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Simulating manufacturing.

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.

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 motions.

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 situations.

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 configurations.

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 improvements.

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 range.

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 robots.

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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Copyright 1984 Gale, Cengage Learning. All rights reserved.

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Author:Dawson, Gary A.
Publication:Tooling & Production
Date:Dec 1, 1984
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