Pick and place automation is a technologically advanced solution to an otherwise straightforward plan, which may require vision systems. The vision systems have the potential of adding great features, although this also comes at a cost, complexity, and maintenance load that is not necessarily justified. The question regarding whether to use vision systems or not lies with manufacturing managers, plant engineers, and automation decision makers. Without information about the actual production environment, part variability, and system objectives, the appropriate decision cannot be made.
Understanding the Core Pick and Place Task
Pick and place automation merely involves the movement of parts between a known location and destination. When position, orientation, and presentation are equal, then the problem is inherently mechanical, but not computational. With these applications, reliable fixturing, tooling, and motion control can be better than the more elaborate vision solutions.
However, very rarely do factories operate under perfectly controlled circumstances. Variability in part geometry, human loading, or upstream processes causes uncertainty.
When Vision Is Truly Necessary.
In cases where the automation job cannot repeat a position of the parts, then vision systems are necessary. Common scenarios include:
- Components are randomly located in conveyor belts or bins.
- Several iterations of parts that were not special-tooled and ran through the same cell.
- Processes that require direction, e.g., welding, labeling, or inspection.
- Irreparable upstream inconsistency, which is more expensive to fix.
In these cases, vision assists the robot in locating, objectifying, and aligning parts in a dynamic fashion. Using a robot welding as an example, the position of seams or the correction of part deformation before the welding process would have to be determined by the use of vision.
Vision is also valuable when the product mix is high, as well as when changeovers are few. Software can be adjusted to a vision-guided system, rather than retooling the fixtures to fit each SKU, which may be faster and with less downtime.
When the Vision Becomes Overkill.
Despite being a handy tool, vision is applied in areas where it does not add much actual value. In case parts are already separated, such as out of a mold, in a holding, or a very precise feeder, then vision can rectify an issue that had not been initially created.
Vision can be overkill when:
- Mechanical components are already oriented components.
- Tolerances are minimal and predictable.
- Cycle time is very short.
- Its conditions are inhuman (dust, oil, vibration).
- Limited maintenance resources exist.
The vision in these situations causes new categories of failure: dirty lenses, light drift, calibration issues, and software bugs. A well-designed mechanical solution will offer uptime and lower lifetime expenses than a vision-intensive system.
Mechanical and Software Compensation.
The other common automation mistake is to use the vision as a compensator for bad mechanical design. Vision should not pay off sloppy presentation of parts when simple changes to guides, tooling, or conveyors can eliminate the variability at the point of origin.
Often, custom automation solutions are best realized through the maximization of the mechanical repeatability. It is then vision that should be added only to the extent that it brings quantifiable value, which can be processing of edge cases or future flexibility.
Ownership, Long-term, and Cost.
The first project cost is not the sole problem that is affected by vision systems. They influence:
- Commissioning time
- Operator training
- Spare parts strategy
- Calibration and maintenance.
Starting capability may not be as important as the total cost of ownership in the case of mid-size to extensive industrial facilities. A simple pick and place system having lower dependencies may be suitable within a life span of 10 years, compared to a higher-level system.
It is this factor that has made many successful automation projects begin with a well-defined minimum intelligence definition. Most of the time, it is easier to add vision afterwards as opposed to deleting it when the system has been designed.
Effective Decision Rules.
As a rule, to decide whether vision is required, the decision makers ought to pose:
- Can part variability be mechanically reduced on the upstream side?
- Is it really necessary to be flexible, or is it just a presumption?
- What happens in case of failure of the vision system?
- Who is going to keep it running and keep it?
- Does the vision bring more throughput, or does it only make it possible?
The queries suit best at the first stages of choosing hardware.
Vision as a Tool, not a Default
Vision systems are robust, but they are not predestined for pick and place automation. In the real factories, the only thing that works is the selection of the most straightforward solution that has the guarantee of meeting the production needs.
By focusing on process understanding, mechanical engineering, and realistic variability, the manufacturers are able to undertake tailored automation, which is durable, manageable, and can be scaled, exercising vision where it is required to be and not where it is not.
Complex systems do not consist of simple systems, but of those that perform all the shifts, all the days.
