Manufacturing Process Capability Research Articles

Design and Mechanical Validation of Commercially Viable, Personalized Passive Prosthetic Feet

Abstract Current high-performance prosthetic feet work well for many users, but the low resolution of size and stiffness categories may limit walking performance for certain users. A line of prosthetic feet with a high resolution of sizes and stiffnesses, designed through amputee-specific personalization, could provide clinical and economic value. The lower leg trajectory error (LLTE) design framework facilitates the design of high-performance, amputee-specific prosthetic feet; however, previous foot prototypes were not designed to satisfy the economic, mechanical, and aesthetic requirements for commercial adoption. The aims of this work were to understand how a personalized, affordable prosthetic foot can align with the clinical-commercial ecosystem, innovate a viable future product, and inform other prosthesis designers of considerations required to connect innovation to real-world implementation. We evaluated needs by identifying how products, capital, and services flow between stakeholders, and we elucidated design requirements for a personalized prosthetic foot that can be manufactured, distributed, and clinically provided. Based on material properties and manufacturing process capabilities, computer numerically controlled (CNC) machining of Nylon 6/6 satisfies these requirements. We present a novel parametric foot architecture that can be CNC machined, fits within a commercial foot shell, and can be designed for individual users’ body characteristics and activity levels. Prototypes made using the new foot design behaved as anticipated (1–12% error in modeled displacement), satisfied industry-standard strength (ISO 10328) and mechanical performance (AOPA dynamic heel/keel) requirements, and elicited positive feedback from both amputees and prosthetists.

Critical condition for initiation of dynamic recrystallisation in electron beam powder bed fused Ti-6Al-4 V Alloy

Despite the unique capabilities of Additive Manufacturing (AM) processes for producing Ti components with complex geometries, the desired properties are only achievable if a holistic scheme is devised, considering the synergistic role of post-processing steps. This work aimed to enhance the applicability of metal AM products by improving the process chain in the manufacturing industry. For this purpose, the current work demonstrates a comprehensive investigation into the hot deformation of Ti-6Al-4 V (Ti64) pre-forms produced via the Electron Beam Powder Bed Fusion (EB-PBF) process focusing on the determination of critical conditions for initiating Dynamic Recrystallization (DRX) in these components. Following a sequential evaluation procedure, hot deformation experiments were carried out at temperatures ranging from 1000 to 1200 °C and strain rates of 0.001–1 s−1 to determine the critical stress and strain required for initiating DRX in Ti64 pre-forms and compare them to their wrought counterparts. In addition, a specific Finite Element Model (FEM) was coupled with DRX kinetics equations to predict the volume percentage of DRX grains during the hot deformation of the pre-forms. The analysis of flow stress curves showed a significant peak stress at low strains, which is then followed by a period of flow softening and eventually transitions into a nearly steady-state flow at higher strains. In addition, compared to their wrought counterparts, the EB-PBF samples exhibited a significantly superior flow softening behavior, as evidenced by a more significant volume fraction of DRX and faster recrystallisation rates. It was also revealed that the normalised strain for DRX initiation in the EB-PBF Ti64 and wrought one was 0.55 and 0.67, respectively. FEM results closely matched the experimental finding, confirming its reliability in providing valuable insights into microstructural evolution and offering a time-efficient alternative for process design and property optimisation, lowering dependence on trial-and-error approaches. Through a combination of experiments, numerical analysis, and finite element simulations, this study sheds light on the macroscopic deformation and microstructural transformations occurring during hot working processes.

Catechol-Fe(III) complexes modified PVDF membrane for hazardous pollutants separation and antifouling properties

Organic pollutants, such as toluene and xylene, in industrial wastewater negatively impact the environment. Membrane treatment is one of the best methods to reduce impurities in wastewater. Existing membranes that coat the water surface with hydrophilic material only effectively resist the initial fouling, resulting in poor oil and water selectivity. Here we report a simple and efficient method to enhance the water flux and antifouling properties of polyvinylidene fluoride (PVDF) membranes. This method involves developing and applying Catechol-Fe(III) complexes with a rough surface to the PVDF surface. Forming Catechol-Fe(III) complexes on the surface better anchors them to the membrane than the dip-coating method. The PVDF membranes with rough Catechol-Fe(III) complexes are superoleophobic, with an oil contact angle of 152 ° and high permeability, with pure water flux of 10487 Lm‐2h‐1bar‐1 and 1 wt% toluene in water emulsion flux of 4697 Lm‐2h‐1bar‐1. Overall, the straightforward manufacturing process, increased permeability, and outstanding antifouling capabilities of the PVDF membrane incorporating rough nanoparticles offer promising prospects for designing and implementing suitable membranes for oil in water emulsion separation applications.

