Growing interests in artificial intelligence, and high-performance and cloud computing are driving demand for data centers. At a data center, rack servers are fundamental computing engines with high complexity that have numerous mechanical, electrical, and electronic components, e.g., processors, memory bars, storage drives, and power supply units. Although energy efficiency is a key focus in data center operations, manufacturing rack server components themselves account for 40–50% of the total carbon footprint, which is often overlooked. When these components reach their end-of-life (EoL) they have differing value that affects recovery strategy/disposal route, which in turn directly influences the overall environmental impact. Thus, it is important to assess the environmental burden of rack servers, including manufacturing and circularity trade-offs for different EoL pathways. This paper presents a life cycle assessment (LCA) based approach to quantify environmental benefits of adopting a value recovery and life cycle engineering approach for rack servers as compared with a business-as-usual (BAU) EoL management. A system boundary was considered to include both the manufacturing and the EoL stage with different circularity options for the LCA modeling which involved goal and scope definition, life cycle inventory analysis for manufacturing and EoL phases, and life cycle impact assessment. Results show a 36% reduction in greenhouse gas (GHG) emissions and an improvement in eutrophication by 99.2% with a circularity approach.

Smart systems such as data-driven machine health monitoring are emerging as powerful technology for advanced manufacturing as a result of the availability of low-cost sensors, wireless communication, and advances in Machine Learning (ML) and Artificial Intelligence (AI). Predictive maintenance (PdM) has become increasingly popular in manufacturing, which can identify approaching failures, determine root causes of operation anomalies, estimate the current health state of a system, and predict the future state and time when a component will fail in the absence of an intervention. One weakness of many past studies is the lack of run-to-failure data from an actual production environment. This paper presents run-to-failure data for the air compressor of an injection molding machine. A Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) is proposed to detect bearing faults in the air compressor, which can capture the long-term dependencies without losing the capability to identify local dependencies. The model achieves a 97.4% of prediction accuracy (95.3% of overall accuracy). Experiments for machine state classification are also conducted, and the classification performance compares favorably with conventional models.

Triboelectric nanogenerators (TENGs) have gained remarkable attention in energy harvesting and smart sensing, allowing for converting mechanical energy into electrical energy. Despite great potential and progress made in this field, there remains a high demand for high-performance electrodes that are produced with sustainable, lowcost, lightweight, and durable materials such as polymers. Here, by combining the material extrusion 3D printing and the cold spray particle deposition methods, we employ a complete additive manufacturing (AM) approach to fabricate functionalized electrodes on 3D-printed parts for TENG technology. First, polylactic acid (PLA) parts were produced by material extrusion printing. Next, the cold spray process (CS) was utilized as just a one-step fabrication method of the conductive electrodes on the printed parts, eliminating the need for surface activation, over-plating, curing, and/or post-processing. Additionally, the process-structure-property relationships of the CS process were uncovered to fabricate high-performance electrodes for TENGs. The resulting electrodes demonstrate promising electrical conductivity (9.8 x 10(4) S.m(-1)), adhesive strength, stability, and microroughness (R-a = 6.32 mu m). The TENG with the fabricated electrode generates an open-circuit voltage of 174 V, which is nearly 1.85-2.9-fold higher than that of the control TENGs. It achieves the short-circuit density of approximate to 55 mA/m(2), and the power density of 1676 mW/m(2). Besides, to address the low-spatial resolution of the cold spray metallization, a manufacturing pathway is proposed, aiming to achieve higher line resolution (1 mm linewidth) electrodes for polymer electronics. This work provides a manufacturing strategy that can advance the field of TENG and polymer electronics by addressing the limitations of conventional electrode manufacturing techniques.

