Manufacturing Challenges Research Articles

Effects of adaptive backlash minimization in worm gear for precision positioning

The paper introduces innovative solutions for worm gears, aiming at adaptive reduction of side clearance to improve accuracy. It addresses manufacturing challenges affecting tooth dimensions and deviations in shape, examining the impact of adjustment settings on clearance components. The document outlines patented solutions and identifies the causes of inaccuracies in worm gears, with a specific focus on the efficiency of a compliant worm in reducing side clearance. Comprehensive analyses were conducted using Matlab, CAD, and empirical data to investigate the impact of adjustments on clearance statistics, correlation, and spectral analyses before and after corrections. It establishes correlations between clearance parameters and assesses positioning accuracy using a rolling screw system. By demonstrating significant improvements in accuracy without the need for gear disassembly during operation, the developed gear compensates for manufacturing deviations. The paper also suggests assembly procedures for precision worm gears in small-scale production, taking into account various criteria for accuracy assessment.

Overviewing Challenges and Solutions in Lean Manufacturing Implementation for Domestic Enterprises

Companies keep focus on enhancing the quality, efficiency, and customer satisfaction of their products and services while at the same time increasing their profits. Lean Manufacturing emerges as a potent strategy to address these imperatives via identifying and eliminating losses. Hence, it leads to optimized production processes and enhances overall performance of enterprises. This study presents an overview and analysis of lean manufacturing principles, implementation strategies, challenges, and economic benefits for Ukrainian enterprises. The main hypostatize of the paper was to investigate whether the Lean manufacturing approach is associated with different scope of businesses and whether it brings efficient outcomes for all of them. Based on conducted structured literature review the study highlights the versatility of lean methodologies across various industries and administrative processes. Employing scientific methods like comparative analysis and theoretical generalization, the research offers insights into the outcomes of lean implementations, including cost savings, productivity enhancements, and improved product quality for business organizations. The main outcomes of study underscore the critical role of strong leadership commitment and organizational culture in driving successful lean transformations. Through overviewing case studies and practical insights, the article illustrates real-world applications of lean manufacturing principles from different industries, exemplified by organizations AeroStar, Nova Poshta and Boeing. The paper is theoretical. However, the study advocates the practical importance of adoption of lean principles to achieve operational excellence for companies regardless of their scope of operating activities. Also, it offers suggestions for future research to explore emerging technologies' impact and cross-cultural comparisons in lean manufacturing practices.

Effects of shape and structure of a new 3D-printed personalized bioresorbable tracheal stent on fit and biocompatibility in a rabbit model.

To date, several types of airway stents are available to treat central airway obstructions. However, the ideal stent that can overcome anatomical, mechanical and microbiological issues is still awaited. In addition, therapeutic effect and self-elimination of these stents are desirable properties, which pose an additional challenge for development and manufacturing. We aimed to create a prototype bioresorbable tracheal stent with acceptable clinical tolerance, fit and biocompatibility, that could be tested in a rabbit model and in the future be further optimized to enable drug-elution and ensure local therapeutic effect. Twenty-one New Zealand White Rabbits received five different types of bioresorbable tracheal stents, 3D-printed from poly(D,L-lactide-co-ε-caprolactone) metacrylates. Various configurations were tested for their functionality and improved until the best performing prototype could undergo detailed in vivo assessment, regarding clinical tolerance, migration and biocompatibility. Previously tested types of 3D printed stents in our preliminary study required improvement due to several problems, mainly related to breakage, unreliable stability and/or migration within the trachea. Abandoned or refined pre-prototypes were not analyzed in a comparative way. The final best performing prototype stent (GSP2 (Group Stent Prototype 2), n = 8) allowed a transoral application mode and showed good clinical tolerance, minimal migration and acceptable biocompatibility. The good performance of stent type GSP2 was attributed to the helix-shaped surface structure, which was therefore regarded as a key-feature. This prototype stent offers the possibility for further research in a large animal model to confirm the promising data and assess other properties such as bioresorption.

