Intelligent Bending Workstations
Main Participants: Satyandra K. Gupta, D.A.
Bourne, K. Kim, and S.S. Krishnan.
Sponsors: This project was sponsored by Amada.
Keywords: Automated Process Planning and heet Metal Bending.
In order to offer flexibility, better quality control, higher degree of
automation, and improved productivity, machine tool manufacturers are combining
material processing, material handling, and part positioning systems into
single integrated manufacturing cells. Programming such integrated cells
manually is a time consuming task and can become a major bottleneck in effectively
using such cells. Process planning and part programming time directly affect
the lot sizes that can be economically produced on these cells. We believe
that automated process planning systems can significantly enhance the throughput
of such integrated cells and dramatically lower the economic lot sizes.
Depending upon the level of process plan details, the process planning systems
can be divided into two different types: macro planners and micro planners.
Macro planners deal with the higher level process planning decisions such
as selection of machines, selection of operation types, ordering operations,
selection of tools etc. Micro planners deal with the lower level planning
decisions such as selection of operation parameters, NC code generation etc.
To create a completely automated process planning system, we need both capabilities.
Traditionally, these two types of planners were developed independently and
were interfaced later. Due to strong interactions among various components
of an integrated manufacturing cell, macro planning and micro planning functions
need to be tightly integrated into a single system.
Main Results and Their Anticipated Benefits
We have developed an automated process planning system for a robotic sheet-metal
bending press-brake. Our system is based on the generative approach and performs
both macro as well as micro planning. Once a CAD design is given for a new
part, the system determines: the operation sequence, the tools and robot grippers
needed, the tool layout, the grasp positions, the gage and the robot motion
plans for making the part. These plans are sent to the press-brakes controller,
which executes them and then returns gaging information back to the planning
system for plan improvement. A second plan is then formulated, which reduces
the gaging time by incorporating the reduced uncertainty in the part location.
Our system is based on a distributed architecture. We have a separate planner
for each specialized component of the robotic press-brake. These specialized
planners collaborate with a central operation planner to perform the process
planning. Currently, our system consists of a central operation planner and
three specialized planners: tooling, grasping, and moving. The central operation
planner proposes various alternative partial sequences and each specialized
planner evaluates them based on its objective function. A distributed architecture
allows us to encapsulate specialized planning knowledge of each component
into a separate module and provides an opportunity for using a different
representation and problem solving technique for each planning module. This
architecture also provides a highly modular environment for adding more specialized
planners to the system. Our system presents a significant improvement over
the state-of-the-art. After the release of final CAD file, using our system,
we can produce the first part in less than an hour. For full production automation
of sheet metal bending, the resulting planning and execution time is reduced
for the first part significantly.
In our system, there is one central planner that sends out queries to specialized
planners. The central planner keeps track of the query results and develops
a near optimal plan. Specialized planners act as servers, which solve problems
in grasping, tooling and moving for given partial operation sequences. Note
that all of the planners communicate in the Feature Exchange Language (FEL),
which is a human readable, extendible language. This FEL syntax is regular,
human readable and easily processed by each module.
In our system, the part's design is presented to the planning system, which
automatically plans all aspects of the setup and the execution steps for making
the part. A person is then guided step-by-step in the setup process and
the plan is sent to the controller. The controller has a built-in interpreter
for executing the plan on the bending machine. The part is loaded by a separate
loading-unloading robot, and the bending robot starts to bend the part bend-by-bend.
At some point, the robot may interfere with a bend-line, and as a result
the robot hands the part to a repositioning gripper, so that the robot can
alter its grasp position. The bending, and regrasping are continued as needed
until the part is complete, at which time the unloading robot grasps the finished
part and stacks it. The results of this production run are used to produce
a better and faster plan, since most of the gage information can be reused
making successive parts (i.e., the robot is not accurate but it is repeatable).
This modified plan is then used to make the rest of the parts in the batch.
Our system has been implemented using the C++ programming language. For
geometric modeling and reasoning we have used NOODLES geometric kernel. For
graphical interface we have used HOOPS graphics library. All message passing
among planners is accomplished by Feature Exchange Language.
Our system architecture offers the following advantages:
- The central planner performs macro planning. Specialized planners
perform micro planning. Communications between the central planner and specialized
planners allow us to tightly integrate macro and micro planning functions.
- Specialized knowledge is encapsulated into modular planners. Each
specialized planner uses the representation most efficient for its planning
activity and employs the most efficient problem solving technique.
- The modular nature of the architecture also makes this system easy
to update. For example, each specialized planner can be be upgraded without
changing the rest of the system.
The following paper provides more details on the above-described results.
- S.K. Gupta, D.A. Bourne, K. Kim, and S.S. Krishnan. Automated process
planning for sheet metal bending operations. Journal of Manufacturing Systems,
For additional information and to obtain copies of the above paper please
Dr. David Bourne
Carnegie Mellon University
5000 Forbes Avenue Pittsburgh, PA