This study presents an experimental and numerical investigation into the stress field in cylinders manufactured from photosensitive resin using the Masked Stereolithography (MSLA) technique. For material characterization, tensile and bending test data from resin specimens were utilized. The stress field in resin disks was experimentally analyzed using photoelasticity and Digital Image Correlation (DIC) methods, subjected to compressive loads, according to the cylinder–plane contact model. Images were captured during the experiments using polarizing film and a low-cost CPL lens, coupled to a smartphone. The experimental results were compared with numerical and analytical simulations, where the formation of fringes and regions indicating the direction and magnitude of normal and shear stresses were observed, with variations ranging from 0.6% to 8.2%. The convergence of the results demonstrates the feasibility of using parts produced with commercially available photosensitive resin on non-professional printers for studying contact theory and stress fields. In the future, this methodology is intended to be applied to studies on stress in gears.

This study investigates the mechanical behavior of water-washable photosensitive resins used in masked stereolithography (mSLA) 3D printing, evaluating the effect of post-curing time (0, 5, 10, 30, and 60 min) and printing orientation (Flat [XY], Vertical [Z], and On-edge [XZ]) on the material characteristics. Specimens were manufactured according to ISO 527-2 type 1B and ISO 178 standards for tensile and bending tests, respectively. A Matlab algorithm was developed to automate the processing of experimental data. This tool enabled the extraction of parameters to fit distinct mathematical models for the elastic (linear) and nonlinear (polynomial) regimes, allowing the material response to be characterized at different curing times and print orientations. These models were implemented in Ansys Workbench for comparison with experimental results. The results show that increasing the post-curing time from 0 to 60 min raises the elastic modulus from 964.5 to 1892.4 MPa in the Flat [XY] orientation and from 774 to 1661.2 MPa in the Vertical [Z] orientation for tensile testing. In bending testing, the Flat [XY] orientation presented the best mechanical properties, while the Vertical [Z] and On-edge [XZ] orientations showed similar behavior. The numerical simulations adequately reproduced the experimental results, validating the developed constitutive models. Finally, a stress–strain correlation model is presented that enables estimation for any post-curing time between 0 and 60 min. This study provides essential data for optimizing 3D printing processes and developing structural applications with photopolymer resins.

The industrial sector faces one of the biggest challenges in decarbonization, mainly due to the high costs associated with the development and implementation of low-carbon, energy-efficient technologies and solutions. The long lifespan of industrial assets and infrequent replacement contribute to maintaining high levels of energy consumption and emissions. As electric motors represent a significant portion of energy consumption in industries, improving their efficiency generates substantial reductions in consumption, energy demand, and emissions, thus optimizing overall energy performance. This article proposes an integrated guideline for the application of multi-criteria decision-making (MCDM) methods, computational thinking (CT), and technical standards in industrial energy management problems. To validate this proposal, the guidelines were applied to a real-world case of electric motor selection in an industrial complex. In this context, the structured analysis of the problem, when based on computational thinking, MCDM methods, and technical standards, provides transparency and traceability to decisions. The motor-selection case study, which incorporated computational thinking and MCDM tools (AHP/TOPSIS) aligned with technical standards, demonstrated that these integrated guidelines can substantially improve decision-making in industrial contexts by structuring selection problems and aligning them with the strategic objectives of organizations.