TY - M-10242
AU - Agarwal, Aditya
AU - Ujawane, Raunak
AU - Mali, Balaji
TI - Part Inspection Tracking and Forecasting in Machining and Assembly Plants
T2 - Scientific Research Journal of Science, Engineering and Technology

PY - 2025
VL - 3
IS - 1
SN - 2584-0584

AB - Production facilities require regular part inspections to ensure that manufactured components meet design specifications and quality standards. These inspections maintain quality and generate valuable data about production patterns, such as peak and low periods, shift-wise performance, and potential bottlenecks. This paper presents a digital Part Inspection Tracking & Forecasting system integrated with a cloud database for real-time recording of part inspection statuses (categorized as OK, NOT OK, or Pending). In parallel, we develop a time-series forecasting module using a Recurrent Neural Network (RNN) model called the Long Short-Term Memory (LSTM) model. By leveraging the historical inspection data captured through the system, the LSTM model forecasts the number of parts expected for inspection over a future horizon. By facilitating data-driven decision-making, the integrated strategy provides a real-time dashboard for forecast visualization and data entry, supporting Industry 4.0 goals. With the aid of this useful technology, plants will be better equipped to predict and prepare for future demands. Many areas like warehousing, transportation, and dispatch will be eased by optimizing resource allocation based on predicted inspection volumes. In this paper, we have implemented all these relevant tools and some quality-of-life improvements to make our research and software invaluable for manufacturing and assembly plants. We describe the system architecture (including a Python Tkinter-based user interface and cloud backend), the data processing and model training approach, and experimental results on an automotive manufacturing dataset. Our findings show that the LSTM-based model outperforms conventional techniques in identifying both short-term trends and long-term seasonal patterns, producing accurate short-term projections of inspection load. This makes it possible for stakeholders to better allocate resources and predict workloads. We achieved RMSE values of less than 1 (error is the number of parts predicted vs. actual), indicating high correlation and accuracy.

KW - Time Series Forecasting
KW -  Long Short-Term Memory
KW -  ARIMA
KW -  LSTM
KW -  Industry 4.0
KW - Part Inspection Automation
KW - Quality 4.0
DO -