Paul Witherell

• PhD
• National Institute of Standards and Technology

Recent advances in Additive Manufacturing (AM), particularly in production scenarios, have been largely driven by insights achieved through data analytics. AM has greatly benefited from the increasingly large amounts of data generated during the design to product transformation. Despite the large amounts of data that can be generated from each buil...

Additive Manufacturing (AM) is gaining popularity in the industry for its cost-effectiveness and time-saving benefits. However, AM encounters challenges that need to be addressed to enhance its efficiency. While Machine Learning (ML) can tackle various AM challenges, it is often limited to specific issues, necessitating multiple models. In contrast...

This paper investigates a novel approach to efficiently construct and improve surrogate models in problems with high-dimensional input and output. In this approach, the principal components and corresponding features of the high-dimensional output are first identified. For each feature, the active subspace technique is used to identify a correspond...

Metal additive manufacturing machines are complex and inherently digital and often cyber-physical systems. As the adoption of this manufacturing technology increases and it becomes increasingly industrialized, concerns about security are evermore prevalent. Both cyber and non-cyber related attacks on critical infrastructure such as additive manufac...

In this paper, we briefly discuss the evolving concepts of the digital economy, digital threads, and product realization (manufacturing) in the digital economy-enabled futuristic world. We posit that if we need to become self-sufficient, it is imperative that small and medium manufacturing enterprises need to be equitably integrated into the digita...

This special section contains a selection of 14 manuscripts (2 Technical Briefs and 12 Research Papers) from the 43nd American Society of Mechanical Engineers (ASME) Computers and Information in Engineering (CIE) Conference that was held in Boston, Massachusetts, August 20-23, 2023, in conjunction with the International Design Engineering Technical...

Laser powder bed fusion (LPBF) is a popular additive manufacturing process with many advantages compared with traditional (subtractive) manufacturing. However, ensuring the quality of LPBF parts remains a challenge in the manufacturing industry. This work proposes the use of unsupervised learning, specifically, the k-means clustering method, to ide...

Laser Powder Bed Fusion (LPBF) additive manufacturing has revolutionized industries with its capability to create intricate and customized components. The LPBF process uses moving heat sources to melt and solidify metal powders. The fast melting and cooling leads to residual stress, which critically affects the part quality. Currently, the computat...

This article summarizes the outcomes from a NIST-sponsored workshop that examined the additive manufacturing (AM) data management pain points experienced by small- and medium-sized enterprises in interacting with larger organizations and government procurement agencies. Three prominent themes emerged: cost of compliance, technology gaps associated...

Metal powder-bed-fusion additive manufacturing (AM) processes have gained widespread adoption for the ability to produce complex geometries with high performance. However, a multitude of factors still affect the build process, which significantly impacts the adoption rate. This, in turn, leads to great challenges in achieving consistent and reliabl...

Metal, powder-bed-fusion-based, additive manufacturing (AM) processes have gained widespread adoption for the ability to produce complex geometries with high performance. However, a multitude of factors still affect the build process, which significantly impacts the adoption rate. This, in turn, leads to great challenges in achieving consistent and...

Laser powder bed fusion (LPBF) has shown enormous potential for metal additive manufacturing in recent years. However, the relationship between the LPBF process parameters and part quality is not yet fully understood. Some LPBF machines now use cameras to monitor melt pools during manufacturing. Machine learning techniques have been proposed to ana...

As a multi-staged digital manufacturing process, Additive manufacturing (AM) inherently benefits from data analytics (DA) decision-making opportunities. The abundance of data associated with the various observations and measurements taken throughout the design-to-product transformation creates ample opportunities for iterative, process improvements...

Traditionally, inspection and geometric dimensioning and tolerancing (GD&T) are deployed at the macroscale, where complete parts are tested to meet geometric and functional requirements. The additive manufacturing (AM) process is unique in that it is a digital process where the fabrication of the part at the macroscale is the result of a series of...

