The following is a table recording the state of serial interface availability in the molding department as of 11-12-25. Device interfaces examined were the Degate and Press Robot Controllers, the Press UI and the eDart as currently configured. Some notable observations were that several workcells employing RJ3i series controllers had only RS-232-C and PCMCIA ports available as potential interfaces. Also of note were that several Press UIs (specifically Pathfinder and Camac series UIs) apparently did not have any serial interface available.

NOTE: Some entries were not examined as of 1-12-25 but will be populated the coming week.

1. All USB ports found were USB Gen 2.0 compatible
2. All R-30i series Robot Controllers featured at least one USB
3. Many press UIs featured either 1 or more USB ports, or an RJ45 port. None were in use.
4. Most eDarts which were in use had either a USB port open, or the 8-pin COM 3 port. Some had RJ45 free.
5. Only some of the RJ3i series had an available RJ-45 port, when Option 2 was installed.

IoT Topology for Presses 2, 3, 31, 36, 24, 23, 10 and 6 — One USB WiFi connection, one USB to RS-232-C connection

IoT Topology for Presses 27, 14, and 15 — One USB WiFi connection, one RJ45 connection to Option 2 in R-J3 controllers

IoT Topology for Presses 11, 13, 38, 5, 8, 7, 34, 32, 33, 35, 37, 20, 21, 29, amd 28 — Two USB WiFi connections

Once again these are drafts, Security and Monitoring sections can include various functions, Program Backup Storage and version control is illustrated here. Version control can help elucidate repetitive program corrections and possibly elucidate some persistent robot motion deviation over time. For example, in theory, if several shifts over the course of a week constantly did gate cutter program adjustments of the same coordinate changes, we might be able to establish a “tell” pattern for am actuator issue. Pattern recognition can potentially streamline Tech operations and increase production, decrease downtime. Another possible feature can be generating a prospective maintenance routine at the beginning of the shift based on non-critical alarms and error codes which will be often ignored on the react-respond model of operation we currently seem to employ.

Delta based Analysis of Program Edits

Here is a generated summary of what my intuition says could increase production and reduce downtime. This would probably be even more applicable to process tech operations and be able to generate insights into press issues or other peripheral systems. Basically I am thinking that predictive maintenance and operation adjustment would be superior, if practical, to reactive maintenance and process adjustment like we are doing now.

We could see if this is any good with these basic IoT robot controller operations, minimal capital investment.

Generated Summary

Here’s a detailed summary of the delta-based analysis system for robot program version control:

Concept Overview: A system which tracks and analyzes changes made to robot programs across different workcells and shifts. By storing and comparing these changes (deltas) over time, we can identify patterns that indicate equipment wear, process drift, or systematic issues that might otherwise go unnoticed.

Data Collection Process:

1. Every program edit on a robot controller is captured and stored locally on the Raspberry Pi
2. Each change is recorded with relevant metadata:
   • Timestamp
   • Shift information
   • Operator identification
   • Specific modifications (position adjustments, speed changes)
   • Controller/robot identification
3. These deltas are then securely transmitted to the main computer for analysis

Analysis Capabilities:

1. Pattern Recognition
   • Identifies common adjustments across different cells running the same part
   • Tracks position corrections over time to spot systematic drift
   • Correlates changes with environmental factors (temperature, time of day)
2. Predictive Maintenance
   • Detects gradual position drift that might indicate:
     • Bearing wear
     • Belt/drive system degradation
     • Calibration drift
   • Tracks speed adjustments that could signal:
     • Motor performance issues
     • Mechanical resistance problems
     • Lubrication needs
3. Process Improvement Opportunities
   • Identifies common adjustments that might indicate:
     • Fixture design issues
     • Tool wear patterns
     • Programming inefficiencies
   • Reveals shift-specific patterns that could point to:
     • Training needs
     • Environmental factors
     • Process inconsistencies

Business Benefits:

1. Cost Reduction
   • Prevent unplanned downtime through early warning
   • Optimize maintenance scheduling
   • Reduce scrap and rework
2. Quality Improvement
   • Better consistency across shifts
   • Earlier detection of process drift
   • More uniform production across different cells
3. Process Optimization
   • Data-driven program improvements
   • Better understanding of tool life
   • Optimized preventive maintenance schedules
4. Knowledge Management
   • Capture tribal knowledge from experienced operators
   • Improve training programs
   • Document successful problem resolutions

Implementation Requirements:

1. Hardware
   • Raspberry Pi 4 B at each workcell
   • Network infrastructure
   • Central analysis server/computer
2. Software
   • Version control system
   • Machine learning analysis tools
   • Visualization dashboard
   • Database management system
3. Process
   • Operator training
   • Standard procedures for program modifications
   • Review and action protocols for identified patterns

The system essentially creates a “learning factory” where each program adjustment contributes to a broader understanding of process behavior and equipment health, enabling proactive rather than reactive maintenance and continuous process improvement.

This solution represents a significant step toward Industry 4.0 practices, leveraging existing equipment and processes to generate actionable insights without requiring major capital investment in new machinery.