Manufacturability Research Articles

Static and Fatigue Properties of Rhenium-Alloyed Inconel 718 Produced by Powder Bed Fusion Additive Manufacturing

Inconel 718 (In718) is the most widely used nickel-based alloy in additive manufacturing due to its favorable processability. However, In718’s high-temperature performance is not suited for the most demanding applications in the aerospace industry. Therefore, in this study, Inconel 718 powder was coated with 3% wt. rhenium (In718-Re) using AM’s in situ alloying capabilities to improve high-temperature properties. The proposed alloy’s mechanical performance was evaluated, focusing on the effects of post-process heat treatment and hot isostatic pressing following the laser-based powder bed fusion of metals (PBF-LB/M) processing. Static tensile tests conducted at room temperature and elevated temperatures (650 °C and 760 °C) demonstrated that the alloy has comparable strength to pure In718 according to ASTM F3055-14a—an ultimate tensile strength of 1247 MPa, yield strength of 909 MPa, and almost 2× higher elongation of 23.8%. Fatigue tests at room temperature indicated a fatigue limit below 400 MPa for 107 cycles. Fractographic analysis revealed that fatigue performance was primarily impacted by a lack of fusion defects inherent to the PBF-LB/M process, highlighting the need for optimized powder preparation and processing parameters to minimize defect formation. While rhenium addition shows limited benefits in Inconel 718, this study underscores the potential of in situ alloying through powder surface modification as a flexible method for incorporating high-melting-point elements into nickel-based alloys for tailored alloy design in additive manufacturing.

Introduction to Design for Additive Manufacturing

Introduction to Design for Additive Manufacturing

Alloy design for additive manufacturing: Continuously reinforced Al-Ce nanocomposites

Alloy design for additive manufacturing: Continuously reinforced Al-Ce nanocomposites

Design for manufacture and assembly (DfMA) for modular buildings: an analytical framework

ABSTRACT Design for Manufacture and Assembly (DfMA) is a proactive strategy aimed at maximizing the potential of modular buildings to address issues, including labor shortages, time overruns, and low productivity. Despite its growing adoption, research on examining existing design practices remains limited. This study therefore aims to 1) develop an analytical framework and 2) uncover underlying modular building design patterns. Drawing inspiration from space syntax analysis, the framework comprises six components: nomenclature system, space identification, spatial relations, module shape, dimensions, and convex break-up. It was then applied to analyze 39 modular building units in Hong Kong. The established framework helps visualize underlying design patterns, e.g. spatial typologies, module shapes and sizes, and standardized spatial configurations. Several design patterns are also identified, i.e. typical space requirements, common module sizes, dimension-coordinated spaces, and the repetitive use of standardized spaces and modules to form units. This innovative framework, along with a discussion on evolving professional practices, offers valuable insights for advancing DfMA and modular construction. Future research is recommended to refine the framework and enhance DfMA by integrating these patterns with expert knowledge and computational intelligence.

HM-SYNC: A Multimodal Dataset of Human Interactions with Advanced Manufacturing Machinery

Abstract A deep understanding of manufacturing processes is essential for advancing manufacturing-oriented design and engineering complex systems. As advanced manufacturing technologies evolve, systems have grown more complex, and human interaction has become a vital component of both operation and design. This shift introduces new challenges, as human roles within these systems extend beyond traditional boundaries and are not yet fully understood in current design processes. Characterizing human interactions within manufacturing systems is therefore critical to supporting further advancements. Additionally, human behavior plays a significant role in many engineered systems beyond manufacturing, underscoring the value of developing methodologies to better analyze human behavior and interactions within complex environments. These methodologies can broadly support and enhance diverse aspects of engineering design. This study presents HM-SYNC, which is a comprehensive dataset of human interactions with advanced manufacturing machinery, specifically a wire-arc additive manufacturing machine. Depth images and 3D skeleton joints are collected over six months using privacy-preserving pose tracking with depth cameras. HM-SYNC includes thousands of interactions across various contexts, goals, and users, providing valuable insights into patterns of human-machine interaction. By capturing a diverse range of interactions in natural settings, this dataset supports advancements in human-centered manufacturing design and facilitates the development of more effective manufacturing systems. This dataset can enhance models and digital twins of manufacturing systems, help operators optimize machinery use and efficiency, and guide designers in refining machine and system design, to name just a few applications.