In this paper we outline the development of a scalable PBF thermal history simulation built on CAPL and based on melt pool physics and dynamics. The new approach inherits linear scalability from CAPL and has three novel ingredients. Firstly, to simulate the laser scanning on a solid surface, we discretize the entire simulation domain instead of onl...

Volume 24A provides a comprehensive review of additive manufacturing (AM) design fundamentals and applications. The primary focus of the Volume is on metallic systems with limited emphasis on polymers and ceramics where applicable. The first five divisions provide an in-depth review of each of the key aspects of the entire AM value chain. The mater...

Volume 24A provides a comprehensive review of additive manufacturing (AM) design fundamentals and applications. The primary focus of the Volume is on metallic systems with limited emphasis on polymers and ceramics where applicable. The first five divisions provide an in-depth review of each of the key aspects of the entire AM value chain. The mater...

This special section contains a selection of six papers from the 42nd American Society of Mechanical Engineers (ASME) Computers and Information in Engineering (CIE) Conference that was held in St. Louis, Missouri August 14-17, 2022, in conjunction with the International Design Engineering Technical Conferences (IDETC). Nominated by the four technic...

Metal Additive Manufacturing (MAM) produces complex, part geometries from a variety of materials in powder and wire form. Due to complexities of MAM processes that create those geometries, especially powder bed fusion, quality assurance, and qualification remain an ongoing challenge. Quality assurance involves assessing the quality of a part’s geom...

Laser Powder Bed Fusion (LPBF) is one of the most promising forms of Additive Manufacturing (AM), allowing easily customized metal manufactured parts. Industry use is currently limited due to the often unknown and unreliable part quality, which is largely caused by the complex relationships between process parameters that include laser power, laser...

Powder bed fusion (PBF) is an additive manufacturing (AM) technology that uses powerful beams to fuse powder material into layers of scanned patterns, thus producing parts with great geometric complexity. For PBF, process parameters, environmental control, and machining functions play critical roles in maintaining fabrication consistency and reduci...

Powder bed fusion (PBF) is an additive manufacturing (AM) technology that uses powerful beams to fuse powder material into layers of scanned patterns, thus producing parts with great geometric complexity. For PBF, process parameters, environmental control, and machining functions play critical roles in maintaining fabrication consistency and reduci...

Metal additive manufacturing (MAM) offers a larger design space with greater manufacturability than traditional manufacturing has offered. Despite continued advances, MAM processes still face huge uncertainty, resulting in variable part quality. Real-time sensing for MAM processing helps quantify uncertainty by detecting build failure and process a...

Additive manufacturing (AM) is rapidly transitioning to an accepted production technology. This transition has led to increasing demands on data analysis and software tools. Advances in data acquisition and analysis are being propelled by an increase in new types of in-situ sensors and ex-situ measurement devices. Measurements taken with these sens...

Additive manufacturing (AM) provides a higher level of flexibility to build customized products with complex geometries, by selectively melting and solidifying metal powders. However, wide applications of AM beyond rapid prototyping are currently limited by its ability to perform quality assurance and control. Advanced melt-pool monitoring provides...

This special issue contains a selection of papers from the 41st American Society of Mechanical Engineers (ASME) Computers and Information in Engineering (CIE) Conference that was held virtually, August 17-19, 2021, in conjunction with the International Design Engineering Technical Conferences (IDETC).

Additive manufacturing (AM) is poised to bring a revolution due to its unique production paradigm. It offers the prospect of mass customization, flexible production, on-demand and decentralized manufacturing. However, a number of challenges stem from not only the complexity of manufacturing systems but the demand for increasingly complex and high-q...

This work presents a data-driven methodology for multi-objective optimization under uncertainty of process parameters in the fused filament fabrication (FFF) process. The proposed approach optimizes the process parameters with the objectives of minimizing the geometric inaccuracy and maximizing the filament bond quality of the manufactured part. Fi...

Additive manufacturing (AM) provides design flexibility and allows rapid fabrications of parts with complex geometries. The presence of internal defects, however, can lead to deficit performance of the fabricated part. X-ray Computed Tomography (XCT) is a non-destructive inspection technique often used for AM parts. Although defects within AM speci...

