Manufacturing Pipeline Research Articles

Steel surface roughness parameter prediction from laser reflection data using machine learning models

Control of surface texture in strip steel is essential to meet customer requirements during galvanizing and temper rolling processes. Traditional methods rely on post-production stylus measurements, while on-line techniques offer non-contact and real-time measurements of the entire strip. However, ensuring accurate measurement is imperative for their effective utilization in the manufacturing pipeline. Moreover, accurate on-line measurements enable real-time adjustments of manufacturing processing parameters during production, ensuring consistent quality and the possibility of closed-loop control of the temper mill. In this study, we formulate the manufacturing issue into a Time Series Extrinsic Regression problem and a Machine Vission problem and leverage state-of-the-art machine learning models to enhance the transformation of on-line measurements into a significantly more accurate Ra surface roughness metric. By comparing a selection of data-driven approaches, including both deep learning such as convolutional, recurrent, and transformer networks and non-deep learning methods such as Rocket and XGBoost, to the close-form transformation, we evaluate their potential using Root Mean Squared Error (RMSE) and correlation for improving surface texture control in temper strip steel manufacturing.

Optimisation of a primary human CAR-NK cell manufacturing pipeline.

Autologous chimeric antigen receptor (CAR) T-cell therapy of B-cell malignancies achieves long-term disease remission in a high fraction of patients and has triggered intense research into translating this successful approach into additional cancer types. However, the complex logistics involved in autologous CAR-T manufacturing, the compromised fitness of patient-derived Tcells, the high rates of serious toxicities and the overall cost involved with product manufacturing and hospitalisation have driven innovation to overcome such hurdles. One alternative approach is the use of allogeneic natural killer (NK) cells as a source for CAR-NK cell therapy. However, this source has traditionally faced numerous manufacturing challenges. To address this, we have developed an optimised expansion and transduction protocol for primary human NK cells primed for manufacturing scaling and clinical evaluation. We have performed an in-depth comparison of primary human NK cell sources as a starting material by characterising their phenotype, functionality, expansion potential and transduction efficiency at crucial timepoints of our CAR-NK manufacturing pipeline. We identified adult peripheral blood-derived NK cells to be the superior source for generating a CAR-NK cell product because of a higher maximum yield of CAR-expressing NK cells combined with potent natural, as well as CAR-mediated anti-tumor effector functions. Our optimised manufacturing pipeline dramatically improves lentiviral transduction efficiency of primary human NK cells. We conclude that the exponential expansion pre- and post-transduction and high on-target cytotoxicity make peripheral blood-derived NK cells a feasible and attractive CAR-NK cell product for clinical utility.

Versatile Microfluidics for Biofabrication Platforms Enabled by an Agile and Inexpensive Fabrication Pipeline.

Microfluidics have transformed diagnosis and screening in regenerative medicine. Recently, they are showing much promise in biofabrication. However, their adoption is inhibited by costly and drawn-out lithographic processes thus limiting progress. Here, multi-material fibers with complex core-shell geometries with sizes matching those of human arteries and arterioles are fabricated employing versatile microfluidic devices produced using an agile and inexpensive manufacturing pipeline. The pipeline consists of material extrusion additive manufacturing with an innovative continuously varied extrusion (CONVEX) approach to produce microfluidics with complex seamless geometries including, novel variable-width zigzag (V-zigzag) mixers with channel widths ranging from 100-400µm and hydrodynamic flow-focusing components. The microfluidic systems facilitated rapid mixing of fluids by decelerating the fluids at specific zones to allow for increased diffusion across the interfaces. Better mixing even at high flow rates (100-1000µLmin-1 ) whilst avoiding turbulence led to high cell cytocompatibility (>86%) even when 100µm nozzles are used. The presented 3D-printed microfluidic system is versatile, simple and efficient, offering a great potential to significantly advance the microfluidic platform in regenerative medicine.

A trivariate T-spline based direct-slicing framework for support-free additive manufacturing

Standard additive manufacturing (AM) methods require addition of support structures below overhanging structures. Support-free AM methods try to avoid this constraint without loss of manufacturing quality. In this study, we analytically proposed three geometric constraint basic requirements: boundary-normal-vector consistency with geodesics, layer smoothness with low curvature, and inter-layer spacing with pseudo-uniformity. Utilizing trivariate T-splines, our slicing framework can directly partition the solid model into a cluster of convex-hull layers so that the printing material obtains effective bottom support during the AM process. Unlike other proposed methods of support-free AM, this framework is not sensitive to mechanical structure; it can guide the whole manufacturing pipeline analytically. Numerical and manufacturing experiments have verified and demonstrated its capabilities.