The Current Design for Additive Manufacturing Research Frontier

Design for additive manufacturing (DFAM) seeks to develop designs that take advantage of the unique capabilities of additive manufacturing (AM) processes to maximize benefits. In this paper, several issues at the frontier of DFAM research are highlighted. First, the need is described to include as-manufactured mechanical and other physical properties, such as topology and shape optimization and generative design, during computational design. AM processes rarely produce parts with homogeneous composition and isotropic properties, so design methods and tools should not assume them. Second, the topic of designing for the AM process chain is important since AM-fabricated parts typically need postprocessing operations, such as support removal, finish machining, heat treatments, etc. DFAM methods need to incorporate the entire process chain, not just the AM process, which is explored with an example from metal powder bed fusion. Third, potentially, the largest benefit of AM can be realized by radically rethinking product architectures. Examples of an electric motorcycle and an assistive exosuit illustrate the ideas and potential benefits. Finally, some thoughts on design for 4D printing are offered, specifically for 3D printed morphing and deployable systems that utilize the shape memory effect.

Additive manufacturing of magnesium-doped calcium silicate/zirconia ceramic scaffolds with projection-based 3D printing: Sintering, mechanical and biological behavior

The lack of traditional bioactivity in zirconia (ZrO2) and limitations in the personalization capabilities of conventional manufacturing processes pose significant challenges for alveolar bone defect repair. This study aims to investigate whether ZrO2/CSi-Mgx scaffolds, produced by combining magnesium-doped calcium silicate (CSi–Mg) as a doping phase with ZrO2 as the matrix, using projection-based 3D printing (3DPP) technology, exhibit favorable biocompatibility and mechanical properties at low sintering temperatures. The effect of CSi–Mg content (x%), pore structure and heating temperature on the strength of scaffolds were investigated systematically. Incorporation of CSi–Mg could readily adjust the sintering properties of the ZrO2 scaffolds and the scaffolds with low (10–20 %) CSi–Mg possess much higher strength (74–92 MPa) after 1150 °C. Meanwhile, the ZrO2/CSi–Mg10 scaffolds with Triply periodic minimum surfaces (TPMS) pore structure had a compression strength of over 92 MPa and maintained a respectable strength (63 MPa) even after immersion in Tris buffer for 6 weeks. Concurrently, cellular experiments showed that incorporation of CSi–Mg could enhance cellular adhesion, proliferation, and migration of the ZrO2 scaffolds and also promote the osteogenic property of the scaffolds. In conclusion, the ZrO2/CSi–Mg10 scaffold with TPMS pore structure showcases remarkable mechanical performance and bioactivity, holding the potential to facilitate in-situ bone regeneration within the alveolar bone.

Measuring and Analyzing the Process Capability of Productivity – An Applied Study in the Al-Tahady Factory for the Production of Filters

This study addressed concerns related to increased percentages of damaged and re-worked production, heightened demand for factory products, and lack of awareness of the approved Sigma (σ) level during manufacturing, and associated deviations in the manufacturing process. The primary research problem was to assess the manufacturing process's stability and capability to consistently produce conical filters that meet required specifications. The study followed a sample-based approach, where twenty samples, each containing four observations, were collected continuously over a period of seven days. For each sample, the mean (X ̅) and range (R) were calculated. The mean X-Double bar of 319.32 and the average range R-bar of 0.848 were obtained through data analysis. The main findings revealed that, on average, the manufacturing process was relatively close to the target value (X-Double bar = 319.32). However, the presence of several data points outside the control limits indicated potential variability in the process. The average range (R-bar = 0.848) highlighted certain variations in the manufacturing process, which might contribute to issues like damaged or re-worked production. The study identified the need for further investigation to determine the root causes of these variations, which could include machine malfunctions, material fluctuations, or operator errors. By addressing these concerns and reducing process variability, the factory can enhance product quality, decrease waste, and improve customer satisfaction. In conclusion, continuous process monitoring and improvement initiatives, such as Six Sigma, are essential for achieving greater process capability in conical filter manufacturing. This research contributes valuable insights into process performance and provides a basis for implementing corrective actions to ensure consistent product quality and meet customer demands.