Artificial Intelligence (AI) has had a strong presence in engineering design for decades, and while theory, methods, and tools for engineering design have advanced significantly during this time, many grand challenges remain. Modern advancements in AI, including new strategies for capturing, storing, and analyzing data, have the potential to revolu...

Additive manufacturing (AM) is a layer-by-layer material deposition process that allows for more manufacturing flexibility and design complexity than traditional manufacturing processes. However, the print quality in metal AM is hard to be predicted and controlled due to its high process variability. Numerous process parameters are correlated/inter...

Many additive manufacturing (AM) processes are driven by a moving heat source. Thermal field evolution during the manufacturing process plays an important role in determining both geometric and mechanical properties of the fabricated parts. Thermal simulation of AM processes is challenging due to the geometric complexity of the manufacturing proces...

Metal additive manufacturing (MAM) provides a larger design space with accompanying manufacturability than traditional manufacturing. Recently, much research has focused on simulating the MAM process with regards to part geometry, porosity, and microstructure properties. Despite continued advances, MAM processes have many variables that are not wel...

Laser powder-bed fusion is an additive manufacturing (AM) process that offers exciting advantages for the fabrication of metallic parts compared to traditional techniques, such as the ability to create complex geometries with less material waste. However, the intricacy of the additive process and extreme cyclic heating and cooling leads to material...

Additive manufacturing (AM) provides design flexibility and allows rapid fabrications of parts with complex geometries. The presence of internal defects, however, can lead to deficit performance of the fabricated part. X-ray Computed Tomography (XCT) is a non-destructive inspection technique often used for AM parts. Although defects within AM speci...

Powder bed fusion (PBF) has become a widely used additive manufacturing technology to produce metallic parts. In PBF, thermal field evolution during the manufacturing process plays an important role in determining both geometric and mechanical properties of the fabricated parts. Thermal simulation of the PBF process is computationally challenging d...

Tremendous efforts have been made to use computational models of, and simulation models of, Additive Manufacturing (AM) processes. The goals of these efforts are to better understand process complexities and to realize better, high-quality parts. However, understanding whether any model is a correct representation for a given scenario is a difficul...

Additive Manufacturing (AM) is becoming data-intensive. The ability to identify Data Analytics (DA) opportunities for effective use of AM data becomes a critical factor in the success of AM. To successfully identify high-potential DA opportunities in AM requires a set of distinctive interdisciplinary knowledge. This paper proposes a methodology tha...

This article reports on outcomes from an additive manufacturing workshop that developed guidelines for AM data management to realize the promise of Materials 4.0 and achieve process qualification for AM parts.

Additive manufacturing (AM) enables the creation of complex geometries that are difficult to realize using conventional manufacturing techniques. Advanced sensing is increasingly being used to improve AM processes, and installing different sensors onto AM systems has yielded more data-rich environments. Transforming data into useful information and...

Multi-scale, multi-physics, computational models are a promising tool to provide detailed insights to understand the process-structure-property-performance relationships in additive manufacturing (AM) processes. To take advantage of the strengths of both physics-based and data-driven models, we propose a novel, hybrid modeling framework for laser p...

Segmentation of additive manufacturing (AM) defects in X-ray Computed Tomography (XCT) images is challenging, due to the poor contrast, small sizes and variation in appearance of defects. Automatic segmentation can, however, provide quality control for additive manufacturing. Over recent years, three-dimensional convolutional neural networks (3D CN...

With the trends of Industry 4.0 spanning physical and virtual worlds, Additive Manufacturing (AM) has been the mainstream for realizing complex geometries designed in computers. Meanwhile, a considerable number of AM studies have focused on effectively building these elaborate designs. However, as the AM technologies have matured, production-driven...

Additive manufacturing’s (AM’s) transition to an accepted production technology has led to increasing demands on data requirements. Many of these advances have been made possible by an increase in in-situ sensing and ex-situ measurement devices. These new devices are rapidly increasing the volume, variety, and value of AM data. The number of softwa...

