Part Inspection Tracking and Forecasting in Machining and Assembly Plants

Title

Part Inspection Tracking and Forecasting in Machining and Assembly Plants

Author

1. Aditya Agarwal, Student, Thapar Institute of Engineering & Technology, India
2. Raunak Ujawane, Other, Dana Anand India Pvt. Ltd., India
3. Balaji Mali, Other, Dana Anand India Pvt. Ltd., India

Abstract

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.

Keywords

Conclusion

In this paper, we have demonstrated a comprehensive approach to modernizing and optimizing the part inspection process in a manufacturing plant through digital tracking and AI-driven forecasting. The proposed system replaces manual record-keeping with a networked application that logs inspection results to a cloud database in real-time, aligning with Industry 4.0 paradigms of interconnectivity and data availability. Building upon this robust data pipeline, we trained a Long Short-Term Memory model to forecast future part inspection volumes. The LSTM model proved capable of learning the complex temporal patterns of production and inspection data, yielding highly accurate forecasts (with substantial error reductions compared to traditional ARIMA benchmarks) and operationally sound.

Our results show that even short-term forecasts (on the order of hours to a day ahead) can provide significant value in a machining and assembly context. Plant managers can take preemptive measures like redistributing the personnel, balancing production lines, or planning maintenance at the right times by understanding the anticipated inspection load ahead of time. Because resources may be matched to guarantee that every item receives the appropriate attention during inspection, this leads to increased productivity and perhaps superior quality. The ability to anticipate peaks in inspection also means critical quality issues (if they correlate with volume surges) could be identified and addressed more promptly.

The LSTM model's success in capturing daily and weekly seasonality and its adaptability to new data underscores the importance of bringing machine learning into manufacturing operations. It reinforces findings from broader Industry 4.0 research that data-driven methods, when applied to the rich streams of production data, can unlock efficiency gains and previously inaccessible insights. Our work contributes explicitly to the relatively niche but essential problem of part inspection forecasting – a link between production output and quality control that historically hasn't seen much automation beyond basic SPC (Statistical Process Control) charts.

There are several avenues for future work and enhancements. First, we plan to incorporate feedback mechanisms where the model can be retrained or fine-tuned on the fly as new data comes in (online learning). This would help the system remain accurate over time, especially if there are gradual changes in production rates or shifts in working patterns (e.g., adding a new shift or changes in inspection procedures). Our second goal is to improve the input properties of the model. Only historical counts of examined components are being used. The projections might be strengthened, particularly for longer time horizons, by using inputs such as the projected production schedule, machine uptime statistics, or even environmental conditions, which can occasionally impact fault rates. This moves towards a multivariate time-series forecasting framework beyond just the series' history.

From a deployment perspective, one future enhancement is integrating the system with enterprise software (like MES – Manufacturing Execution Systems or quality management systems) so that the insights flow upstream and downstream automatically. For instance, if the forecast predicts a potential overload, the MES could alert maintenance to ensure all inspection tools are functional or alert procurement if a particular part type seems to be failing more (if we extend forecasting to each part type's OK/NOT OK counts).

In conclusion, the Part Inspection Tracker and Forecasting system exemplifies how adopting digital tools and AI in a traditional manufacturing setting can yield smart factory benefits. We demonstrated that our in-house LSTM model, trained on real production data, can reliably forecast production-related metrics (inspection counts) and thus act as a surrogate for production forecasting and quality monitoring. This contributes to improved transparency and agility in operations. As manufacturing plants continue to embrace digital transformation, we expect that such approaches will become increasingly common, turning factories into environments where data from every process—machining, assembly, or inspection—flows into predictive models that drive informed, timely decisions. The ultimate vision is a brilliant production line where resources are optimally orchestrated through predictive analytics, minimizing downtime and ensuring quality in a manner that outperforms even the most experienced human planners.

Author Contrubution

Mr. Aditya Agarwal conceptualized the approach, trained/tested the models, and wrote the code. Mr. Raunak Ujawane supervised the write-ups and helped in dataset building; Mr. Balaji Mali is the 2nd supervisor and contributed immensely to the methodology and problem statement definition.

Funding

There was no funding received for the research work.

Conflict of Interest

The authors affirm that the research was conducted without any financial or commercial relationships that could be interpreted as a potential conflict of interest.

Data Sharing Statement

The data used to study, train, and test our work is provided only upon reasonable request from the corresponding author. Feel free to contact the corresponding author for any guidance needed to reproduce the setup, implementation, or any other use case.

Software And Tools Use

Python 3.8 (including libraries like PyTorch, NumPy, Pandas, etc.) Google Collab VS Code Windows 10/11

Acknowledgements

The authors thank Thapar Institute of Engineering & Technology (TIET) for granting access to Google Collab for training and testing the machine learning models used in this study.