Improving milling tool wear prediction through a hybrid NCA-SMA-GRU deep learning model

Milling tool wear, a ubiquitous challenge in industrial automation and manufacturing, leads to diminished equipment utilization, escalating costs, and a decline in product quality. The prediction of tool wear is a complex and challenging task, as it involves numerous variables. This paper introduces a pioneering hybrid approach, the NCA-SMA-GRU model. It involves the hybridization of three major components and is designed to enhance the precision and expedite the process of tool wear prediction. The NCA component is adept at filtering and retaining the most relevant features associated with milling tool wear from the raw signals, and it also improves the model’s interpretability. Subsequently, SMA optimizes the GRU network’s hyperparameters, including the initial learning rate, hidden layer neurons, network training iterations, and the L2 regularization factor, to identify an optimal combination that bolsters predictive performance. The modeling steps and the development of fitness function are explained in detail. The model’s efficacy is rigorously evaluated using data from the 2010 High Speed CNC Machine Tool Health Prediction Contest (PHM 2010), which encompasses wear data from both single and multiple cutters. The performance is compared using metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-Squared (R2), and computational time. To assess the ability and characteristics of the proposed approach, other popular hybrid models are constructed for comparative analysis. The results demonstrate that the proposed model addresses the limitations of traditional prediction methods and provides insights into the development of deep learning and optimization algorithms for tool wear prediction.

Separation of full, empty, and partial adeno-associated virus capsids via anion-exchange chromatography with continuous recycling and accumulation

The field of recombinant adeno-associated virus (rAAV) gene therapy has attracted increasing attention over decades. Within the ongoing challenges of rAAV manufacturing, the co-production of impurities, such as empty and partial capsids containing no or truncated transgenes, poses a significant challenge. Due to their potential impact on drug efficacy and clinical safety, it is imperative to conduct comprehensive monitoring and characterization of these impurities prior to the release of the final gene therapy product. Nevertheless, existing analytical techniques encounter notable limitations, encompassing low throughput, long turnaround times, high sample consumption, and/or complicated data analysis. Chromatography-based analytical methods are recognized for their current Good Manufacturing Practice (cGMP) alignment, high repeatability, reproducibility, low limit of detection, and rapid turnaround times. Despite these advantages, current anion exchange high pressure liquid chromatography (AEX-HPLC) methods struggle with baseline separation of partial capsids from full and empty capsids, resulting in inaccurate full-to-empty capsid ratio, as partial capsids are obscured within peaks corresponding to empty and full capsids. In this study, we present a unique analytical AEX method designed to characterize not only empty and full capsids but also partial capsids. This method utilizes continuous N-Rich chromatography with recycling between two identical AEX columns for the accumulation and isolation of partial capsids. The development process is comprehensively discussed, covering the preparation of reference materials representing full (rAAV-LacZ), partial (rAAV-GFP), and empty (rAAV-empty) capsids, N-rich method development, fraction analysis, determination of fluorescence response factors between capsid variants, and validation through comparison with other comparative techniques.

Lentiviral vector determinants of anion-exchange chromatography elution heterogeneity.

The demand for Lentiviral Vector (LV) drug substance is increasing. However, primary capture using convective anion-exchange chromatographyremains a significant manufacturing challenge. This stems from a poor understanding of the complex adsorption behaviors linked to LVs intricate and variable structure, such as high binding heterogeneity which is typically characterized by a gradient elution profile consisting of two peaks. Understanding which LV structural components drive these phenomena is therefore crucial for rational process design. This work identifies the key LV envelope components responsible for binding to quaternary-amine membrane adsorbents. Eliminating the pseudotype protein (Vesicular Stomatitis Virus G glycoprotein [VSV-G])did not impact the heterogenous two-peak elution profile, suggesting it is not a major binding species. Digestion of envelope glycosaminoglycans (GAGs), present on proteoglycans, leads to a dramatic reduction in the proportion of vector eluted in peak 2, decreasing from 50% to 3.1%, and a threefold increase in peak 1 maximum. Data from reinjection experiments point towards interparticle envelope heterogeneity from discrete LV populations, where the two-peak profile emerges from a subpopulation of LVs interacting via highly charged GAGs (peak 2) along with a weaker binding population likely interacting through the phospholipid membrane and envelope protein (peak 1).

Preparation and Modeling of Graphene Bubbles to Obtain Strain-Induced Pseudomagnetic Fields.

It has been both theoretically predicted and experimentally demonstrated that strain can effectively modulate the electronic states of graphene sheets through the creation of a pseudomagnetic field (PMF). Pressurizing graphene sheets into bubble-like structures has been considered a viable approach for the strain engineering of PMFs. However, the bubbling technique currently faces limitations such as long manufacturing time, low durability, and challenges in precise control over the size and shape of the pressurized bubble. Here, we propose a rapid bubbling method based on an oxygen plasma chemical reaction to achieve rapid induction of out-of-plane deflections and in-plane strains in graphene sheets. We introduce a numerical scheme capable of accurately resolving the strain field and resulting PMFs within the pressurized graphene bubbles, even in cases where the bubble shape deviates from perfect spherical symmetry. The results provide not only insights into the strain engineering of PMFs in graphene but also a platform that may facilitate the exploration of the strain-mediated electronic behaviors of a variety of other 2D materials.