Quality is a key determinant in deploying new processes, products or services, and influences the adoption of emerging manufacturing technologies. The advent of additive manufacturing (AM) as a manufacturing process has the potential to revolutionize a host of enterprise-related functions from production to supply chain. The unprecedented level of...

Additive Manufacturing (AM) is becoming data-intensive while increasingly generating newly available data. The availability of AM data provides Design for AM (DfAM) with a newfound opportunity to construct AM design rules with improved understanding of AM’s influence on part qualities. To seize the opportunity, this paper proposes a novel approach...

The use of Industrial Internet-of-Things (IIoT) and related technology is propelling manufacturing into a new era in the fourth industry revolution characterized by ubiquitous connectivity. IIoT allows new and unprecedented interactions amongst hardware, software, and humans. Adopting the capability of IIoT with artificial intelligence tools, manuf...

The issue of Additive Manufacturing (AM) energy consumption attracts attention in both industry and academia, as the increasing trend of AM technologies being employed in the manufacturing industry. It is crucial to analyze, understand, and manage the energy consumption of AM for better efficiency and sustainability. The energy consumption of AM sy...

This paper develops a computational framework to optimize the process parameters such that the bond quality between extruded polymer filaments is maximized in fused filament fabrication (FFF). A transient heat transfer analysis providing an estimate of the temperature profile of the filaments is coupled with a sintering neck growth model to assess...

Powder bed fusion (PBF) has become a widely used additive manufacturing (AM) technology to produce metallic parts. Since the PBF process is driven by a moving heat source, consistency in part production, particularly when varying geometries, has proven difficult. Thermal field evolution during the manufacturing process determines both geometric and...

One of the most prevalent additive manufacturing processes, the powder bed fusion process, is driven by a moving heat source that melts metals to build a part. This moving heat source, and the subsequent formation and moving of a melt pool, plays an important role in determining both the geometric and mechanical properties of the printed components...

Multi-scale, multi-physics, computational models are a promising tool to provide detailed insights to understand the process-structure-property-performance relationships in additive manufacturing (AM) processes. To take advantage of the strengths of both physics-based and data-driven models, we propose a novel, hybrid modeling framework for laser p...

Computational modeling for additive manufacturing has proven to be a powerful tool to understand physical mechanisms, predict fabrication quality, and guide design and optimization. Varieties of models have been developed with different assumptions and purposes, and these models are sometimes difficult to choose from, especially for end-users, due...

This paper presents a systematic methodology to enable environmental sustainability and productivity performance assessment for integrated process and operation plans at the machine cell level of a manufacturing system. This approach determines optimal process and operation plans from a range of possible alternatives that satisfy the objectives and...

Segmentation of additive manufacturing (AM) defects in X-ray Computed Tomography (XCT) images is challenging, due to the poor contrast, small sizes and variation in appearance of defects. Automatic segmentation can, however, provide quality control for additive manufacturing. Over recent years, three-dimensional convolutional neural networks (3D CN...

The use of lattice structures produced using additive manufacturing (AM) is of great interest to the aerospace and medical industries because of their potential for strength/weight optimization. However, their use is often limited due to challenges in qualification. Recent standards proposed for the definition and verification of lattice structures...

Tremendous effort has been dedicated to computational models and simulations of Additive Manufacturing (AM) processes to better understand process complexities and better realize high-quality parts. However, understanding whether a model is an acceptable representation for a given scenario is a difficult proposition. With metals, the laser powder b...

Powder bed fusion (PBF) is a widely used additive manufacturing (AM) technology to produce metallic parts. Understanding the relationships between process parameter settings and the quality of finished parts remains a critical research question. Developing this understating involves an intermediate step: Process parameters, such as laser power and...

Variability in product quality continues to pose a major barrier to the widespread application of additive manufacturing (AM) processes in production environment. Towards addressing this barrier, monitoring AM processes and measuring AM materials and parts has become increasingly commonplace, and increasingly precise, making a new wave of AM-relate...

