Topology optimization has evidenced its capacity to provide new optimal designs in many different disciplines. However, most novel methods are difficult to apply in commercial software, limiting their use in the academic field and hindering their application in the industry. This article presents a new open methodology for solving geometrically complex non-self-adjoint topology optimization problems, including stress-constrained and stress minimization formulations, using validated FEM commercial software. The methodology was validated by comparing the sensitivity analysis with the results obtained through finite differences and solving two benchmark problems with the following optimizers: Optimality Criteria, Method of Moving Asymptotes, Sequential Least-Squares Quadratic Programming (SLSQP), and Trust-constr optimization algorithms. The SLSQP and Trust-constr optimization algorithms obtained better results in stress-minimization problem statements than the methodology available in ABAQUS^®. A Python implementation of this methodology is proposed, working in conjunction with the commercial software ABAQUS^® 2023 to allow a straightforward application to new problems while benefiting from a graphic user interface and validated finite element solver.

Additive manufacturing (AM) has swiftly emerged as a substitute for conventional methods such as machining and injection moulding. Its appeal is attributed to accelerated prototyping, improved sustainability, and the capacity to fabricate intricate shapes. Nonetheless, the size constraints of additive manufacturing components require the assembly of smaller 3D-printed elements to create larger structures. This study investigates the tensile properties of scarf joints (SJs) created from several polymers, including ABS, PETG, and PLA, adhered with Araldite^® 2015 and Sikaforce^® 7752 adhesives. The characteristics of the adherends were assessed prior to examining the adhesive efficacy in the SJ configuration. Experimental evaluations quantified failure modes, joint strength, assembly stiffness, and energy at failure, comparing findings with predictions from a cohesive zone model (CZM). The objective was to determine the ideal combination of materials and adhesives for enhanced joint performance. Results indicated that joint performance is greatly affected by the adherend material, adhesive selection, and scarf angle. PLA and Araldite^® 2015 typically exhibited optimal strength and stiffness, but Sikaforce^® 7752 demonstrated enhanced energy absorption for extended bonding lengths.