Search for: All records

Maraging steels are known for their exceptional strength but suffer from limited work hardening and ductility. Here, we report an intermittent printing approach to tailor the microstructure and mechanical properties of maraging 250 steel via engineering of the thermal history during plasma arc additive manufacturing (PAAM). Through introducing a dwell time between adjacent layers, the maraging 250 steel is cooled below the martensite start temperature, triggering a thermally driven, in-situ martensitic transformation during the printing process. Re-heating or thermal cycling during subsequent layer deposition impedes complete martensitic transformation, enabling coexistence of martensite and retained austenite phases with elemental segregation. The enrichment of Ni in the austenite phase promotes stabilization of the retained austenite upon cooling down to room temperature. The retained austenite is yet metastable during deformation, leading to stress-induced martensitic transformation under loading. Specifically, a 3 min interlayer dwell time produces a maraging 250 steel with approximately 8% retained austenite, resulting in improved work hardening via martensitic transformation induced plasticity (TRIP) during deformation. Meanwhile, the higher cooling rate induced by the dwell time results in substantially refined grain structures with an increased dislocation density, leading to a simultaneously improved yield strength. Notably, the yield strength increases from 836 MPa (0 min dwell) to 990 MPa (3 min dwell), and the uniform elongation increases from 3.2% (0 min dwell) to 6.5% (3 min dwell). This intermittent deposition strategy demonstrates the potential to tune the microstructure and mechanical properties of maraging steels through engineering the thermal history during additive manufacturing. 

Laser powder-bed fusion (L-PBF) additive manufacturing presents ample opportunities to produce net-shape parts. The complex laser-powder interactions result in high cooling rates that often lead to unique microstructures and excellent mechanical properties. Refractory high-entropy alloys show great potential for high-temperature applications but are notoriously difficult to process by additive processes due to their sensitivity to cracking and defects, such as un-melted powders and keyholes. Here, we present a method based on a normalized model-based processing diagram to achieve a nearly defect-free TiZrNbTa alloy via in-situ alloying of elemental powders during L-PBF. Compared to its as-cast counterpart, the as-printed TiZrNbTa exhibits comparable mechanical properties but with enhanced elastic isotropy. This method has good potential for other refractory alloy systems based on in-situ alloying of elemental powders, thereby creating new opportunities to rapidly expand the collection of processable refractory materials via L-PBF.

 

Motivations:Recent research has emerged on generally how to improve AI products’ Human-AI Interaction (HAI) User Experience (UX), but relatively little is known about HAI-UX inclusivity. For example, what kinds of users are supported, and who are left out? What product changes would make it more inclusive?

Objectives:To help fill this gap, we present an approach to measuring what kinds of diverse users an AI product leaves out and how to act upon that knowledge. To bring actionability to the results, the approach focuses on users’ problem-solving diversity. Thus, our specific objectives were: (1) to show how the measure can reveal which participants with diverse problem-solving styles were left behind in a set of AI products; and (2) to relate participants’ problem-solving diversity to their demographic diversity, specifically gender and age.

Methods:We performed 18 experiments, discarding two that failed manipulation checks. Each experiment was a 2x2 factorial experiment with online participants, comparing two AI products: one deliberately violating one of 18 HAI guideline and the other applying the same guideline. For our first objective, we used our measure to analyze how much each AI product gained/lost HAI-UX inclusivity compared to its counterpart, where inclusivity meant supportiveness to participants with particular problem-solving styles. For our second objective, we analyzed how participants’ problem-solving styles aligned with their gender identities and ages.

Results & Implications:Participants’ diverse problem-solving styles revealed six types of inclusivity results: (1) the AI products that followed an HAI guideline were almost always more inclusive across diversity of problem-solving styles than the products that did not follow that guideline—but “who” got most of the inclusivity varied widely by guideline and by problem-solving style; (2) when an AI product had risk implications, four variables’ values varied in tandem: participants’ feelings of control, their (lack of) suspicion, their trust in the product, and their certainty while using the product; (3) the more control an AI product offered users, the more inclusive it was; (4) whether an AI product was learning from “my” data or other people’s affected how inclusive that product was; (5) participants’ problem-solving styles skewed differently by gender and age group; and (6) almost all of the results suggested actions that HAI practitioners could take to improve their products’ inclusivity further. Together, these results suggest that a key to improving the demographic inclusivity of an AI product (e.g., across a wide range of genders, ages, etc.) can often be obtained by improving the product’s support of diverse problem-solving styles.

 

This paper explores the application of sensemaking theory to support non-expert crowds in intricate data annotation tasks. We investigate the influence of procedural context and data context on the annotation quality of novice crowds, defining procedural context as completing multiple related annotation tasks on the same data point, and data context as annotating multiple data points with semantic relevance. We conducted a controlled experiment involving 140 non-expert crowd workers, who generated 1400 event annotations across various procedural and data context levels. Assessments of annotations demonstrate that high procedural context positively impacts annotation quality, although this effect diminishes with lower data context. Notably, assigning multiple related tasks to novice annotators yields comparable quality to expert annotations, without costing additional time or effort. We discuss the trade-offs associated with procedural and data contexts and draw design implications for engaging non-experts in crowdsourcing complex annotation tasks.