the analysis into the nonlinear domain where finite-element methods either do not work or have
discontinuous effects. Both programs can simulate mechanical stops,
friction, nonlinear component characteristics, impact, and large

To use this system, the engineer creates a model of the mechanism
being evaluated using standard joints and geometric elements, and then
examines its behavior. Both tabular and graphic outputs are available
for study and analysis.

Of particular concern in the simulation of whole plants are robots
and transfer-type machine tools. The ADAMS program can match individual
robot capabilities to job requirements by evaluating the motions
necessary to accomplish a given task.

Examples of analysis

In fusion welding, for example, differences in joint gap or fitup
caused by manufacturing variations are a major problem for robot
welders. Common solutions package a sensory feedback device–like
vision, magnetic sensing, or electromagnetic tracking–with a very
mobile robot in hopes that this will cover all the possible welding

By examining the range of seam configurations and the motions
required to weld each of them, an ADAMS-based analysis can select the
most cost-effective robot for the job. In many cases, this has proven
to be a less capable and less costly unit than was initially considered.
A relatively simple vision device can scan the actual joints prior to
welding, and the robot control can select the appropriate weld-tip path
from a preprogrammed library to reliably weld the parts.

Painting is another common robot application where simulation can
make a productivity contribution. Requirements like reach, number of
axes of freedom, spray pattern for the desired coverage, and correlation
of motions to avoid collisions can all be defined and evaluated on the
computer using an ADAMS-based simulation.

Even the displacement caused by the reaction force of the
paint-spray jet can be accurately predicted. Actual paint coverage is
calculated by a simple algorithm, and the simulation pinpoints potential
trouble spots in the automated painting operation.

The result of the analysis may be that a different spray pattern is
required, or that a product-design change should be made. Whatever the
case, simulation of the painting operation can often catch quality
problems before they end up in the actual product.

Deflection is also a problem with certain types of lasers used for
welding and inspection. Suspended at the end of a robot arm, these
lasers can generate forces that cause significant displacement. An
ADAMS-based simulation predicts the pattern of this deflection and
allows the robot control to compensate for it. This is particularly
critical in laser-inspection applications where any variation in laser
position produces incorrect readings.

ADAMS simulations are particularly useful in inspection
application. By examining a number of different approaches to the
inspection task, it is possible to find a solution using a less capable
robot than was originally selected. Interferences between the robot and
workpiece can be predicted. Preventing just one collision between a
$50,000+ laser and the part it is measuring would pay for the computer
simulation many times over.

In assembly situations, simulations can predict the deflection
pattern of the robot over time and affect the selection of the
appropriate robot for the task. Again, this may result in choosing a
less sophisticated unit than might have been specified.

Machine loading often requires complex motions. By examining
alternative approaches, computer simulation can often eliminate problems
and production bottlenecks before they happen. When more than one part
is handled, the program can help optimize effector design by determining
the least expensive device that will effectively grasp all part

Computer simulation offers another unique capability. By running a
simulation faster than real time, they can be used to predict wear and
failure modes. Fed back into the design cycle, this information can be
used to yield performance, reliability, and end-user productivity

Fewer changes

The design of complex metalworking machines has always been a
process that is more of an art than a science. A significant portion of
the final cost of these machines is attributed to all the engineering
changes that are required to make the machine perform as expected.

Simulating the operation of a transfer machine on the computer
gives the machine designer an opportunity to identify and eliminate many
potential problems. Factors like machine/workpiece interference,
distortion caused by excess clamping force, and vibration can be
examined and evaluated before any commitment is made to hardware.
Metalcutting machine tools can benefit greatly from accelerated wear
simulation to predict both accuracy drift and failure modes before the
machine is even built.

Obviously, the ADAMS and DRAM programs are not the only
computer-based tools for addressing such problems. Finite-element
analysis is widely used to optimize part designs. The problem with FEA is that these methods only work for displacements within a very narrow
range, whereas ADAMS and DRAM evaluate a system over its full operating

Examining whole plants

In many ways, a transfer machine is a micro example of whole-plant
simulation. The interaction of its various stations and transfer
mechanisms as it processes parts is very similar in principle to what
happens in a real manufacturing operation. All that’s missing are
the detail operations like welding, assembly, and inspection. In
factories of the future, most of these functions will be performed by

The ADAMS and DRAM programs have been applied successfully to
simulate most of the equipment found in a typical manufacturing plant.
The next step will be to tie this experience together into a single
simulation covering the entire facility.

Before the first concrete is poured or the first machine ordered,
alternative approaches to each requirement will be tested and compared.
Each piece of equipment can be located for maximum productivity, with
its capabilities exactly matched to its job requirements. With
simulation eliminating bottlenecks and optimizing work flow before
anything is built, the result can be dramatic productivity improvements
and reduced costs.

Computer-based simulations have held out much promise from the very
beginning, but the reality of simulation technology has never quite
lived up to the need. Now, that appears to be changing, and this could
change the way we approach manufacturing from this point on.

For more information on the ADAMS and DRAM programs, circle E1.


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