As a key strategy for achieving a circular economy, remanufacturing involves bringing end-of-use (EoU) products or cores back to a ‘like new’ condition, providing more affordable and sustainable alternatives to new products. Despite the potential for substantial resources and energy savings, the industry faces operational challenges. These challenges arise from uncertainties surrounding core quality and functionality, return times, process variation required to meet product specifications, and the end-of-use (EoU) product values, as well as their new life expectancy after extended use as a ‘market product’. While remanufacturing holds immense promise, its full potential can only be realized through concerted efforts towards resolving the inherent complexities and obstacles that impede its operations. Machine learning (ML) and data-driven models emerge as transformative tools to mitigate numerous challenges encountered by manufacturing industry. Recently, the integration of cutting-edge technologies, such as sensor-based product data acquisition and storage, data analytics, machine health management, artificial intelligence (AI)-driven scheduling, and human–robot collaboration (HRC), in remanufacturing procedures has received significant attention from remanufacturers and the circular economy community. These advanced computational technologies help remanufacturers to implement flexible operation scheduling, enhance quality control, and streamline workflows for EoU products. This study embarks on a comprehensive review and in-depth analysis of state-of-the-art algorithms across various facets of remanufacturing processes and operations. Additionally, it identifies key challenges to advancing remanufacturing practices through data-driven and ML methods and uncovers research opportunities in synergy with smart manufacturing techniques. The study aims to offer guidelines for stakeholders and to reinforce the industry’s pivotal role in circular economy initiatives.

Cooling and lubrication are vital during tool-based machining processes. Conventional flood cooling has been historically dominant among several cooling methods. In modern times, besides improving machining performance, the advancement and adoption of minimum quantity lubrication (MQL) technology have gained high prominence for reducing parts production cost and achieving environmental and ecological sustainability. Attempts and efforts were driven towards several major directions that include: (i) retrofitting/modifying of the existing MQL system with design changes for internal MQL delivery, (ii) design of novel MQL systems/methods for better spray, (iii) development of biodegradable base fluids for green manufacturing, (iv) inclusion of nanoadditives within MQL fluids, (v) the use of cryogenic gas for better cooling and lubrication. This chapter discusses the working principles of these advanced MQL methodologies, followed by their test results. Under the new MQL system development, an ultrasonic atomization-based cutting fluid (ACF) spray system is presented in detail with fundamental modeling, simulation, and experimental validation to explain how fluid mist droplets develop a dynamic fluid film that accesses the cutting zone and improves machining performance. Fundamental insight of this novel MQL system is transformed into meaningful scenarios of machining, such as turning and milling of a titanium alloy. Findings from this ACF spray and other novel MQL systems are summarized. Lab tests and a few industry adoptions of the abovementioned advanced designs and developments have demonstrated high potential to achieve the anticipated manufacturing outcomes. Some combinations of two or more MQL approaches (e.g., cryo-based nanoMQL, hybrid nanoadditives, etc.) have been found to further enhance overall performance. However, all the proposed advanced MQL methodologies are still under development and iteration stages, while industry adoption is limited within a few companies. This chapter concludes with key findings, limitations, and future recommendations.

Triboelectric nanogenerator (TENG) is an emerging energy harvesting device to effectively harness various mechanical energy sources. In TENG technology, polymers are of particular interest as tribo-negative materials owing to their unique properties (e.g., charge storage capability, impact resistance, flexibility, low cost, recyclability, etc.). Despite significant advances, one major challenge for TENG is to develop high-performance back electrodes that are conformably attached to the tribo-negative polymer substrates to fully exploit the potential of TENG in energy harvesting. To this end, the present study is aimed to employing direct "cold spray" particle deposition as just one-step fabrication method for high-performance electrodes on flexible polymers. In this regard, Tin (Sn) particles are directly written on the polymer (PET) surface by cold spraying to achieve conformal electrodes with high-adhesive strength and stable electrical conductivity. The resulting electrodes are thoroughly characterized in terms of microstructure, adhesion strength, and electrical performance. Arc-shaped TENG devices with both traditional aluminum (control) and cold-sprayed Sn electrodes are fabricated, followed by evaluating the TENGs' performance. Owing to the strong adhesion and micro-roughness (i.e., Ra = 4.865 & mu;m) of the Sn electrodes, electricity generation performance was found to be improved by & AP;2.4 folds as compared to the control TENG. The TENG with Sn electrode can generate an open-circuit output voltage of up to 243 V with a maximum output power of 130 mW/m2, thereby indicating the promising potential of the cold spray technique in TENG technology.