Design for additive manufacturing (DFAM) provides design freedom for creating complex geometries and guides designers to ensure the manufacturability of parts fabricated using additive manufacturing (AM) processes. However, there is a lack of formalized DFAM knowledge that provides information on how to design parts and how to plan AM processes for...

This paper presents a comprehensive review on the sources of model inaccuracy and parameter uncertainty in metal laser powder bed fusion process (L-PBF). Metal additive manufacturing (AM) involves multiple physical phenomena and parameters that potentially affect the quality of the final part. To capture the dynamics and complexity of heat and phas...

Additive manufacturing (AM) has enabled control over heterogeneous materials and structures in ways that were not previously possible, including functionally graded materials and structures. This paper presents a novel method for representing and communicating heterogeneous materials and structures that include tolerancing of geometry and material...

Various sources of uncertainty that can potentially cause variability in the product quality exist at different stages of the laser powder bed fusion (L-PBF) process. To implement computational models and simulations for quality control and process optimization, quantitative representation of their predictive accuracy is required. In this study, a...

Design for additive manufacturing (DFAM) provides design freedom for creating complex geometries and guides designers to ensure manufacturability of parts fabricated using additive manufacturing (AM) processes. However, there is a lack of formalized DFAM knowledge that provides information on how to design parts and how to plan AM processes for ach...

Metamodeling has been widely used in engineering for simplifying predictions of behavior in complex systems. The kriging method (Gaussian Process Regression) could be considered as a metamodeling technique that uses spatial correlations of sampling points to predict outcomes in complex and random processes. However, for large and nonideal data sets...

Additive manufacturing (AM) has enabled the production of complex geometries such as conformal lattices, topology optimized shapes, and organic structures. These complex geometric shapes must sometimes meet functional requirements, including (1) following specific curves or surfaces and (2) being bounded by specific surfaces. Mechanisms such as The...

Purpose As the technology matures, design rules for additive manufacturing (AM) can help ensure manufacturability, which can be viewed as compatibility between designs and the fabrication processes that produce those designs. Though often informal, current rules frequently provide direct guidelines or constraints for designing AM-destined parts. H...

Recent studies have shown advantages to utilizing metamodeling techniques to mimic, analyze, and optimize system input-output relationships in Additive Manufacturing (AM). This paper addresses a key challenge in applying such metamodeling methods, namely the selection of the most appropriate metamodel. This challenge is addressed with domain-specif...

As additive manufacturing (AM) continues to mature as a production technology, the limiting factors that have hindered its adoption in the past still exist, for example, process repeatability and material availability issues. Overcoming many of these production hurdles requires a further understanding of geometry-process-structure-property relation...

Additive manufacturing (AM) is gaining popularity in industrial applications including new product development, functional parts, and tooling. However, due to the differences in AM technologies, processes, and process implementations, functional and geometrical characteristics of manufactured parts can vary dramatically. Planning, especially select...

This paper develops a two-stage grey-box modeling approach that combines manufacturing knowledge-based (white-box) models with statistical (black-box) metamodels to improve model reusability and predictability. A white-box model can use various types of existing knowledge such as physical theory, high fidelity simulation or empirical data to build...

Additive manufacturing (AM) has enabled control over heterogeneous materials in ways that were not previously possible. This paper presents a novel method for representing and communicating heterogeneous materials based structures that include tolerancing of geometry and material together. AM has expanded design possibilities to include specified m...

Additive Manufacturing (AM) has intrigued the minds of many. The artist can create truly unique designs. The production engineer has a completely new way of making parts. The warfighter can repair or replace equipment on the battlefield. Moreover, any curious person can build trinket and toys at home. As such, the systems challenges facing AM users...

Additive manufacturing (AM) has been envisioned by many as a driving factor of the next industrial revolution. Potential benefits of AM adoption include the production of low-volume, customized, complicated parts/products, supply chain efficiencies, shortened time-to-market, and environmental sustainability. Work remains, however, for AM to reach t...

Purpose Additive manufacturing (AM) processes are the integration of many different science and engineering-related disciplines, such as material metrology, design, process planning, in-situ and off-line measurements and controls. Major integration challenges arise because of the increasing complexity of AM systems and a lack of support among vendo...