Model verification for population detection of counterfeits.

The quality of counterfeit items has increased dramatically, with modern global manufacturing being able to duplicate the materials, construction, and visual features of items. Detection of fraudulent coinage can parallel authentication of food, beverages, and manufactured goods by studying product-inherent features. Counterfeit detection is performed by comparing an Example group with a Questioned group. A model is developed for both groups using standard tests on individual pieces. Coin weight is used here as an illustration. The model should also follow the natural science of the system. In this case, the manufacturing process variation is known and steady, and the underlying distribution is known or can be determined from authentic pieces. The proposed detection method uses testing of many individual pieces, then using reverse-quality-engineering methods to identify possible sources. This strategy looks at the variation between individual pieces to determine the process capability of a machine, assembly line, or plant to create product consistency for a manufacturer. Fraudulent items may be manufactured within specification, but demonstrate a manufacturing process capability different than that of the authentic manufacturer. In this report, we examine the model previously reported and use reconstruction techniques to re-create the evidence set to validate the model, increase model accuracy, and confirm the conclusion previously reached, showing that the Questioned set is likely over 37% non-conforming by weight. In this case, the decision outcome of the analysis was improved by using additional methods not included in the modeling software package originally used.

Colored Woven Cloth‐Based Textile for Passive Radiative Heating

AbstractPersonal thermal management is attracting growing attention due to the aggravation of climate anomalies and the increasing emphasis on physical health. Passive radiative heating textiles offer a promising solution to improve thermal comfort without energy consumption; however, using woven cloth to achieve passive heating while still exhibiting an aesthetic appearance remains a problem. Here, a colored textile based on woven cloth that features superior passive radiative heating capability without sacrificing aesthetics is demonstrated. By coating infrared transparent inorganic nanoparticles on MXene‐decorated cotton, the textile achieves selectively visible reflectance for desired color, high near‐infrared absorptivity, and low mid‐infrared emissivity for passive heating management, as well as uniformly distributed nanopores for wearability. Thermal tests on simulated skin and real human bodies show considerable temperature increases compared to pure cottons. Owing to its easy manufacturing process and remarkable spectral engineering capability, this textile can be further applied to energy conservation and multi‐spectral camouflage.

GREEN CONCEPTS AND MATERIAL FLOW COST ACCOUNTING APPLICATIONS FOR MANUFACTURING COMPANY: APPROACH FOR COMPANY SUSTAINABILITY

Cosmetic items and medical supplies work together to provide community with effective management of medical concerns. Manufacturers begin the management of cosmetic products and pharmaceuticals within the parameters of the market and ethical business practises. The purpose of this study was to implement green ideas and MFCA at Al-Mansour Pharmaceuticals & Medical Supplies & Beauty in Iraq and also find out the impacts on the company's sustainability in terms of various aspects. Multiple regression models were employed via SPSS 23. The research revealed that findings led to the formulation of a variety of managerial implications for preserving the integrity of the company sustainability scale, including recommendations for improving manufacturing process capability.

Drug Shelf Life and Release Limits Estimation Based on Manufacturing Process Capability.

Specification limits are the competence regulatory agencies, whereas the release limit is a manufacturer's internal specification to be applied at the time of batch release to assure that quality attributes will remain within the specification limits until the expiry time. The aim of this work is to propose a method to set the shelf life from drug manufacture process capacity and degradation rate, using a modified version of the proposed method by Allen et al. (1991) Two different data sets were used to do this. The first data set corresponds to analytical method validation to measure the insulin concentration in order to estimate the specification limits, whereas the latter set gathered information on stability data of six batches of human insulin pharmaceutical preparation. In this context, the six batches were divided into two groups: Group 1 (batches 1, 2, and 4) was used to estimate shelf life; Group 2 (batches 3, 5, and 6) was used to test the estimated lower release limit (LRL). The ASTM E2709-12 approach was applied to verify that the future batches fulfill the release criterium. The procedure has been implemented in R-code.