Supersonic cold spray (CS) of functional nanomaterials from atomized droplets has attracted significant attention in advanced thin-film coating as it enables particle deposition with high-adhesion strength. In CS, optimum design of the supersonic nozzle (i.e., converging-diverging nozzle) is essential for accelerating particles to desired velocities. However, research on the nozzle design for supersonically spraying of "liquid droplets" for nanocoating applications is limited. To this end, we investigate the effect of nozzle geometrical parameters, including throat diameter, exit diameter, and divergent length on droplets impact velocity by numerical modeling and experimental validation, followed by a case study on nanocoating. The discrete-phase modeling was employed to study droplets' flow behavior in continuous gas flow for various nozzle geometries. The results reveal that the nozzle expansion ratio, defined as a function of throat and exit diameters, has a significant influence on droplet velocity, followed by divergent length. Noteworthy, to correctly accelerate "low-inertia liquid microdroplets," it was found that the optimum nozzle expansion ratio for axisymmetric convergent-divergent nozzles should be in a range of 1.5-2.5, which is different and way smaller than the recommended expansion ratio (i.e., 5-9) for CS of conventional micron-scale "metal" powders. Based on the simulation results, an optimum design of supersonic nozzle is established and prototyped for the experimental studies. Particle image velocimetry (PIV) was used to experimentally investigate the spray flow field and to validate the numerical modeling results. Moreover, coating experiments using the optimized nozzle confirmed the effective supersonic spraying of droplets containing nanoparticles, thereby showing the potential for advanced nanocoating applications.

Selective surface metallization of insulating polymers is of particular interest in smart films, energy harvesting, and sensing applications. However, traditional polymer metallization techniques face challenges due to the need for environmentally hazardous pretreatment (e.g., strong acid etching) and cost-intensive palladium seeding processes, thereby limiting the large-scale deployment of metallized polymers. With the advent of rapid prototyping, metallization on additively manufactured polymers drew attention in a variety of technological applications, as it enables the fabrication of low-cost electronic devices. In the current work, we deploy and evaluate a hybrid additive metallization route that can enable the fabrication of functional selective metallization on 3D-printed polymers in a rapid and eco-friendly methodology with improved electrical conductivity. The metallization route sequentially comprises (1) material extrusion 3D printing, (2) cold spray metallization, and (3) electroless deposition. The resulting metal (copper) layers on the polymer surfaces are characterized in terms of the microstructure, surface chemistry, wettability, and electrical conductivity. Notably, selective metallization with promising electrical conductivity (i.e., 6.47 x 10(6) S m(-1) for ABS and 5.27 x 10(6) S m(-1) for PLA parts) is achieved on both linear and curvilinear polymer surfaces. Moreover, strong adhesion between the metallized layer and the 3D-printed structures was confirmed by adhesion tests. Detailed evaluation of the proposed hybrid metallization route unlocks great potential to advance the field of conductive surface metallization on 3D-printed polymers.

Supersonic cold spraying of droplets containing functional nanomaterials is of particular interest in advanced thin-film coating, that enabling high-adhesion strength particle deposition. In this method, coating occurs when the particles are accelerated to supersonic velocities in a converging-diverging nozzle, and then impact onto a target surface. Here, the optimum design of the nozzle is essential to deal with low-inertia particles like droplets. In particular, nozzle geometrical parameters (i.e., throat diameter, exit diameter, divergent length) determine droplets’ acceleration and deposition characteristics under supersonic flow conditions. To this end, we thoroughly investigate the influence of nozzle geometrical parameters on droplets acceleration by numerical modeling followed by experimental validation, and a case study on surface coating application. Two-phase flow modeling was used to predict droplets’ behavior in continuous gas flow for different nozzle configurations. The results show that the nozzle expansion ratio — a function of throat and exit diameters — has a significant influence on droplet velocity, followed by divergent length. In particular, to correctly accelerate low-inertia liquid droplets, optimum nozzle expansion ratio for an axisymmetric convergent-divergent nozzle is found to be in a range of 1.5–2.5 for various sets of parameters, which is different than the recommended expansion ratio (i.e., 5–9) for cold spraying of micro-scale metal particles. The findings can help determine the ideal design of a supersonic nozzle to minimize turbulent velocity fluctuation and shock wave formation that in turn assist to effectively spray low-inertia particles like micro-scale droplets. Based on the simulation results, an optimal design of supersonic nozzle is selected and prototyped for the experimental studies. Numerical modeling results are validated by particle image velocimetry (PIV) measurements. Moreover, coating experiments confirm the adaptability of the optimized nozzle for supersonic cold spraying of droplets containing nanoparticles, which thereby has the potential for rapid production of advanced thin films.