Non-contact targeted removal of remnant powder particles from additive manufacturing builds with nano-second laser pulsing

Post-production precision cleaning is one critical challenge in Additive Manufacturing (AM) production of high-value, critical parts/builds. A part shedding particle during its operational use could cause severe complications in critical applications such as medical devices and aerospace components. This study presents a non-contact precision particle removal procedure based on a novel Dry Laser Cleaning (DLC) framework. DLC utilizes the thermo-elastic wave generation/propagation and high-frequency surface acceleration fields to dislodge and remove remnant particles due to inertial forces from a well-defined (user-specified) cleaning zone in a non-contact manner, as opposed to cleaning the entire build surface, which could lead to undesirable re-deposition and re-location of particles as well as high cost. The removal of spherical Stainless Steel 316 L (SS316L) micro-particles commonly used in AM from polished, atomically flat Silicon (Si) wafer substrates with uniform high free surface energy using a nano-second laser pulse is experimentally and computationally investigated. In general, a rough surface has a lower average free surface energy; thus, its cleaning, compared to Si wafer, is less challenging. The data with the SS316-Si material pair offers a consistent baseline. In parallel to experiments, a fully-coupled thermo-mechanical finite element study is also conducted for transient surface acceleration simulations to predict the effectiveness of SS316L micro-particle removal from a Si wafer substrate with laser pulsation. It is observed that SS316L micro-particles with a diameter larger than 5.61 μm, 8.43 μm, and 12.24 μm can be removed entirely at 3 mm, 6 mm, and 9 mm distances from its epicenter, respectively. Accordingly, a work-of-adhesion of 990.8 mJ/m2 is selected between the SS316-Si material pair, consistent with the literature. The reported experiments and simulations demonstrate the potential of the proposed rapid precision particle removal procedure for cleaning critical powder-based AM builds, where the targeted removal of particles is essential for the safe and reliable operation of high-value builds and parts.

Recent progress and challenges for manufacturing and operating solid-state batteries for electric vehicles

Recent progress and challenges for manufacturing and operating solid-state batteries for electric vehicles

On the design of vehicle front-ends for pedestrian safety with the advanced Pedestrian Legform Impactor (aPLI)

The latest Euro NCAP Vulnerable Road Users Assessment Protocol (v11.3) introduces the advanced Pedestrian Legform Impactor (aPLI) to reduce the risk of lower extremity injury to pedestrians in a vehicle collision. The addition of femur injury criteria and the added simplified upper body mass, compared to its predecessor –the FlexPLI–, allows a more comprehensive assessment of passive pedestrian safety. However, this presents new challenges for vehicle manufacturers as these additions must be accounted for. We perform four sensitivity analyses at two test positions (centre and at the headlight) on two vehicles (compact car and SUV). Thereby, we investigate five design parameters that represent the main front-end components and identify that the vehicle type determines the sensitive design parameters significantly. Furthermore, we show that the influence of the lower stiffener and energy-absorbing foam diminishes compared to older legform impactors, while that of the bonnet increases.

High-density culturing of the dermal fibroblast cells on hydrogel-based soft microcarriers for cell therapy application

Dermal fibroblasts play a pivotal role in cell therapy, serving as versatile cells essential for tissue repair and regeneration. Their unique ability to produce extracellular matrix components contributes significantly to the efficacy and success of various cell-based therapies in treating skin-related conditions and wounds. Scaling up the production of dermal fibroblasts poses a challenge due to their limited lifespan in culture and the need to maintain their specialized functions over multiple generations. Bioreactors offer a promising solution by providing controlled environments that optimize cell growth, allowing for higher yields of dermal fibroblasts while preserving their functionality and minimizing the limitations associated with traditional culture methods. To overcome the critical challenges in large-scale cell manufacturing, we fine-tuned the composition of our existing smart microcarrier, Cytogel, and tailored its chemical and physical properties to cultivate dermal fibroblasts. The fibroblast cell attachment rate to the new optimized formulation is more than 90%, followed by 60% thermal detachment efficiency in 1 h. Additionally, cells harvested from Cytogel displayed superior CD90 expression and enhanced collagen production compared to fibroblasts cultured in tissue flasks in both static and dynamic bioreactor cultures. Overall, our developed hydrogel-based microcarriers exhibited significant promise for efficient cell expansion, offering a groundbreaking approach for industrial-scale cell culture for cell therapeutic applications.

