Estimate Completion Time Based On Execution Speed

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Knowing how much time remains for a job while it's running is super helpful for operators when they're trying to schedule their work. But, you know, predicting exactly how fast something will run can be tricky because it depends on all sorts of details about how the firmware handles acceleration. So, let's dive into how we can make those "time remaining" estimates way more accurate and useful. This article explores a solution to provide a more accurate "time remaining" estimate by observing the progress during a job and updating the estimate based on the actual observed progress through the g-code versus the predicted one based on first-principles.

The Challenge: Predicting Execution Speed

Alright, let's be real. Figuring out how long a job will actually take is tough. You might think you can just look at the g-code, do some math, and boom – you've got your answer. But it's never that simple, is it? The problem is, the execution speed depends on a ton of factors, especially how the firmware handles acceleration. Different machines, different settings, different materials – they all throw a wrench in the gears.

So, why is this such a big deal? Well, think about it from the operator's point of view. They're trying to juggle multiple jobs, allocate resources, and meet deadlines. If they have a good idea of how long each job will take, they can plan their day effectively. But if the estimates are way off, it throws everything into chaos. It's like trying to bake a cake without knowing how long it needs to be in the oven – you're just guessing and hoping for the best!

Factors Affecting Execution Speed:

  • Machine Capabilities: Each machine has its own limitations in terms of acceleration, deceleration, and maximum speed.
  • Firmware Optimization: The way the firmware interprets and executes g-code commands can significantly impact the overall speed.
  • Material Properties: Different materials require different cutting speeds and feed rates.
  • Job Complexity: Intricate designs with lots of fine details will naturally take longer to execute.

Why Accurate Estimates Matter:

  • Improved Scheduling: Operators can create more realistic schedules and allocate resources effectively.
  • Reduced Downtime: Knowing when a job will finish allows for timely preparation for the next task.
  • Enhanced Productivity: Efficient scheduling leads to increased overall productivity.
  • Better Customer Satisfaction: Accurate estimates help manage customer expectations and ensure timely delivery.

The Solution: Observe and Adapt

So, how do we tackle this prediction problem? Well, there's a clever trick we can use: watch what's happening during the job and adjust the estimate based on what we actually see. The idea is to compare the progress through the g-code to what we initially predicted. If the machine is running faster than expected, we decrease the estimated time remaining. If it's running slower, we increase it. It's like having a GPS for your CNC machine – it constantly updates its ETA based on real-time traffic conditions.

This approach is especially useful because it doesn't rely on perfect knowledge of the machine's inner workings. Instead, it learns from experience and adapts to the specific conditions of each job. It's like learning to ride a bike – you might start with training wheels and a few stumbles, but eventually, you get the hang of it and can adjust your balance as needed.

Key Components of the Solution:

  • Progress Tracking: Monitor the actual progress of the job by tracking the executed g-code commands.
  • Initial Prediction: Generate an initial estimate of the total execution time based on first-principles (e.g., g-code analysis).
  • Comparison and Adjustment: Compare the actual progress to the predicted progress and adjust the estimated time remaining accordingly.
  • Continuous Update: Continuously update the estimate throughout the job to account for changes in execution speed.

Benefits of the Solution:

  • Improved Accuracy: The estimated time remaining becomes more accurate as the job progresses.
  • Adaptability: The solution adapts to different machines, materials, and job complexities.
  • Real-time Feedback: Operators get a clear and up-to-date view of the job's progress.

Diving Deeper: How It Works

Okay, let's get into the nitty-gritty of how this observation-based estimation works. The basic idea is to break the job down into smaller chunks and track how long it takes to complete each chunk. Then, we can use this information to project how long it will take to complete the remaining chunks.

Step-by-Step Breakdown:

  1. Initial Prediction: Before the job starts, we need to make an initial guess about how long it will take. This could be based on the total length of the g-code, the programmed feed rates, or some other heuristic. It doesn't have to be perfect, but it should be in the ballpark.
  2. Progress Monitoring: As the job runs, we keep track of which g-code commands have been executed. This could be done by monitoring the serial communication between the control software and the machine, or by analyzing the g-code file directly.
  3. Time Measurement: For each chunk of g-code, we measure how long it takes to execute. This could be done using a timer in the control software, or by reading the timestamps from the machine's log files.
  4. Estimation Adjustment: After each chunk is completed, we compare the actual execution time to the initial prediction. If the actual time is shorter than expected, we reduce the estimated time remaining. If it's longer, we increase the estimate.
  5. Display Update: The updated estimated time remaining is displayed to the operator, so they can see how the job is progressing.

Example Scenario:

Let's say we have a job that's predicted to take 60 minutes. After 10 minutes, we've completed 20% of the g-code. If we were right on track, we'd expect to have 50 minutes remaining. But let's say we actually completed 30% of the g-code in those 10 minutes. That means we're running faster than expected! So, we adjust our estimate and predict that the job will now finish in about 43 minutes.

Important Considerations:

  • Chunk Size: The size of the g-code chunks can affect the accuracy of the estimate. Smaller chunks provide more frequent updates, but they also introduce more overhead. Larger chunks are less sensitive to short-term fluctuations, but they might not catch changes in execution speed as quickly.
  • Averaging: To smooth out the estimates, it's helpful to average the execution times over multiple chunks. This can reduce the impact of random variations in speed.

Measuring Prediction Accuracy

Not only does this approach give you a better "time remaining" estimate, but it also helps you understand how accurate your initial predictions are. By comparing the predicted execution time to the actual execution time, you can identify areas where your prediction strategy needs improvement. It's like getting a report card on your forecasting skills!

How to Measure Accuracy:

  • Calculate the Error: For each job, calculate the difference between the predicted execution time and the actual execution time. This gives you a measure of the prediction error.
  • Track the Error Over Time: Keep track of the prediction error for a series of jobs. This will allow you to see how your prediction accuracy is changing over time.
  • Analyze the Results: Look for patterns in the prediction errors. Are you consistently overestimating or underestimating the execution time? Are there certain types of jobs that are more difficult to predict?

Using the Data to Improve Predictions:

Once you've collected enough data on prediction accuracy, you can use it to improve your prediction strategy. For example, if you find that you're consistently underestimating the execution time for jobs with lots of fine details, you could adjust your prediction algorithm to account for this factor.

This feedback loop is essential for continuously improving the accuracy of your "time remaining" estimates. It's like learning from your mistakes and getting better with each iteration.

Conclusion

So there you have it, guys! By observing the progress during a job and updating the estimate based on the actual observed progress through the g-code versus the predicted one based on first-principles, we can get a much more accurate "time remaining" estimate. This not only helps operators schedule their work more effectively but also provides valuable insights into the accuracy of our prediction strategies. And remember, folks, accurate estimates aren't just about convenience – they're about efficiency, productivity, and keeping those customers happy! This helps us design even better prediction strategies in the future. Cheers to smarter, more accurate CNC machining!