Supersonic spray coating of nanomaterials, owing to high impact velocity of particles, offers significant potential to improve the physical and mechanical properties of target surfaces. Rather than handling individual light nanoscale particles directly with a number of limitations, aqueous nanomaterial colloids and suspensions can be supersonically deposited onto surfaces by converting these complex liquid mixtures into the form of atomized micro-scale droplets. Dispersion and deposition characteristics of these droplets play a vital role in the quality and efficacy of the resultant nanomaterial coating. In the present study, comprising numerical modeling and experimental validation, we investigate details of the dispersion and deposition characteristics of droplets under supersonic flow conditions. In the numerical study, a two-way coupled discrete phase modeling is used to track the discrete phase (i.e., droplets) and to investigate the interaction of droplets with the continuous gas phase (i.e., high-velocity driving gas) in regard to their properties and conditions. The results through computational fluid dynamics (CFD) show that driving gas properties (i.e., temperature, pressure, gas type) and droplet size play a prominent role in droplets dispersion and deposition phenomena. In particular, smaller droplets (<= 2 mu m) are observed to be more susceptible to turbulent dispersion and evaporation. In the experimental study, an atomization-based supersonic spray system is developed for model validation and a case example of nano-coating applications. The CFD modeling results are validated by particle image velocimetry (PIV) measurements. A case study of titanium dioxide (TiO2) nanomaterial coating on a polymer substrate (ITO/PET film) is performed to demonstrate the suitability of the present spray deposition system, which also addresses the current challenges of TiO2 coating onto ITO/PET surface.

In conventional metal cutting, different tool wear modes, and their individual deterioration rates play vital roles in overall production performance. For a given tool (i.e., geometry or materials), many shop floors still follow a standard rule by pre-setting a tool life, which is conservative but not realistic. Premature failure of atool can cause unexpected machine downtime and material losses, while another tool could serve beyond that pre-set life. As a result, optimized tool life and productivity cannot be achieved. Moreover, nowadays, there is an incread demand of process monitoring and optimization on the unmanned andthe semi-automated shop floors. Tool condition monitoring (TCM) systems for process improvement and optimization have been in research for several decades. Both offline and online TCM systems are invented and discussed. A wide range of original publications are reported focusing on different sub-topics, e.g., specific machining process-based TCM methods, measurement or signal acquisition methods, processing methods, and classifiers. With the recent evolution of smart sensors in the era of Industry 4.0, development of online TCM systems received much attention to the researchers. Accordingly, research on some sub-topics also gets motivated into different directions, such as, feasibility of power or current sensors, machine vision technique, and combination of multi-sensors. Thus, from the industrial viewpoint, the current state of implementation of the propod TCM systems for (near) real-time process monitoring and control needs to be clear. This paper presents the state-of-the-art of the TCM systems covering three major machining operations, discusses their application feasibility in industry environments, and states some current TCMS implementations. Challenges being faced by the industry are concluded, along with direction and suggestions for future researches. (c) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the Scientific Committee of the NAMRI/SME.

Most 'as-received' stock materials possess local anisotropic and heterogeneous properties, as induced by rolling process, e.g., extrusion, forging, cold/hot-rolling, etc. followed by optional heat treatment process(es). During machining, such local (in-built) material properties can adversely affect the machinability. This work is per-formed based on a real industry problem in regard to unusual chip jamming, inconsistent tool life, and product quality. The aim of this work is to investigate local variability of anisotropic and heterogenous material and mechanical properties towards center of cylindrical bars, and their effect on machinability, such as, cutting force during machining of stainless steel AISI 304. Two different sources of cylindrical stock material are considered for investigation. Drilling experiments are performed in axial direction of cylindrical bars by varying tool size (6.5 mm and 10.8 mm), and cutting parameters (viz, speed and feed). It is observed that material and mechanical properties, e.g., engineering stress, strain, hardness and microstructures vary significantly within 6 mm from the side surface (outer dia). Such variation of in-built properties within any sample causes a variability in peak-to peak force (similar to 13-25%) and cutting instability as the drill cutting edge passes through different radial locations in each rotation of the drill bit. Larger cutting force and variation were observed with larger variation in material properties. Further drilling tests at the center of the bars reveal that the machining issue is caused by these two material properties. The study also suggests some solutions to encounter the material issue.

