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316 L stainless steel alloy is potentially the most used material in the selective laser melting (SLM) process because of its versatility and broad fields of applications (e.g. medical devices, tooling, automotive, etc.). That is why producing fully functional parts through optimal printing configuration is still a key issue to be addressed. This paper aims to present an entirely new framework for simultaneously reducing surface roughness (SR) while increasing the material processing rate in the SLM process of 316L stainless steel, keeping fundamental mechanical properties within their allowable range.

Considering the nonlinear relationship between the printing parameters and features analyzed in the entire experimental space, machine learning and statistical modeling methods were defined to describe the behavior of the selected variables in the as-built conditions. First, the Box–Behnken design was adopted and corresponding experimental planning was conducted to measure the required variables. Second, the relationship between the laser power, scanning speed, hatch distance, layer thickness and selected responses was modeled using empirical methods. Subsequently, three heuristic algorithms (nonsorting genetic algorithm, multi-objective particle swarm optimization and cross-entropy method) were used and compared to search for the Pareto solutions of the formulated multi-objective problem.

A minimum SR value of approximately 12.83 μm and a maximum material processing rate of 2.35 mm³/s were achieved. Finally, some verification experiments recommended by the decision-making system implemented strongly confirmed the reliability of the proposed optimization methodology by providing the ultimate part qualities and their mechanical properties nearly identical to those defined in the literature, with only approximately 10% of error at the maximum.

To the best of the authors’ knowledge, this is the first study dealing with an entirely different and more comprehensive approach for optimizing the 316 L SLM process, embedding it in a unique framework of mechanical and surface properties and material processing rate.

This study, carried out in the bilingual and bicultural border area of South Texas, is an exploration of bilingual preservice teachers’ identity formation and their experiences and beliefs about literacy and biliteracy during an undergraduate class focused on learning about emergent literacy in the bilingual classroom. This study is based on a sociocultural approach to learning and identity development, and research that explores how bilingual teachers’ identity is shaped through their participation in cultural and linguistic practices. The purpose of this practitioner research is to provide insights into preservice teachers’ identities as they start to explore literacy and biliteracy practices. Two research questions guide the study: What experiences about literacy and biliteracy development do prospective teachers identify as meaningful? How do these experiences contribute to define bilingual preservice teachers’ identities? Findings indicate that bilingual preservice teachers’ identities are shaped by cultural and linguistic experiences that define the bilingual and bicultural dynamics of the region. Two predominant types of experiences impact bilingual preservice teachers’ beliefs about teaching, learning, and literacy/biliteracy development. Particularly significant in defining their perceptions are the lessons learned from meaningful others – especially mothers and teachers – and certain relevant memories regarding effective practices they experienced when learning to read and write. Implications for teacher education preparation of bilingual teachers are identified.