The quality of a part is of paramount importance to manufacturers. For CNC machining, part quality can be influenced by a multitude of factors including the condition of the cutting tool, process parameters, temperature fluctuations and the expertise of the operator. Although smart manufacturing technologies can improve production performance, the value of these solutions can remain unclear to manufacturers. In this project, a multi-criteria decision-making analysis is conducted on alternatives for the machining process: 1) the baseline scenario -- conventional machining without real-time monitoring or predictive modeling, 2) machining with tool condition monitoring (TCM), 3) predictive modeling of the ambient conditions via computational fluid dynamics (CFD), and 4) machining with TCM and predictive modeling via CFD.In machining operations, unmonitored tool wear can lead to unscheduled downtime and lost revenue. TCM can introduce several advantages including extending the useful life of cutting tools, improving part quality, and reducing unexpected costs. TCM techniques can be categorized into direct and indirect sensing methods, with studies predominantly employing cutting force and vibration sensors. This project first investigates the feasibility of TCM via thermal imaging for the face milling of Al 6061 and A36 Mild Steel. A positive trend is observed between the surface roughness of the workpiece and the cutting tool temperature, which makes it a good candidate for a low-cost solution for TCM.To investigate the variability in the ambient conditions of a machining environment, the airflow and thermal conditions have been modeled with CFD and validated with real-time sensor data acquired inside the S3 Lab’s manufacturing laboratory. The models developed consist of the laboratory with the HVAC system (no machining), the laboratory with the HVAC system and the CNC machine in use, and these scenarios with machine operators. Although the initial development of the CFD model can be time-intensive depending on the size of the production environment and the complexity of the factory, the model can be used to reduce energy demand by optimizing the layout of the factory floor and HVAC design and to predict temperature fluctuations to aid in production scheduling.Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used to compare the alternative machining scenarios (i.e., conventional machining, TCM, CFD, and TCM with CFD). TOPSIS is given preference due to the ease of understanding of the linguistic parameters used. The evaluation criteria includes performance, sustainability, and cost metrics. Accordingly, the results obtained from the decision matrix provide visibility into feasible and sustainable machining alternatives, which can help companies frame a roadmap for future adoption at a large-scale factory setting. The tool ultimately aims to provide a means for evaluating a large range of smart manufacturing technologies while considering the status quo. As multiple stakeholders are surveyed, the preferred criteria for smart and sustainable machining are also identified, to determine the technology that would be best suited for the organization. Future research will expand the simulations conducted for this research to larger scales (from the laboratory to a factory setting) and further develop the MCDM tool with industry feedback.

Data-Driven Multi-Criteria Decision-Making for Smart and Sustainable Machining

Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2021/volume/85543

ASME 2021 International Mechanical Engineering Congress and Exposition (IMECE2021)

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