This proposal is aimed developing models and providing analysis to aid Ricoh in the development of spare parts supply chain networks with additive manufacturing (AM) capability. The goal is to design and exercise a decision-support model that helps Ricoh evaluate the answers to the following questions: When should I consider adding AM capability into my spare parts logistics network? If I should use AM, where do I locate AM capability in the network? Do the statistical characteristics of my spare parts demand impact the design of my supply network? How can I quantitatively measure the efficiency, resilience and security of the network? After establishing it, how do I best resource, manage, and employ the AM capability? Due to the fast-changing environment and capabilities of AM equipment, the model must be flexible enough to adjust to new evolutions of AM technology as new materials, processes, and capabilities continue to emerge over time. The model will provide optimization of supply chain configurations given the production capabilities, both traditional and AM, available to the supply chain under forecast demand scenarios.

The purpose of this effort is to design, initiate, and manage a serious of workforce development, training, and AM skills workshops to respond to the growing demand facing both industrial, government, and military organizations seeking to build organic additive manufacturing capability.

Legacy advanced manufacturing systems utilize digital communication between devices such as 3D printers, CAD servers, and CNC machines. The communication channels may be used to send commands or design specifications (e.g. CAD Files) between devices as part of the manufacturing process. These channels are typically not secured in legacy systems, leaving them vulnerable to attacks that could result in compromised machines, loss/theft of IP, or incorrectly manufactured parts. In addition, legacy systems are often outdated and unpatched due to prohibitive operational cost, financial cost, or lack of vendor support, so the most secure option is to leave this systems disconnected (i.e. ����������������air-gapped���������������) from untrusted networks. While this strategy may be the most feasible or considered best practice with existing technology, it provides insufficient security and significant operational burden. This project will develop a low cost solution that will increase the cybersecurity for small manufacturing companies.

Residual stress exists in material that is produced by nearly every mechanical, chemical, and thermal process, and can be defined as the internal stress distribution locked into a material when it has reached equilibrium with its environment with no interference of external load or force. Residual stress can arise from a variety of sources. In many cases these stresses are unwanted because they can cause an unexpected failure of the part and seriously shorten its lifetime, especially tensile stress which favors propagation of cracks from the surface. In this project we will develop optimization models for scanning strategies (raster or/and spot melt) in powder bed additive manufacturing process to minimize thermogradients, and therefore residual stresses.

This STIR grant will advance the current understanding of supply chain performance prediction, capacity planning, and resiliency analysis. It will provide military logistics planners with capabilities that are currently lacking in prevalent logistics planning tools. Research products will be designed to leverage data from the Army's new Enterprise Resource Planning (ERP) system to pursue end-to-end analysis and optimization of military supply chains, incorporating network modeling, queuing theory, and simulation to enable planners to evaluate logistics plans in near-real time. The analysis will focus on expeditionary operations as part of contingency scenarios. The project will construct a network-based model that captures routing alternatives and characterize the solutions to conduct capacity planning and resiliency analysis in near-real time.

This represents membership in the Center for Additive Manufacturing Logistics (CAMAL). CAMAL Serves the industry through fundamental and applied research in the technologies of the industry and an active program of technology transfer. The Center has developed core research, non-core research, and technology transfer activities.

This represents membership in the Center for Additive Manufacturing and Logistics (CAMAL). CAMAL serves the industry through fundamental and applied research in the technologies of the industry and an active program of technology transfer. The Center has developed core research, non-core research, and technology transfer activities.

Residual stress exists in material that is produced by nearly every mechanical, chemical, and thermal process, and can be defined as the internal stress distribution locked into a material. Residual stress can arise from many sources and can be unwanted because their presence can cause an unexpected failure of the part and seriously shorten its lifetime. Moreover, residual stresses can result in deformations from the intended shape. Metal based AM processes are known to introduce large amounts of residual stress, due to the large thermal gradients which are inherently present in the processes. In this research we seek the development of models that can lead to lower residual stresses as well as determine appropriate methods to evaluate impacts.

Additive manufacturing (AM) offers a potentially game changing approach for manufacturing products. Together, industry and academia are working towards identifying the opportunities and tackling the associated challenges of employing AM technology for final-use part production. Notable AM operational challenges include: (i) integrating the technology into the industry; (ii) optimizing the productivity within the build chamber; and (iii) utilizing resources more effectively (e.g. existing manufacturing capacity, additive manufacturing capacity, labor). The research proposed aims to contribute clarity towards the third operational challenge listed, utilizing the collection of resources most effectively within a production environment consisting of both AM and traditional manufacturing (TM) technologies. The primary aim of the research focuses on scheduling and dispatch policies for an AM-TM resourced facility through cost modelling analysis. A secondary aim of research investigates the associated inventory policies for all types of the associated system entity flow (i.e. raw materials, work-in-progress, and finished goods).

This represents membership in the Center for Additive Manufacturing and Logistics (CAMAL). CAMAL serves the industry through fundamental and applied research in the technologies of the industry and an active program of technology transfer. The Center has developed core research, non-core research, and technology transfer activities.