Mechanical Properties of Titanium/Nano-Fluorapatite Parts Produced by Laser Powder Bed Fusion

Laser powder bed fusion (L-PBF) has attracted great interest in recent years due to its ability to produce intricate parts beyond the capabilities of traditional manufacturing processes. L-PBF processed biomedical implants are usually made of commercial pure titanium (CP-Ti) or its alloys. However, both alloys are naturally bio-inert, and thus reduce the formation of apatite as implants are put into the human body. Accordingly, in an attempt to improve the bioactivity of the materials used for making orthopedic implants, the present study decomposed fluorapatite material (FA, (Ca10(PO4)6F2)) into the form of nano-powder and mixed this powder with CP-Ti powder in two different ratios (99%Ti + 1%FA (Ti-1%FA) and 98%Ti + 2%FA (Ti-2%FA)) to form powder material for the L-PBF process. Experimental trials were conducted to establish the optimal processing conditions (i.e., laser power, scanning speed and hatching space) of the L-PBF process for the two powder mixtures and the original CP-Ti powder with no FA addition. The optimal parameters were then used to produce tensile test specimens in order to evaluate the mechanical properties of the different samples. The hardness of the various samples was also examined by micro-Vickers hardness tests. The tensile strength of the Ti-1%FA sample (850 MPa) was found to be far higher than that of the CP-Ti sample (513 MPa). Furthermore, the yield strength of the Ti-1%FA sample (785 MPa) was also much higher than that of the CP-Ti sample (472 MPa). However, the elongation of the Ti-1%FA sample (6.27 %) was significantly lower than that of the CP-Ti sample (16.17%). Finally, the hardness values of the Ti-1%FA and Ti-2%FA samples were around 63.8% and 109.4%, respectively, higher than that of the CP-Ti sample.

Automated manufacturability analysis and machining process selection using deep generative model and Siamese neural networks

Industry 4.0 calls for highly autonomous manufacturing process planning. Significant effort has been devoted over the years to generative Computer Aided Process Planning (CAPP), which aims to generate process plans for new designs without human intervention. This goal has not been realized to date due to several reasons, such as poor scalability and the difficulty in modeling manufacturing process capability, which encapsulates the part shape, quality, and material property transformation capabilities of the process. In our prior work, the shape transformation capabilities of lathe-based machining operations were modeled as latent probability distributions using a data-driven deep learning-based generative machine learning approach, from which visualizations of realistic machinable features could be sampled to assist manual process selection. In this paper, a Siamese Neural Network (SNN) is integrated with Autoencoder-based deep generative models of machining operations to enable automated comparison of the query part shapes with sampled outputs. This enables automated manufacturability analysis and machining process selection necessary for generative CAPP. The paper also demonstrates that the proposed Autoencoder and Siamese Neural Network (AE-SNN) achieves a class-average process selection accuracy of 89 %, and a manufacturability analysis accuracy of 100 %, which outperforms a discriminative model trained on the same dataset.

Multilayer films of graphene oxide and polymeric microgels: reusable adsorbents

Graphene oxide (GO) has arisen as an effective adsorbent for water treatment owing to its high removal efficiency for water pollutants. However, separating GO adsorbents from the pollutant solution is difficult after adsorption. The GO adsorbents are unsuitable for various dyes, and can only remove cationic dyes from an aqueous solution. To address these issues, this study utilized a simple and cost-effective layer-by-layer assembly technique to deposit multilayer films onto solid substrates. These films were composed of poly(allylamine hydrochloride)–dextran (PAHD) microgels and GO, and were designed to be highly effective while remaining affordable. The PAHD/GO multilayer films obtained produced an effortless separation process and demonstrated exceptional adsorption capabilities for cationic, anionic and non-ionic dyes. Specifically, the adsorption capacities for carmine and mulberry red were notably high, measuring 337.4 and 417.7 mg g−1, respectively. In addition, the PAHD/GO multilayer films could be regenerated well in sodium chloride solution without obvious compromise of removal efficiency. The adsorption kinetics, isotherms and thermodynamics of dyes on the PAHD/GO multilayer films were also studied. Thanks to the straightforward manufacturing process and outstanding adsorption capabilities of PAHD/GO multilayer films, this study presents a significant opportunity to advance the practical application of GO in water treatment.