Design for Additive Manufacturing of Lattice Structures for Functional Integration of Thermal Management and Shock Absorption

Design optimization through the integration of multiple functions into a single part is a highly effective strategy to reduce costs, simplify assembly, improve performance, and reduce weight. Additive manufacturing facilitates the production of complex structures by allowing parts consolidation, resulting in optimized designs, where multiple functions are integrated into a single component. This study presents a design for additive manufacturing method for integrating multiple lattice structures to achieve thermal management and shock absorption functions. The method follows modeling and simulation phases for dimensioning and optimizing solutions to deliver the design functions at different macro- and mesoscale levels. Hierarchical complexity was leveraged to design the two-levels structure in a single part, each delivering a specific function. Specifically, the external layer addresses energy absorption and thermal insulation, while the internal layer acts as a thermal battery by incorporating a phase change material. The design of a container carried by an unmanned aerial vehicle for the transport of healthcare and biological materials is presented. The container is shock-resistant and can maintain the content at 4 ± 2 °C for at least 1 h. As it operates passively without the need for additional energy-consuming devices, it is easy to operate and contributes to increased flight autonomy. A flight mission experiment for urgent transport of blood bags confirmed the capability of the container to preserve blood integrity. This case study demonstrates that the two-layer lattice structure design represents a highly efficient approach to multifunctional design optimization.

Navigating translational research in nanomedicine: A strategic guide to formulation and manufacturing.

Over the past two decades, extensive research has focused on both the fundamental and applied aspects of nanomedicine, driven by the compelling advantages that nanoparticles offer over their bulk counterparts. Despite this intensive research effort, fewer than 100 nanomedicines have been approved by the U.S. Food and Drug Administration and the European Medicines Agency since 1989. This disparity highlights a substantial gap in translational research, reflecting the disconnect between the prolific research in nanomedicine and the limited number of products that successfully reach and sustain themselves in the market. For instance, the nanomedicine DepoCyt, which received FDA approval in 1999 for the treatment of lymphomatous meningitis, was discontinued in 2017 due to persistent manufacturing issues. To address similar translational challenges, this review aims to identify and analyse issues related to the formulation design and manufacturing of nanomedicines. It provides an overview of the most prevalent manufacturing technologies and excipients used in nanomedicine production, followed by a critical evaluation of their clinical translatability. Furthermore, the review presents strategies for the rational formulation design and optimization of nanomedicine manufacturing, adhering to the principles of quality-by-design and quality risk management.

Generalized Design for Additive Manufacturing (DfAM) Expert System Using Compliance and Design Rules

Additive manufacturing (AM) has revolutionized the design and production of complex geometries by offering unprecedented creative freedom over traditional manufacturing. Despite its growing prominence, AM lacks automated and standardized design rules tailored to specific AM processes, resulting in time-consuming and expert-dependent manual verification. To address these limitations, this research introduces a novel design for additive manufacturing (DfAM) framework consisting of two complementary models designed to automate the design process. The first model, based on a decision tree algorithm, evaluates part compliance with established AM design rules. A modified J48 classifier was implemented to enhance data mining accuracy by achieving a 91.25% classification performance accuracy. This model systematically assesses whether input part characteristics meet AM processing standards, thereby providing a robust tool for verifying design rules. The second model features an AM design rule engine developed with a Python-based graphical user interface (GUI). This engine generates specific recommendations for design adjustments based on part characteristics and machine compatibility, offering a user-friendly approach for identifying potential design issues and ensuring DfAM compliance. By linking part specifications to various AM techniques, this model supports both researchers and engineers in anticipating and mitigating design flaws. Overall, this research establishes a foundation for a comprehensive DfAM expert system.

