polymer-aging-ml / docs /PROJECT_TIMELINE.md
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πŸ“… PROJECT_TIMELINE.md

AI-Driven Polymer Aging Prediction and Classification System

Intern: Jaser Hasan

βœ… PHASE 1 – Project Kickoff and Faculty Guidance

Tag: @project-init-complete

Received first set of research tasks from Prof. Kuppannagari

  • Reeived research plan
  • Objectives defined: download datasets, analyze spectra, implement CNN, run initial inference

βœ… PHASE 2 – Dataset Acquisition (Local System)

Tag: @data-downloaded

  • Downloaded Raman .txt (RDWP) and FTIR .csv data (polymer packaging)
  • Structured into:
  • datasets/rdwp
  • datasets/ftir

βœ… PHASE 3 – Data Exploration & Spectral Validation

Tag: @data-exploration-complete

  • Built plotting tools for Raman and FTIR
  • Validated spectrum structure, removed malformed samples
  • Observed structural inconsistencies in FTIR multi-layer grouping

βœ… PHASE 4 – Preprocessing Pipeline Implementation

Tag: @data-prep

  • Implemented preprocess_dataset.py for Raman
  • Applied: Resampling -> Baseline correction -> Smoothing -> Normalization
  • Confirmed reproducible input/output behavior and dynamic CLI control

βœ… PHASE 5 – Figure2CNN Architecture Build

Tag: @figure2cnn-complete

  • Constructed Figure2CNN modeled after Figure 2 CNN from research paper
  • Figure2CNN: 4 conv layers + 3 FC layers
  • Verified dynamic input length handling (e.g., 500, 1000, 4000)

βœ… PHASE 6 – Local Training and Inference

Tag: @figure2cnn-training-local

  • Trained Raman models locally (FTIR now deferred)
  • Canonical Raman accuracy: 87.29% Β± 6.30%
  • FTIR accuracy results archived and excluded from current validation
  • CLI tools for training, inference, plotting implemented

βœ… PHASE 7 – Reproducibility and Documentation Setup

Tag: @project-docs-started

  • Authored README.md, PROJECT_REPORT.md, and ENVIRONMENT_GUIDE.md
  • Defined reproducibility guidelines
  • Standardized project structure and versioning

βœ… PHASE 8 – HPC Access and Venv Strategy

Tag: @hpc-login-successful

  • Logged into CWRU Pioneer (SSH via PuTTY)
  • Setup up FortiClient VPN as it is required to access Pioneer remotely
  • Explored module system; selected venv over Conda for compatibility
  • Loaded Python 3.12.3 + created polymer_env

βœ… PHASE 9 – HPC Environment Sync

Tag: @venv-alignment-complete

  • Created environment_hpc.yml
  • Installed dependencies into polymer_env
  • Validated imports, PyTorch installation, and CLI script execution

βœ… PHASE 10 – Full Instruction Validation on HPC

Tag: @prof-k-instruction-validation-complete

  • Ran Raman preprocessing and plotting scripts
  • Executed run_inference.py with CLI on raw Raman .txt file
  • Verified consistent predictions and output logging across local and HPC

βœ… PHASE 11 – FTIR Path Paused, Raman Declared Primary

Tag: @raman-pipeline-focus-milestone

  • FTIR modeling formally deferred
  • FTIR preprocessing scripts preserved and archived for future use
  • All resources directed toward Raman pipeline finalization
  • Saliency, FTIR ingestion, and train_ftir_model.py archived

βœ… PHASE 12 – ResNet1D Prototyping & Benchmark Setup

Tag: @resnet-prototype-complete

  • Built ResNet1D architecture in models/resnet_cnn.py
  • Integrated train_model.py via --model resnet
  • Ran initial CV training with successful results

βœ… PHASE 13 – Output Artifact Isolation

Tag: @artifact-isolation-complete

  • Patched train_model.py to save:
    • figure2_model.pth, resnet_model.pth
    • raman_figure2_diagnostics.json. raman_resnet_diagnostics.json
  • Prevented all overwrites by tying output filenames to args.model
  • Snapshotted as reproducibility milestone. Enabled downstream validation harness.

βœ… PHASE 14 – Canonical Validation Achieved

Tag: @validation-loop-complete

  • Created validate_pipeline.sh to verify preprocessing, training, inferece, plotting
  • Ran full validation using Figure2CNN with reproducible CLI config
  • All ouputs verified: logs, artifacts, predictions, plots
  • Declared Raman pipeline scientifically validated and stable

⏭️ NEXT - Results Analysis & Finalization

  • Analyze logged diagnostics for both models
  • Conduct optional hyperparameter tuning (batch size, LR)
  • Begin deliverable prep: visuals, posters, cards
  • Resume FTIR work only after Raman path is fully stablized and documented & open FTIR conceptual error is resolved