International Business Machines Corporation

The Texas A&m University System

Bing Zhang

Aly Megahed

Shubhi Asthana

Alaa Elwany

Pawan Raghunath Chowdhary

Ibrahim Karaman

Raymundo Arroyave

Rohit Avinash Mujumdar

Raiyan Seede

Luke Johnson

Mohammed Saeed Abuelmakarm Shafae

A method for determining alloy processing parameters is provided. Simulated melt pool temperature and melt pool geometries can be used to create an initial printability map based on laser speed and laser power, and the printability map can include regions with potential manufacturing defects. Single-track experiments can be used to calibrate the printability map, to produce a revised printability map. Finally, contour lines representing hatch spacing can also be added to the revised printability map to produce a final printability map that can be used to configure additive manufacturing machinery.

Methods of optimizing 3-D printing parameters for metallic materials

Embodiments are provided for ticket-agent matching and agent skillset development. In some embodiments, a system includes a processor that executes computer-executable components stored in memory. The computer-executable components can include a matching component that determines, using a ticket profile and a space of agent profiles, a ticket-agent pair including a ticket identifier of a service request and an agent identifier of a particular agent within a pool of agents. The computer-executable components also can include a rematching component that assigns a second agent identifier to the service request to develop a skillset of a second particular agent within the pool of agents, the second agent identifier being associated with an unsatisfactory skill score for a defined skill to resolve the service request.

Ticket-agent matching and agent skillset development

A method, a computer program product, and a system for predicting low-frequency sensor signal predictions using a hierarchical prediction model. The method includes receiving a historical dataset of high-frequency sensor signal data and low-frequency sensor signal data. The method also includes generating a Gaussian process regression model using the historical dataset and sensor parameters and outputting high-frequency sensor signal predictions. The method also includes generating a hierarchical Gaussian process model using the historical dataset and the high-frequency sensor signal predictions and predicting low-frequency sensor signal predictions.

Sensor signal prediction at unreported time periods

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College Station, TX, United States of America

Bing Zhang