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This paper aims to present a predictive model approach to estimate the tensile behavior of polylactic acid (PLA) under uncertainty using the fused deposition modeling (FDM) and American Society for Testing and Materials (ASTM) D638’s Types I and II test standards.

The prediction approach combines artificial neural network (ANN) and finite element analysis (FEA), Monte Carlo simulation (MCS) and experimental testing for estimating tensile behavior for FDM considering uncertainties of input parameters. FEA with variance-based sensitivity analysis is used to quantify the impacts of uncertain variables, resulting in determining the significant variables for use in the ANN model. ANN surrogates FEA models of ASTM D638’s Types I and II standards to assess their prediction capabilities using MCS. The developed model is applied for testing the tensile behavior of PLA given probabilistic variables of geometry and material properties.

The results demonstrate that Type I is more appropriate than Type II for predicting tensile behavior under uncertainty. With a training accuracy of 98% and proven presence of overfitting, the tensile behavior can be successfully modeled using predictive methods that consider the probabilistic nature of input parameters. The proposed approach is generic and can be used for other testing standards, input parameters, materials and response variables.

Using the proposed predictive approach, to the best of the authors’ knowledge, the tensile behavior of PLA is predicted for the first time considering uncertainties of input parameters. Also, incorporating global sensitivity analysis for determining the most contributing parameters influencing the tensile behavior has not yet been studied for FDM. The use of only significant variables for FEA, ANN and MCS minimizes the computational effort, allowing to simulate more runs with reduced number of variables within acceptable time.

Time standards are essential to plan and analyze manufacturing processes. A key element of process planning that is not generated from a typical computer‐aided process plan (CAPP) is the process time standards. Generative process planning that includes time standards is particularly needed in the construction steel building (CSB) industry due to variability in projects (orders) size and content. Hence, the purpose of this paper is to focus on incorporating time standards into CAPP of CSB.

Empirical formulas are developed to generate time standards for variant steel beams based on their CAD files (design parameters and geometry) and process parameters (operational conditions). A Motion and Time Study (MTS) is used to estimate times for manual work elements such as load/unload activities and to validate the generated time standards. A generic parametric model is developed with Excel and integrated into the CAPP system to automatically estimate the standard time of each process operation.

Results showed that developing the time standards module for process operations and integrating its spreadsheets into a generative CAPP has helped process planners to arrive at better estimates of process parameters and has helped production management and the overall project management process in CSB industry.

The application of the proposed approach is not limited to CSB industry but it can also contribute to the continuing growth of CAPP applications in other industries.

The study is unique since it incorporates time standards into the architecture of CAPP system for accurate time and cost estimation and effective resource allocation and project management and it utilizes motion and time study (MTS) to collect complementary process data and validate the model‐generated cycle times.