Machining is one of the major manufacturing methods having very wide applications in industries. Unlike layer-by-layer additive three-dimensional (3D) printing technology, the lack of an easy and intuitive programmability in conventional toolpath planning approach in machining leads to significantly higher manufacturing cost for direct computer numerical control (CNC)-based prototyping (i.e., subtractive 3D printing). In standard computer-aided manufacturing (CAM) packages, general use of B-rep (boundary representation) and non-uniform rational basis spline (NURBS)-based representations of the computer-aided design (CAD) interfaces make core computations of tool trajectories generation process, such as surface offsetting, difficult. In this work, the problem of efficient generation of freeform surface offsets is addressed with a novel volumetric (voxel) representation. It presents an image filter-based offsetting algorithm, which leverages the parallel computing engines on modern graphics processor unit (GPU). The compact voxel data representation and the proposed computational acceleration on GPU together are capable to process voxel offsetting at four-fold higher resolution in interactive CAM application. Additionally, in order to further accelerate the offset computation, the problem of offsetting with a large distance is decomposed into successive offsetting using smaller distances. The performance trade-offs between accuracy and computation time of the offset algorithms are thoroughly analyzed. The developed GPU implementation of the offsetting algorithm is found to be robust in computation, and demonstrates a 50-fold speedup on single graphics card (NVIDIA GTX780Ti) relative to prior best-performing algorithms developed for multicores central processing units (CPU). The proposed offsetting approach has been validated for a variety of complex parts produced on different multi-axis CNC machine tools including turning, milling, and compound turning-milling.

Digital manufacturing systems are determined to be a major key to enhance productivity and quality mainly due to real-time process monitoring and control capability with instant data processing. During machining, such systems are anticipated to excerpt reliable data within a short time-lapse, monitor tool wear progress, anticipate its wear and breakage, alert the machinist in real time to avoid unexpected failure of tool or machine, and help obtaining quality products. This is vital, especially, when drilling Ni-/Ti-based superalloys because catastrophic failure and premature breakage of tools occur in random manner due to aggressive welding and chipping of tool including the rake and/or flank faces and tool corner.Nowadays, spindle power data are easy to collect directly from modern machine tools and can be made available in production floor for such real-time data processing. This work aims to evaluate spindle power data for real-time tool wear/breakage prediction during drilling of a Ni-based superalloy, Inconel 625. Experiments were performed by varying speed and feed. Spindle power data were collected from the power meter (also called load meter) to feed into the neural network (NN) for functional processing. To understand the reliability of the spindle power data, force data were also collected and compared. The results show that the trends of these two different types of data over cutting time are similar for any feed and speed combinations. The error in NN prediction from actual wear was found to be between 0.8–18.4% with power data as compared to that between 0.4–17.9% with force data. Findings suggest that spindle power data integrated with the artificial intelligence (NN) system can be used for real-time tool wear/breakage monitoring and process control, thus appreciate digital manufacturing systems.

In this paper, both software model visualization with path simulation and associated machining product are produced based on the step ring-based three-axis path planning to demo model-driven graphics processing unit (GPU) feature in tool path planning and 3D image model classification by GPU simulation. Subtractive 3D printing (i.e., 3D machining) is represented as integration between 3D printing modeling and computer numerical control (CNC) machining via GPU simulated software. Path planning is applied through visualization of surface material removal in high-resolution and 3D path simulation via ring selective path planning based on accessibility of path through pattern selection. First, the step ring selects critical features to reconstruct computer-aided design (CAD) design model as stereolithography (STL) voxel, and then, local optimization is attained within interested ring area for time and energy saving of GPU volume generation as compared to global automatic path planning with longer latency. The reconstructed CAD model comes from an original sample (GATech buzz) with 2D image information. CAD model for optimization and validation is adopted to sustain manufacturing reproduction based on system simulation feedback. To avoid collision with the produced path from retraction path, we pick adaptive ring path generation and prediction in each planning iteration, which may also minimize material removal. Moreover, we did partition analysis and G-code optimization for large-scale model and high density volume data. Image classification and grid analysis based on adaptive 3D tree depth are proposed for multilevel set partition of the model to define no cutting zones. After that, accessibility map is computed based on accessibility space for rotational angular space of path orientation to compare step ring-based pass planning verses global path planning of all geometries. Feature analysis via central processing unit (CPU) or GPU processor for GPU map computation contributes to high-performance computing and cloud computing potential through parallel computing application of subtractive 3D printing in the future.