Circular economy and sustainable manufacturing: a bibliometric analysis

Abstract This study conducts a detailed bibliometric analysis of the research landscape on circular economy and sustainable manufacturing. Using data from the Scopus database, 9,946 publications on “Circular Economy” and 212 publications on both “Circular Economy” and “Sustainable Manufacturing” were identified. The analysis with VOSviewer spanned publication trends from 2006 to July 2024. Key platforms for publication included Sustainability Switzerland, known for its multidisciplinary approach to sustainability challenges, and Lecture Notes in Mechanical Engineering and Procedia CIRP, which were prominent for their contributions to sustainable manufacturing and engineering innovations. These platforms played a pivotal role in disseminating cutting-edge research at the intersection of sustainability and engineering. The study highlighted top-cited works and leading contributors, mainly from India, Italy, and the United Kingdom. A co-occurrence analysis of author keywords identified seven thematic clusters: Sustainable Manufacturing Practices, Sustainable Economic and Environmental Practices, Green and Efficient Manufacturing Processes, Sustainable Manufacturing and Environmental Decision-Making, Industrial Ecology and Sustainable Development, Advanced Sustainable Manufacturing Technologies, and Closed-Loop Manufacturing and Product Design. These findings offered critical insights into emerging research trends and served as a foundational reference for future research and policy-making aimed at promoting sustainability in manufacturing.

AI-Driven Process Design for Closed-Loop Manufacturing

This study investigates the potential of artificial intelligence (AI) in enhancing closed-loop manufacturing (CLM) systems, which emphasize sustainability through recycling, reuse, and optimized resource utilization. CLM faces significant challenges in minimizing waste, improving operational efficiency, and optimizing resource usage. Although AI holds promise in addressing these challenges, its application in CLM systems, especially in dynamic decision-making and resource optimization, requires further exploration. This research aims to explore how AI technologies can improve material flows, reduce waste, and enhance decision-making to achieve both sustainability and operational efficiency in manufacturing systems. To understand the impact of AI on CLM, the study conducted a comprehensive review of case studies and literature across various industries. It focused on AI techniques, such as machine learning (ML) and reinforcement learning (RL), and their roles in optimizing resource usage, refining recycling processes, and improving production schedules. Statistical methods, including regression and sensitivity analysis, were employed to evaluate the effectiveness of AI-driven models in enhancing closed-loop manufacturing performance. The review revealed promising results, highlighting AI's ability to optimize resource consumption and waste management strategies across diverse manufacturing environments. The study’s findings demonstrated significant improvements in resource efficiency, waste reduction, and cost savings across multiple industries. AI-driven models optimized material consumption, resulting in reductions of up to 12% in raw material use. Additionally, AI-enhanced recycling and waste management strategies led to a 25% increase in material recovery, while operational costs were reduced by up to 15%. AI models also displayed a strong ability to adapt to fluctuations in demand and production conditions, ensuring sustained operational efficiency even amid dynamic market changes. These outcomes highlight AI’s transformative potential in achieving sustainable manufacturing practices and advancing circular economy goals.

A novel method of hydraulic valves design for additive manufacturing based on structural decomposition

A novel method of hydraulic valves design for additive manufacturing based on structural decomposition

Multi-Material 3D Printing and Computational Design in Pharmaceutical Tablet Manufacturing

