Ball milling (BM) offers a flexible process for nanomanufacturing of reactive bimetallic multiscale particulates ( nanoheaters ) for self-heated microjoining engineering materials and biomedical tooling. This paper introduces a mechanics-based process model relating the chaotic dynamics of BM with the random fractal structures of the produced particulates, emphasizing its fundamental concepts, underlying assumptions, and computation methods. To represent Apollonian globular and lamellar structures, the simulation employs warped ellipsoidal (WE) primitives of elasto-plastic strain-hardening materials, with Maxwell–Boltzmann distributions of ball kinetics and thermal transformation of hysteretic plastic, frictional, and residual stored energetics. Interparticle collisions are modeled via modified Hertzian contact impact mechanics, with local plastic deformation yielding welded microjoints and resulting in cluster assembly into particulates. The model tracks the size and diversity of such particulate populations as the process evolves via sequential collision and integration events. The simulation was shown to run in real-time computation speeds on modest hardware, and match successfully the fractal dimension and contour shape of experimental ball-milled Al–Ni particulate micrographs. Thus, the model serves as a base for the design of a feedback control system for continuous BM.

Successful fabrication of intermetallic coatings on surfaces of manufacturing interest involves regulation of the temperature/concentration dynamic distributions that develop in the molten layer during the thermal and reaction process. Modeling the spatio-temporal dynamics of this metallurgical process, however, requires partial differential equations that are cumbersome to solve on-line, as part of a real time reference model to the controller. To this end, we present a computationally parallel and meshless model (i.e., decoupled with the capability to be solved numerically in real time) to decipher the dynamics of the thermal coating process and to permit real time monitoring and control of the resulting coating microstructure. The analytical model is based on kinetic growth theories, lumped energy and mass balances, and convolution expressions of distributed temperature and concentration Green’s fields (accounting for the orientation of their gradient and decomposing heat and mass transfer across the coating from substrate conduction). The model is validated with nickel aluminide coatings processed on a robotic plasma arc laboratory station, through in-process infrared thermal sensing and off-line metallographic analysis. A Monte Carlo sample control scheme, that involves on-line parameter identification and model adaptation, is also developed using the model as an in-process observer for successful production of binary metal system coatings that exhibit the desired microstructure geometry and characteristics.

This paper presents a simple analytical model of the temperature and concentration dynamic distributions during thermal processing of intermetallic and metal-matrix composite coatings, such as nickel aluminide coatings on steel substrates, by melting, e.g., preplated aluminum/nickel layers using a moving heat source such as a plasma arc. Such a source of Gaussian power distribution scans the surface of the coating, giving rise to the temperature evolution and component dissolution during the thermal melting and reaction process. The model is based on a system of lumped energy and mass balances, and convolution expressions of distributed temperature and concentration Green’s fields (accounting for the orientation of their gradient and decomposing heat and mass transfer across the coating from substrate conduction), and is solved numerically in real-time. The simulation results are validated on Ni–Al coatings processed using a robotic plasma arc laboratory station, through in-process infrared thermal sensing and off-line metallographic analysis. It is shown that the predicted temperature and dissolution penetration values compare well with the experimentally obtained results, therefore supporting the model as a real-time basis for design and/or adaptation of a thermal control system for the coating process.