Thermal, Microstructural, and Mechanical Analysis of Complex Lattice Structures Produced by Direct Energy Deposition.

Lattice structures have gained attention in engineering due to their lightweight properties. However, the complex geometry of lattice structures and the high melting temperature of metals present significant manufacturing challenges for the large-scale fabrication of these structures. Direct Energy Deposition (DED) methods, such as the Wire Arc Additive Manufacturing (WAAM) technique, appear to be an interesting solution for overcoming these limitations. This study provides a detailed analysis of the manufacturing process of carbon steel lattice structures with auxetic geometry. The study includes thermal analysis using infrared thermography, microstructural characterization through metallography, and mechanical evaluation via hardness and mechanical testing. The findings reveal the significant impact of heat input, thermal cycles, and deposition sequence on the morphology and mechanical properties of the lattice structures. Fast thermal cycles are related to areas with higher hardness values, smaller strut diameters, and porous formations, which shows that controlling heat input and heat dissipation is crucial for optimizing the properties of lattice structures produced using WAAM.

Mesenchymal stem cells biological and biotechnological advances: Implications for clinical applications

BackgroundMesenchymal stem cells (MSCs) are multipotent stem cells that are able to differentiate into multiple lineages including bone, cartilage, muscle and fat. They hold immunomodulatory properties and therapeutic ability to treat multiple diseases, including autoimmune and chronic degenerative diseases. In this article, we reviewed the different biological properties, applications and clinical trials of MSCs. Also, we discussed the basics of manufacturing conditions, quality control, and challenges facing MSCs in the clinical setting. MethodsExtensive review of the literature was conducted through the databases PubMed, Google Scholar, and Cochrane. Papers published since 2015 and covering the clinical applications and research of MSC therapy were considered. Furthermore, older papers were considered when referring to pioneering studies in the field. ResultsThe most widely studied stem cells in cell therapy and tissue repair are bone marrow-derived mesenchymal stem cells. Adipose tissue-derived stem cells became more common and to a lesser extent other stem cell sources e.g., foreskin derived MSCs. MSCs therapy were also studied in the setting of COVID-19 infections, ischemic strokes, autoimmune diseases, tumor development and graft rejection. Multiple obstacles, still face the standardization and optimization of MSC therapy such as the survival and the immunophenotype and the efficiency of transplanted cells. MSCs used in clinical settings displayed heterogeneity in their function despite their extraction from healthy donors and expression of similar surface markers. ConclusionMesenchymal stem cells offer a rising therapeutic promise in various diseases. However, their potential use in clinical applications requires further investigation.

Characterization of material flow behavior in friction stir welded AA2014 aluminum alloy joints

Abstract Steel rivets serve as a substitute material for connecting similar and dissimilar materials within the structural fabrication industries. However, the use of steel rivets can result in a significant increase in the structure’s weight and may trigger corrosion at the interface due to galvanic coupling. Combining dissimilar alloys through the fusion welding process poses numerous challenges for manufacturers and designers. A solid-state welding technique called friction stir welding (FSW) has been developed. FSW can effectively join materials without reaching their melting points, relying on severe plastic deformation and recrystallization to form a welded joint. The proper selection of the tool and process parameters is essential for achieving a sound weld. The findings of this study indicate that plastic deformation, material flow, and recrystallization play pivotal roles in the strength of the joint. This implies that FSW represents an ideal joining process for high-strength alloys and serves as a viable alternative to replace permanent joints like rivets.

Effect of CD22-directed CAR-T cells secreting anti-CD19 T cell engagers on control of leukemia progression compared to tandem anti-CD19/CD22 CAR-T cells.

