Hybrid additive manufacturing (hybrid-AM) has described hybrid processes and machines as well as multimaterial, multistructural, and multifunctional printing. The capabilities afforded by hybrid-AM are rewriting the design rules for materials and adding a new dimension in the design for additive manufacturing (AM) paradigm. This work primarily focuses on defining hybrid-AM in relation to hybrid manufacturing (HM) and classifying hybrid-AM processes. Hybrid-AM machines, materials, structures, and function are also discussed. Hybrid-AM processes are defined as the use of AM with one or more secondary processes or energy sources that are fully coupled and synergistically affect part quality, functionality, and/or process performance. Historically, defining HM processes centered on process improvement rather than improvements to part quality or performance; however, the primary goal for the majority of hybrid-AM processes is to improve part quality and part performance rather than improve processing. Hybrid-AM processes are typically a cyclic process chain and are distinguished from postprocessing operations that do not meet the fully coupled criterion. Secondary processes and energy sources include subtractive and transformative manufacturing technologies, such as machining, remelting, peening, rolling, and friction stir processing (FSP). As interest in hybrid-AM grows, new economic and sustainability tools are needed as well as sensing technologies that better facilitate hybrid processing. Hybrid-AM has ushered in the next evolutionary step in AM and has the potential to profoundly change the way goods are manufactured.

Rotary ultrasonic machining (RUM) is a hybrid machining approach that combines two material removal mechanisms, namely, diamond grinding and ultrasonic machining. This paper presents the results of dynamic process modeling for RUM of alumina, as currently available literature mainly focuses on static parametric relationships. A stochastic modeling and analysis technique called data dependent systems (DDS) was used to study RUM generated surface profiles and cutting force signals. Variations in the data sets of surface profiles, for the entrance and exit segments of machined holes and for that of machined rods, and cutting force signals were modeled and decomposed to gain insight into the RUM process mechanism. The DDS wavelength decomposition of the surface profiles suggested that the major characteristic root wavelength had a positive correlation with feed rate, and the wavelength magnitude may be linked to the grain size of the workpiece material. The roughness of machined surfaces increased as the tool moved deeper due to reduced flushing efficiency. Surfaces on the machined rods were less sensitive to the input variables than the hole surfaces. Moreover, spindle speed and feed rate affected the cutting force significantly.

The rapid development of modern science and technology brings with it a high demand for manufacturing quality. The surface integrity of a machined part is a critical factor which needs to be considered in the selection of the appropriate machining processes. Surface integrity is also tightly linked with tool wear. Tool wear is one of the most significant and necessary parameters to be considered for machining sustainability. By monitoring and predicting tool wear, it is possible to improve sustainability by reducing the scrap rate due to poor surface integrity. In this work, data-dependent systems (DDS), a stochastic modeling and analysis technique, was applied to study the power of spindle motor during a hard milling operation. The objective was to correlate the spindle power to tool wear conditions using DDS analysis. The spindle power was monitored, and the time series trends were decomposed to study the frequency variation with different severities of tool wear conditions and processing parameters. Analysis of variance (ANOVA) was also used to determine factors significant to the power by a spindle motor. Experiments indicate that low-level frequency of spindle power is correlated with the amount of tool wear, cutting speed, and feed per tooth. The results suggest that effective tool wear monitoring may be achieved by focusing on low-level frequencies highlighted by DDS methodology.

Foundries represent a significant part of the world’s economy and are a large consumer of energy and producer of solid waste. Sand-handling processes can use 5–10% of a foundry’s total energy. The goal of this research was to explore source reduction and waste minimization at a foundry, using both economic and Life Cycle Assessment (LCA) techniques to compare three secondary sand-reclamation options. LCA software modeled all sand processes at a mid-sized ferrous foundry in the USA. The LCA showed all secondary reclamation technologies, while more energy intensive at the foundry, lowered life cycle environmental impacts, including GHG emissions, ecotoxicity, and human health indicators, due primarily to source reduction and corresponding reduction in transportation both from the virgin sand source and to the landfill. Varying transportation distance had a large impact on LCA results to the point where the life cycle benefit of secondary reclamation became a liability in a zero distance scenario. Varying electricity generation to favor greener sources was also examined, but proved to have minimal impact on the LCA results. This research suggests that the greatest reduction of life cycle impacts in the sand-handling processes for a foundry is to find a geographically closer source for virgin sand.

Rotary Ultrasonic Machining (RUM) is a hybrid machining approach that combines two material removal mechanisms, namely diamond grinding and ultrasonic machining. Currently available literature mainly focuses on static parametric relationships. This paper presents preliminary results of an experimental study on some aspects of the RUM process performance, surface integrity, and dynamic process modeling. A stochastic modeling and analysis technique called Data Dependent Systems (DDS) was used to study RUM generated surface profiles and cutting force signals. The DDS wavelength decomposition of the surface profiles suggested that the major characteristic root wavelength had a positive correlation with feed rate, and the wavelength magnitude may be linked to the grain size of the workpiece material. In addition, the difference of the major wavelength between the surface profiles for machined holes and that for machined rods was investigated. The surface variation between the entrance and exit segments of both holes and rods were also studied. Moreover, the DDS modeling approach was applied to cutting force signals collected during RUM and comparisons were made to the surface profiles results.