Multi-material 3D printing has revolutionized pharmaceutical tablet manufacturing by enabling unprecedented control over the spatial arrangement of active pharmaceutical ingredients (APIs) and excipients. This systematic review analyzes the significant advances in computational methods and 3D printing technologies for pharmaceutical applications from 2005 to 2024. The review explores the integration of artificial intelligence and evolutionary algorithms in solving complex inverse problems of tablet design, where computational methods achieve better accuracy in predicting drug release profiles. Recent developments in material science, including novel thermoresponsive polymers and stimuli-responsive materials, have enhanced manufacturing capabilities while maintaining drug stability. Clinical trials and real-world implementations demonstrate improvements in therapeutic outcomes, with personalized 3D printed medications showing enhanced treatment efficacy and better safety profiles compared to conventional formulations. The review also addresses critical challenges in regulatory compliance, quality control, and scale-up processes, providing a framework for future developments in personalized medicine manufacturing. This work synthesizes current knowledge and identifies emerging trends, offering insights into the future direction of pharmaceutical 3D printing technology and its implications for personalized medicine.

A user-centered approach in complex engineering design environments

In complex engineering design environments, the demand for high quality products and high functionality are critical, the end-user requirements have to be integrated into the design process since the earliest design stages. In such contexts, strict design requirements related to quality, safety, and usability must be taken into account concurrently. To this aim, in this work, an integrated user-centered decision-based design method is proposed in the medical design environment context. This work is aimed at proposing a design method to be applied to complex design environments. The proposed case study is the design of an intercom device, aimed at improving patient-doctor communication in the case of bedridden patients on with helmet for Continuous Positive Airway Pressure (CPAP) therapy during SARS- CoV2 pandemic emergency. Patients undergoing helmet-assisted ventilation are often immersed in a highly noisy environment, unable to fully communicate their needs to the doctors. A three-steps decision based integrated product design and process simulation are used in the early design stages, to gain optimal product designs in a user-centered design viewpoint, for novel industrial products. The first step, modular design, is aimed at defining relations between the components of the assembly in a user centered approach. Design alternatives are generated in a Design for Manufacturing and Assembly DFMA viewpoint. In the second step, the group decision making step, in a multiple criteria context, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is applied together with the fuzzy set theory, to overcome uncertainties in the verbal judgments of the decision makers. The third step is related to the simulation of the assembly and manufacturing process, with the aim of defining a final optimal design. The proposed group multicriteria decision making based design method proved to be efficient in complex user centered engineering design contexts.

Closed-loop supply chain decision making considering product design strategy under patent protection

This paper combines product design and patent protection and construct a dynamic decision-making model consisting of a single original manufacturer and a single remanufacturer based on Stackelberg game. We study the impact of different product design strategies on remanufacturing decisions in the presence of patent protection and explore the influencing factors of the original manufacturer's product design level. The results show that: (1) the original manufacturer’s product design level is negatively correlated with the coefficient of R&D cost impact and the coefficient of remanufacturing cost, and positively correlated with the coefficient of consumer utility impact; (2) the unit patent licensing fee is positively correlated with the coefficient of R&D cost impact and negatively correlated with the coefficient of remanufacturing cost and the coefficient of consumer utility impact; (3) under different product design strategies, product design strategies that consider both eco-orientation and remanufacturing-orientation are most conducive to the stable development of the closed-loop supply chain.

Manufacturing design and static analysis of a gear shaft

This paper presents a gear shaft design that was constructed with the use of Solidworks, the manufacturing process of the design done by the use of the Edgecam software and also a Finite Element Analysis (FEA) which includes examining the part's displacement behavior with corresponding stress and deformation. The 3-dimensional finite element simulation and meshing of the model was done with the ANSYS Workbench. Boundary requirements including external loads were also utilized. The manufacturing design was done in two parts due to the high level of complexity associated with the gear shaft. The first part included the parametric configurations used for machining one end of the gear shaft and the second part included the parametric configurations used for machining the other end of the gear shaft. This means that when the machining process has to be done on the CNC machine, there will be two separate CNC codes to be utilized which can run successively. During the manufacturing design phase, important information was obtained, such as the overall time for machining of one or both ends of the gear shaft as well as the safety conditions that were utilized during the process.