Capability Analysis of Drift-Inherent Processes: Case of Nail Wire Drawing

Purpose: The central purpose of the study is to model the process capability of drift-inherent manufacturing processes by testing the efficacy of a novel approach that filters trend from raw process data before applying statistical process control tools. A secondary aim was to ascertain the intrinsic capability of the process following the filtering.
 Design/Methodology/Approach: Specifically, the study focused on processes in a nail-wire drawing and tested a method for analysing data from naturally-drifting processes that involves filtering trends from data before applying appropriate tools to verify the state of statistical control and capability of the process. The physical foundation for this work is based on data collected from a nail-wire drawing process A total of 250 data points were gathered over 50 days in two successive instances of 125 points, each spanning 25 days. Data were checked for normality followed by mathematical conditioning to filter out the wear trend before analysis by normal statistical process capability and control chart procedures.
 Findings: Results show that the proposed method is effective for tracking hidden effects in steadily drifting processes such as those associated with wear. After filtering, the data is found to fall within product specifications, though robust statistical control was still required through appropriate measures.
 Research Limitation: To investigate the intrinsic nature of the process outside of the process, material wear is assumed to be the sole source of the inherent drift. In processes where several sources of inherent drift are present, this may pose a problem. Additionally, the study focused on just one plant; however, data from other similar plants will be needed to buttress the findings and widen the scope of applicability of the findings.
 Practical implication: The competitive pressures of today’s marketplace are increasingly forcing companies to place premium emphasis on product quality while aiming at the lowest costs possible. The study recommends continuous and sustained efforts to reduce variation in manufacturing processes to brighten firms’ competitive survival.
 Social implication: The study will bring new knowledge to metal product manufacturers that can help them deliver high-quality products and value for money to consumers. 
 Originality / Value: New insights afforded by the study’s approach include revelations of otherwise hidden measurement errors as well as undersized finishing-die. Any other out-of-control occurrences can then be more easily tracked and identified and root-cause analysis applied to eliminate them. This is a practical study that seeks to develop an innovative way to monitor the quality of processes whose tracking is made difficult by inherent drift. The easy-to-adopt methodology can be implemented by metal product manufacturers grappling with drift-inherent processes.

X-ray Tomography Investigation of the Quality of Architected Structures Obtained with Additive Manufacturing Processes

Additive Manufacturing (AM) appears to be the best candidate to manufacture random architected materials, as it offers significant freedom in the design of hollowed parts with complex geometry. However, when these structures are needed with thins walls and struts, AM processes may encounter difficulties in properly manufacturing these structures due to their capability limits. This study proposes to characterize the manufacturing of random architected structures to see firstly their fabricability and the capability of the additive manufacturing processes used, such as vat photopolymerization (Stereolithography process (SLA)), material extrusion (Fused Filament Fabrication process (FFF)) and powder bed fusion (Selective Laser Sintering process (SLS)) through tomographic, dimensional, and mass analysis. Several defects specific to each process were identified. A higher predominance of porosities, lack of printing and excess of material manifests as trapped or partially fused powder for SLS and angel hair for FFF. These defects strongly affect the dimensional and geometric accuracy of the struts and, thus, the final mass of the structure obtained with these two processes. The SLA process makes it possible to print thinner details of random architected structures with better material quality and good dimensional and geometric accuracy, under the conditions and protocol used in this study.

A data-driven framework for learning the capability of manufacturing process sequences

Automatically acquiring knowledge of manufacturing process capabilities described in terms of the part shapes they can produce, materials they can process, and part qualities they can generate is necessary to enable on-demand cyber manufacturing. This paper aims to present a novel data-driven framework to (i) identify the sequences of processes used to manufacture a discrete part, and (ii) describe their capabilities via quantifiable descriptors of shape, material properties, and part quality. Specifically, given existing manufacturing data consisting of different parts and their corresponding manufacturing methods, the proposed framework utilizes a sequence mining algorithm to identify frequently occurring sequence patterns of different manufacturing processes. In addition, the manufacturing capability of each sequence pattern is described quantitatively in terms of the achievable shapes, material properties, and part quality metrics. Such manufacturing process capability descriptions can be queried to obtain suggestions of feasible process sequences with the capability to manufacture a new part design. An exemplar implementation of the proposed framework with a curated manufacturing dataset is given to illustrate how the framework can enable manufacturing process selection and planning. The high Confidence Rates achieved by the proposed framework show its predictive strength for use in Computer Aided Process Planning (CAPP).

Nano−Micro Characterization of Defects on Silicon Surfaces: An Industrial Perspective of Metrology Challenges

The electronic device industry is known to have been driven since its early stages by a continuous technological effort of miniaturization because of the dual need of manufacturing cost reduction through integration and offers to the user market of smaller electronic applications, more efficient, versatile, and differentiable. Silicon wafer manufacturers have been forced to provide larger size silicon wafers. 300 mm diameter is nowadays covering more than 50% of the total market, with dimension and density of residual defects moving from micrometers to nanometers, from ppt to ppq, or even less. In this context, the proper choice of measurement technique and setup which can guarantee measurement reliability to detect a given defect and provide information of its distribution inside the wafer is mandatory. It has to cope the need to control material quality in a range more frequently close to the measurement detection limit and improve the associated manufacturing process capabilities. Herein, a few examples in the area of crystallographic/morphological defects and chemical impurities on silicon wafers are proposed, reviewing the defect analysis approach inside a manufacturing environment. The discussion also involves the final product certification data reporting which tends to convey in a very synthetic format the whole measurement and control process.