Product quality and productivity are important factors in manufacturing industries, especially when dealing with cumbersome materials like titanium. Cooling and lubrication effects offered by the associated metalworking fluid application system play a vital role in determining these factors, especially during finish cutting. Recently, the atomization–based cutting fluid (ACF) spray system has shown promising cooling and lubrication effects during rough turning of titanium at the macro–scale, but yet to be examined during finish cutting (e.g., depth of cut and feed rate 0.2mm or lower). This paper aims to study the effect of the ACF spray system on machining performance during finish turning of Ti-6Al-4V. In the first set of experiments, two spray parameters (viz., gas velocity and flow rate) and cutting parameters (viz., cutting speed, feed rate and depth of cut) are varied to select the most suitable condition for the application of the ACF spray system. Machining outputs are evaluated in terms of nose wear, cutting temperature, surface roughness, roundness error, chip morphology, and part hardness. A separate set of experiments is then performed to compare the performance of the ACF spray system against compressed air (dry) and flood coolant conditions. It is found that, even a lower fluid flow rate of 1.5mL/min (10vol.%) at a lower gas velocity of the spray system outperforms the other two coolant conditions, thus further enhancing the performance of environmentally-friendly manufacturing process.

Tool wear is an important limitation to machining productivity and part quality. In this paper, remaining useful life (RUL) prediction of tools is demonstrated based on the machine spindle power values using the neural network (NN) technique. End milling tests were performed on a stainless steel workpiece at different spindle speeds and spindle power was recorded. The NN curve fitting approach with different MATLAB™ training functions was applied to the root mean square power (Prms) values. Sample Prms growth curves were generated to take into account uncertainty. The Prms value in the time domain was found to be sensitive to tool wear. Results show a good agreement between the predicted and true RUL of tools. The proposed method takes into account the uncertainty in tool life and the percentage increase in nominal Prms value during the RUL prediction. Using MATLAB™ on an Intel i7 processor, the computation takes 0.5s Thus, the method is computationally inexpensive and can be incorporated for real time RUL predictions during machining.

The lack of plug-and-play programmability in conventional toolpath planning approach in subtractive manufacturing, i.e., machining leads to significantly higher manufacturing cost for CNC based prototyping. In computer aided manufacturing (CAM) packages, typical B-rep or NURBS based representations of the CAD interfaces challenge core computations of tool trajectories generation process, such as, surface offsetting to be completely automated. In this work the problem of efficient generation of free-form surface offsets is addressed with a novel volumetric representation. It presents an image filter based offsetting algorithm, which leverages the parallel computing engines on modern graphics processor unit (GPU). The scalable voxel data structure and the proposed hardware-accelerated volumetric offsetting together advance the computation and memory efficiencies well beyond the capability of past studies. Additionally, in order to further accelerate the offset computation the problem of offsetting with a large distance is decomposed into successive offsetting using smaller distances. The accuracy of the offset algorithms is thoroughly analyzed. The developed GPU implementation of the offsetting algorithm is robust in computation, easy to comprehend, and achieves a 50-fold speedup on single graphics card (NVIDIA GTX780Ti) relative to prior best-performing dual socket quad-core CPU implementation.

In this paper, both software model visualization with path simulation and associated machining product are produced based on the step ring based 3-axis path planning to demo model-driven graphics processing unit (GPU) feature in tool path planning and 3D image model classification by GPU simulation. Subtractive 3D printing (i.e., 3D machining) is represented as integration between 3D printing modeling and CNC machining via GPU simulated software. Path planning is applied through material surface removal visualization in high resolution and 3D path simulation via ring selective path planning based on accessibility of path through pattern selection. First, the step ring selects critical features to reconstruct computer aided design (CAD) design model as STL (stereolithography) voxel, and then local optimization is attained within interested ring area for time and energy saving of GPU volume generation as compared to global all automatic path planning with longer latency. The reconstructed CAD model comes from an original sample (GATech buzz) with 2D image information. CAD model for optimization and validation is adopted to sustain manufacturing reproduction based on system simulation feedback. To avoid collision with the produced path from retraction path, we pick adaptive ring path generation and prediction in each planning iteration, which may also minimize material removal. Moreover, we did partition analysis and g-code optimization for large scale model and high density volume data. Image classification and grid analysis based on adaptive 3D tree depth are proposed for multi-level set partition of the model to define no cutting zones. After that, accessibility map is computed based on accessibility space for rotational angular space of path orientation to compare step ring based pass planning verses global all path planning. Feature analysis via central processing unit (CPU) or GPU processor for GPU map computation contributes to high performance computing and cloud computing potential through parallel computing application of subtractive 3D printing in the future.