Molecular Engineering Chemical Pre-lithiation Reagent with Low Redox Potential for Graphite Anode Enables High Coulombic Efficiency.

Graphite (Gr) is a low-cost and high-stability anode for lithium-ion batteries (LIBs). However, Gr anode exhibits an obstinate drawback of low initial Coulombic efficiency (ICE), owing to the active lithium loss for the solid electrolyte interphase (SEI) layer. Herein, a straightforward and effective chemical pre-lithiation strategy is proposed to compensate for the lithium loss. A molecular engineering phenanthrene-based lithium-arene complex (Ph-based LAC) reagent is designed by density functional theory (DFT) calculations. The engineering Ph-based reagent enhances the stability of the π-electron system and the electron-donating capacity, resulting in a reduced redox potential to facilitate lithium transfer. The electrochemical distinct of the Ph-based reagent is illustrated, the prelithiation process in a low Li-insertion platform, and the lithiation degree is controllable with the dipping time (ICE = 102%, 3min). Notably, a denser and homogeneous SEI layer has pre-formed to enhance the Li+ transport and interface stability. Moreover, the lithium-ion full batteries assemble with LiFePO4 and NCM811 cathode, which exhibits high ICE (96.5% and 90.3%) and energy density (310 and 333Whkg-1). These findings present a facile and controllable pre-lithiation strategy to compensate for the lithium of LIBs, providing new valuable insights into the design and optimization of battery manufacture.

An integrated supply chain network design for advanced air mobility aircraft manufacturing using stochastic optimization

An integrated supply chain network design for advanced air mobility aircraft manufacturing using stochastic optimization

Enhancing process optimization through adaptive jig design in lean manufacturing: insights from Universitas Mercu Buana's laboratory

Enhancing process optimization through adaptive jig design in lean manufacturing: insights from Universitas Mercu Buana's laboratory

Interface optimization design and bonding mechanism of friction rolling additive manufactured aluminum/steel dissimilar metal

The aluminum/steel dissimilar structure is a cornerstone for lightweight components and equipment. Yet, traditional fusion welding methods' high heat input frequently gives rise to detrimental defects such as porosity, cracks, and excessively thick intermetallic compound layers (IMCLs) at the interface. To overcome these challenges, this paper innovatively combines low heat input solid-state friction rolling additive manufacturing (FRAM) and strategic interface design to achieve reliable bonding between these dissimilar metals. Our investigation found that conventional FRAM (C-FRAM), hindered by inadequate heat input, struggled to facilitate continuous atomic migration, leading to incomplete joint formation. However, the introduction of arc-assisted FRAM (Aa-FRAM) significantly increased aluminum/steel mixing, fostering interdiffusion of interface atoms under high temperature and pressure conditions. This resulted in the formation of uniform 2.3 μm IMCLs composed of Fe7Al11, Fe4Al13, and FeAl6, and the nanoscale amorphous layer was found between IMCLs and steel. The metallurgical bonding was successfully established at the Aa-FRAM interface. Moreover, by using arc/micro-hole assisted FRAM (AHa-FRAM), which machined an array of micro-holes on the steel surface, we further optimized the aluminum-steel interface bonding quality. The plasticized aluminum alloy (Al alloy) seamlessly flowed into these micro-holes, creating a robust “self-riveting” structure that bolstered mechanical interlocking at the interface. Consequently, we achieved a high-strength joint with an exceptional ultimate tensile strength (UTS) of 167.2 MPa. In addition, the crystallographic analysis showed that the grain size was significantly refined by using the two auxiliary methods, which played a fine grain strengthening role on the interface. This paper innovatively improves the interface bonding between Al alloy and steel through the combination of solid-state FRAM and interface design, thereby opening up a new pathway for the manufacture of aluminum-steel dissimilar structural components.

Precision mechanical manufacturing and product appearance VR design based on environmental thermal energy control

Precision mechanical manufacturing and product appearance VR design based on environmental thermal energy control