The Effect of Hot Isostatic Pressing on Surface Integrity, Microstructure and Strength of Hybrid Metal Injection Moulding, and Laser-Based Powder Bed Fusion Stainless-Steel Components

Hybrid manufacture of components by combining capabilities of replication and additive manufacturing processes offer a flexible and sustainable route for producing cost-effectively small batches of metal parts. At present, there are open issues related to surface integrity and performance of such parts, especially when utilising them in safety critical applications. The research presented in this paper investigates the ductility amplification of hybrid components produced using metal injection moulding to preform and then build on them customisable sections by laser-based powder bed fusion. The properties of such hybrid components are studied and optimised through the use of non-conventional post treatment techniques. In particular, hot isostatic pressing (HIP) is employed to improve mechanical strength and to produce hybrid components that have consistent properties across batches and throughout the samples, minimising microstructural heterogeneities between fabrication processes. Thus, the investigated post-processing method can offer an extended service life of hybrid components, especially when operating under severe conditions. The optimised post treatment was found to increase the hybrid components’ strength compared to as-built ones by 68% and ~11% in yield strength (YS) and ultimate tensile strength (UTS), respectively. Subsequently, leading to a great pitting resistance, thus, making HIP samples suitable for corrosive environments. The advantages of the HIP treatments in comparison to the conventional heat treatment of hybrid components are discussed and also some potential application areas are proposed.

Mechanical behaviour and interface evaluation of hybrid MIM/PBF stainless steel components

PurposeThe paper reports an investigation into the mechanical behaviour of hybrid components produced by combining the capabilities of metal injection moulding (MIM) with the laser-based powder bed fusion (PBF) process to produce small series of hybrid components. The research investigates systematically the mechanical properties and the performance of the MIM/PBF interfaces in such hybrid components.Design/methodology/approachThe MIM process is employed to fabricate relatively lower cost preforms in higher quantities, whereas the PBF technology is deployed to build on them sections that can be personalised, customised or functionalised to meet specific technical requirements.FindingsThe results are discussed, and conclusions are made about the mechanical performance of such hybrid components produced in batches and also about the production efficiency of the investigated hybrid manufacturing (HM) route. The obtained results show that the proposed HM route can produce hybrid MIM/PBF components with consistent mechanical properties and interface performance which comply with the American Society for Testing and Materials (ASTM) standards.Originality/valueThe manufacturing of hybrid components, especially by combining the capabilities of additive manufacturing processes with cost-effective complementary technologies, is designed to be exploited by industry because they can offer flexibility and cost advantages in producing small series of customisable products. The findings of this research will contribute to further develop the state of the art in regards to the manufacturing and optimisation of hybrid components.

Automated Classification of Manufacturing Process Capability Utilizing Part Shape, Material, and Quality Attributes

Abstract The ability to classify the capabilities of different manufacturing processes based on computer-aided design (CAD) models of parts is a key missing link in cybermanufacturing. In this paper, we present a one-step approach for automatically classifying the capabilities of three discrete manufacturing processes—milling, turning, and casting—based on part shape, quality, and material property attributes. Specifically, our approach utilizes machine learning to classify manufacturing process capabilities of these processes in terms of part shape attributes such as curvature, rotational symmetry, and pairwise surface point distance (D2) histogram computed from CAD models, as well as part quality (surface finish and size tolerance) and material property attributes of parts. In this manner, historical data can be utilized to classify the capabilities of manufacturing processes. We show that it is possible to achieve high classification accuracies—88% and 83% for the training and test data sets, respectively—using this approach. In addition, a key insight gained from this work is that part shape attributes alone are inadequate for discriminating between the capabilities of the manufacturing processes considered. Specifically, the inclusion of material property and part quality attributes enables the classifier to predict viable manufacturing processes that would otherwise be ignored using shape attributes alone. Future extensions of this work will include enriching the classification process with additional attributes such as production cost, as well as alternative classification methods.