2550 Background: Antigen-specific cancer immunotherapies, based on engineered T cells bearing chimeric antigen receptors (CARs) or the systemic administration of bispecific T cell-engagers (TCEs), have a significant impact on relapsed/refractory (R/R) B cell malignancies. However, a significant percentage of patients relapse following CAR-T or TCE therapy, with antigen loss accounting for up to one third of relapses/progressions. To avoid antigen loss after administration of single-targeted CAR-T cells and to minimize tumor escape, strategies targeting two antigens simultaneously have been developed and validated in both preclinical models and clinical trials. Nevertheless, despite lowering the risk of antigen loss, these strategies still have some limitations, mainly related to design and manufacturing challenges. Methods: In this context, we have generated the first dual-target strategy for hematological malignancies based on T cells bearing an anti-CD22 CAR and secreting an anti-CD19 T-cell engager antibody (CAR-STAb-T cells) and conducted a comprehensive preclinical study comparing its therapeutic potential with a previously validated anti-CD19/CD22 tandem CAR therapy (TanCAR-T) for B-cell acute lymphoblastic leukemia (B-ALL). Results: We have demonstrated in both short- and long-term assays, using contact and non-contact co-culture systems, that CAR-STAb-T cells efficiently redirect bystander T cells, resulting in enhanced cytotoxic activity compared to that exhibited by TanCAR-T cells at very low E:T ratios. In addition, CAR-STAb-T cells induce more potent and faster cytotoxic responses than TanCAR-T cells in both short- and long-term co-culture assays when reproducing antigen-downmodulated conditions in vitro. In vivo assays conducted in NSG mice transplanted with a B-ALL patient-derived xenograft (PDX), followed by intravenous injection of CAR-STAb-T or TanCAR-T cells under a T cell-limiting experimental setting, also showed that CAR-STAb-T cells maintained a tighter control of tumor progression than TanCAR-T cells in peripheral blood and bone marrow. Conclusions: In conclusion, we have demonstrated that the combination of a cell surface CAR and a soluble TCE recognizing different antigens may be advantageous over the use of conventional multi-targeted strategies based on cell surface-anchored receptors. Furthermore, we have proven that a small number of transduced CAR-STAb-T cells is sufficient to redirect non-transduced bystander T cells specifically and efficiently in the presence of leukemia cells, providing a significant advantage over current dual-targeted CAR-T cell therapies. CAR-STAb-T cells could therefore become an alternative to CAR-T therapies for the treatment of R/R B cell malignancies, especially in lymphodepleted patients with low T cell counts.

Molecular design of controllable recombinant adeno-associated virus (AAV) expression systems for enhanced vector production.

Recombinant adeno-associated virus (rAAV) is the leading vector for the delivery of gene therapies. However, low viral genome (VG) titers are common and the proportion of "full" capsids containing the therapeutic gene payload can be highly variable. The coordinated molecular design of plasmids encoding viral components and Helper functions remains a major challenge for rAAV manufacturing. Here we present the design of improved Rep/Cap and Helper plasmids for rAAV2/8 production, (i) a Rep/Cap expression vector harboring independently controllable rep and cap genes and (ii) an improved Helper plasmid harboring E4 gene deletion variants. First, an optimized Rep/Cap vector utilized a truncated p5 promoter, a p5 cis-regulatory element at the 3' end in combination with a heterologous promoter to drive Cap expression and an additional copy of the rep52/40 gene to overexpress short Rep proteins. We demonstrate that Rep78 is essential for efficient rAAV2/8 production in HEK293 cells, and a higher ratio of short Rep to long Rep proteins enhances genome packaging. Second, we identified regulators and open reading frames within the Helper plasmid that contribute to increased rAAV2/8 production. While L4-33k/22k is integral to optimal production, the use of E4orf6-6/7 subset significantly enhanced VG titer. Together, an optimal combination of engineered Rep/Cap and Helper plasmid variants increased VG titer by 3.1-fold. This study demonstrates that configuring and controlling the expression of the different AAV genetic elements contributes toward high rAAV production and product quality (full/empty capsid ratio).

Effects of high gravity on properties of parts fabricated using material extrusion system by additive manufacturing

Additive manufacturing (AM) has gained significant attention in recent years owing to its ability to fabricate intricate shapes and structures that are often challenging or unattainable using conventional manufacturing techniques. This high-quality development trend entails higher requirements for the structural design of 3D printers. In this study, polylactic acid (PLA) and acrylonitrile butadiene styrene (ABS) filaments were fed through a heated extrusion nozzle, which melted the material and deposited it onto a build platform. This study's objectives are high-gravitational material extrusion (HG-MEX) systems development, analyzing the high gravity influences on the flow behavior of materials during extrusion, and understanding the effects of gravitational on material flow and overall extrusion performance. HG-MEX systems have great potential for addressing various challenges in additive manufacturing, such as precise manufacturing. The highlight of the progress is that we developed an HG-MEX system and applied surface science to material extrusion in different gravity. We established a system and obtained results on different gravity, we analyzed the analogy between different gravity phenomena. We analyzed the interplay between the behavior of the fabricated parts and gravity. We analyzed high gravity effects on extrusion processes. The results confirmed the characteristics and feasibility of the developed system. The results suggest that a material extrusion line operating under 15 G conditions resulted in better printing quality compared to one operating under 1 G conditions. This observation implies that high gravity had a positive effect on the extrusion process, leading to improved material extrusion performance.