Purpose – As the technology matures, design rules for additive manufacturing (AM) can help ensure manufacturability, which can be viewed as compatibility between designs and the fabrication processes that produce those designs. Though often informal, current rules frequently provide direct guidelines or constraints for designing AM-destined parts....

As Additive Manufacturing (AM) is viewed more and more as a production-capable technology, data and information needs have made the costs of AM complexity increasingly apparent. Techniques available in current GD&T practices do not fully support product definitions needs in additive manufacturing. The fully model-driven process introduces new intri...

As Additive Manufacturing (AM) matures as a technology, modeling methods have become increasingly sought after as a means for improving process planning, monitoring and control. For many, modeling offers the potential to complement, and in some cases perhaps ultimately supplant, tedious part qualification processes. Models are tailored for specific...

As Additive Manufacturing (AM) matures as a technology, mod-eling methods have become increasingly sought after as a means for improving process planning, monitoring and control. For many, modeling offers the potential to complement, and in some cases perhaps ultimately supplant, tedious part qualification processes. Models are tailored for specifi...

Additive manufacturing (AM) is a promising technology that is expected to revolutionize industry by allowing the production of almost any shape directly from a 3D model. In metal-based AM, numerous process parameters are highly interconnected, and their interconnections are not yet understood. Understanding this interconnectivity is the first step...

Software tools, knowledge of materials and processes, and data provide three pillars on which Additive Manufacturing (AM) lifecycles and value chains can be supported. These pillars leverage efforts dedicated to the development of AM databases, high-fidelity models, and design and planning support tools. However, as of today, it remains a challenge...

1*2 Additive manufacturing (AM) is a promising technology that is expected to revolutionize industry by allowing the production of almost any shape directly from a 3D model. In metal-based AM, numerous process parameters are highly interconnected, and their interconnections are not yet understood. Understanding this interconnectivity is the first s...

As additive manufacturing (AM) matures, models are beginning to take a more prominent stage in design and process planning. A limitation frequently encountered in AM models is a lack of indication about their precision and accuracy. Often overlooked, model uncertainty is required for validation of AM models, qualification of AM-produced parts, and...

A limitation frequently encountered in additive manufacturing (AM) models is a lack of indication about their precision and accuracy. Often overlooked, information on model uncertainty is required for validation of AM models, qualification of AM-produced parts, and uncertainty management. This paper presents a discussion on the origin and propagati...

Additive manufacturing (AM) has increasingly gained attention in the last decade as a versatile manufacturing process for customized products. AM processes can create complex, freeform shapes while also introducing features, such as internal cavities and lattices. These complex geometries are either not feasible or very costly with traditional manu...

Large amounts of data are generated, exchanged, and used during an additive manufacturing (AM) build. While the AM data from a single build is essential for establishing part traceability, when methodically collected, the full processing history of thousands of components can be mined to advance our understanding of AM processes. Hence, this full b...

Additive manufacturing (AM) has gained increased attention in the last decade as a versatile manufacturing process for customized products. AM processes can create complex free-form shapes, introducing features such as internal cavities and lattices. These complex geometries are either not feasible or very costly with traditional manufacturing proc...

Additive Manufacturing (AM) processes intertwine aspects of many different engineering-related disciplines, such as material metrology, design, in-situ and off-line measurements, and controls. Due to the increasing complexity of AM systems and processes, data cannot be shared among heterogeneous systems because of a lack of a common vocabulary and...

This paper introduced the notion of tolerance transfer in process planning. A comparison of tolerance transfer for a part produced using traditional and additive manufacturing was presented. The comparison highlighted several challenges related to tolerance transfer in AM processes. When performing tolerance transfer, a process planner needs to con...

In this paper, we advocate for a more harmonized approach to model development for additive manufacturing (AM) processes, through classification and metamodeling that will support AM process model composability, reusability, and integration. We review several types of AM process models and use the direct metal powder bed fusion AM process to provid...