Fast Pareto set approximation for multi-objective flexible job shop scheduling via parallel preference-conditioned graph reinforcement learning

The Multi-Objective Flexible Job Shop Scheduling Problem (MOFJSP) is a complex challenge in manufacturing, requiring balancing multiple, often conflicting objectives. Traditional methods, such as Multi-Objective Evolutionary Algorithms (MOEA), can be time-consuming and unsuitable for real-time applications. This paper introduces a novel Graph Reinforcement Learning (GRL) approach, named Preference-Conditioned GRL, which efficiently approximates the Pareto set for MOFJSP in a parallelized manner. By decomposing the MOFJSP into distinct sub-problems based on preferences and leveraging a parallel multi-objective training algorithm, our method efficiently produces high-quality Pareto sets, significantly outperforming MOEA methods in both solution quality and speed, especially for large-scale problems. Extensive experiments demonstrate the superiority of our approach, with remarkable results on large instances, showcasing its potential for real-time scheduling in dynamic manufacturing environments. Notably, for large instances (50 × 20), our approach outperforms MOEA baselines with remarkably shorter computation time (less than 1% of that of MOEA baselines). The robust generalization performance across various instances also highlights the practical value of our method for decision-makers seeking optimized production resource utilization.

Configuration of last-mile distribution networks for an encroaching manufacturer

AbstractThe surge of e-commerce has revolutionized distribution channels, escalating from simple single-channel frameworks to complex multi-channel and omni-channel networks. In particular developments in information technology and rising customer expectations have popularized the transition from multi- to omni-channel distribution, where the classic brick-and-mortar stores can also be part of the omni-channel distribution strategy. This evolution poses intricate challenges for manufacturers, especially in the integration and optimization of these channels. Thus, there is a strong need for an in-depth analysis of how manufacturers navigate the transition across diverse distribution channels to meet the varying needs of different customer segments. To this end, we investigate single-, multi-, and omni-channel distribution strategies for the case of a manufacturer selling both standard and customized products to different customer segments with varying preferences. A central contribution of this research is the creation of an integrated optimization model that resolves a location-routing problem, designing a complex and realistic supply chain configuration suitable for an omni-channel distribution system. This model strategically serves to fragmented customer demands through multiple shopping and delivery options. The outcomes of our study indicate that an omni-channel distribution system is a viable approach, capable of serving more customer segments while simultaneously minimizing logistics costs. In addition, we offer a detailed analysis of the cost implications of in-store pickup versus home-delivery options, providing a comprehensive evaluation of their respective impacts on total logistics costs and customer responsiveness.

Developing a virtual reality and AI-based framework for advanced digital manufacturing and nearshoring opportunities in Mexico.

The growing expansion of the manufacturing sector, particularly in Mexico, has revealed a spectrum of nearshoring opportunities yet is paralleled by a discernible void in educational tools for various stakeholders, such as engineers, students, and decision-makers. This paper introduces a state-of-the-art framework, incorporating virtual reality (VR) and artificial intelligence (AI) to metamorphose the pedagogy of advanced manufacturing systems. Through a case study focused on the design, production, and evaluation of a robotic platform, the framework endeavors to offer an exhaustive educational experience via an interactive VR environment, encapsulating (1) Robotic platform system design and modeling, enabling users to immerse themselves in the design and simulation of robotic platforms under varied conditions; (2) Virtual manufacturing company, presenting a detailed virtual manufacturing setup to enhance users' comprehension of manufacturing processes and systems, and problem-solving in realistic settings; and (3) Product evaluation, wherein users employ VR to meticulously assess the robotic platform, ensuring optimal functionality and customer satisfaction. This innovative framework melds theoretical acumen with practical application in advanced manufacturing, preparing entities to navigate Mexico's manufacturing sector's vibrant and competitive nearshoring landscape. It creates an immersive environment for understanding modern manufacturing challenges, fostering Mexico's manufacturing sector growth, and maximizing nearshoring opportunities for stakeholders.