Assessing and Engineering Antibody Stability Using Experimental and Computational Methods.

Engineering increased stability into antibodies can improve their developability. While a range of properties need to be optimized, thermal stability and aggregation are two key factors that affect the antibody yield, purity, and specificity throughout the development and manufacturing pipeline. Therefore, an ideal goal would be to apply protein engineering methods early-on, such as in parallel to affinity maturation, to screen out potential drug molecules with the desired conformational and colloidal stability. This chapter introduces our methods to computationally characterize an antibody Fab fragment, propose stabilizing variants, and then experimentally verify these predictions.

A Set of Novel Procedures for Carbon Fiber Reinforcement on Complex Curved Surfaces Using Multi Axis Additive Manufacturing

There has been considerable research in recent years on the additive manufacturing (AM) of carbon fiber reinforced polymer (CFRP) parts based on the process of fused deposition modeling (FDM). The currently-applied steps within the manufacturing pipeline, such as slicing and path planning, consider only the planar case of filament deposition and mostly make no use of the possibility to place single pre-impregnated (prepreg) filaments. Classical methods such as tape-laying and laminating struggle with highly curved and complex geometries and require the costly production of molds, whereas when using AM, these geometries can be realized more easily and molds can be created using the same process. In this paper, a set of algorithms is presented that aims to resolve these problems. Criteria are formulated which enable the goal oriented development and evaluation of the presented methods and represent metrics for future methods. The developed algorithms enable the use of both continuous and discontinuous fiber patches in a much wider range of applications in designing and manufacturing of CFRPs. This opens up new possibilities in this promising field. The developed metrics and infrastructure further constitute progress in the field of multi-axis non-planar path planning for slicing algorithms in general and the conducted evaluation proves the formal applicability of the developed algorithms.

Current status and advances of fish vaccines in Malaysia.

Fish diseases have a significant negative influence on the Malaysian aquaculture industry. Since the 1980s, the sector has grown in size, which has resulted in a rise in the prevalence of infectious outbreaks affecting both freshwater and marine cultured fish species. Demand for commercially available fish vaccinations is predicted to increase as infectious disease outbreaks continue to occur. In Malaysia, aquaculture vaccine research and development (R&D) are still in its infancy, with most efforts concentrating on producing vaccines against bacterial infections, most notably streptococcosis, vibriosis, and motile Aeromonas septicemia. Despite several attempts, no homegrown vaccine has been effectively introduced into the manufacturing pipeline to date. At the moment, only three imported aquatic vaccines have received full permission, a far cry from the 314 and 60 vaccines licensed in the poultry and porcine industries, respectively. This review will describe recent findings regarding the development of aquaculture vaccines for certain fish species and diseases in Malaysia. In our opinion, R&D on fish vaccines is critical to the aquaculture industry’s viability.

Implementation of an Automated Manufacturing Platform for Engineering of Functional Osteochondral Implants

The EU Horizon 2020 project »JointPromise« proposes the development and implementation of an end-to-end automated production platform for three-dimensional joint implants, paving the way for tissue-engineered implants able to regenerate deep osteochondral defects. Currently, the manufacturing pipeline consists in manual production processes for microtissue cultivation, harvest and bioassembly into larger implants. In the conceptualizing stage of this project, the manual processes were translated into standard operating protocols (SOPs) and process design criteria like material flow and throughput as well as technical specifications of laboratory devices for an automated performance were elaborated.Spheroid-based implants provide a novel approach in tissue engineering by aggregating progenitor cells into potent microtissues. After the differentiation of cartilaginous microtissues, functional joint implants are assembled via 3D bioprinting to match the complex structural organization of native cartilage tissue. The »JointPromise« platform includes suitable devices for cell and microtissue cultivation, harvest and implant production as well as quality control in an overall layout consisting of according pipetting units, incubator, centrifuge, bioprinter and high-speed microscope. After initiating the platform build-up, a control software for process controlling and monitoring during cell seeding, cultivation and harvest is implemented. Clinical feasibility and efficacy of osteochondral defect regeneration by the produced joint implants will subsequently be proven in large animal models.This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no 874837.

Impedance controlled human–robot collaborative tooling for edge chamfering and polishing applications

Surface finishing, as the final stage in the manufacturing pipeline, is a key process in determining the quality and life span of a product. Such a task is characterized by low contact forces and minimal material removal from the object surface. Despite the advancements in machine learning and artificial intelligence, human workforce is still irreplaceable in performing such tasks due to superior dexterity and adaptability, but this is often prone to risks such as hand-arm vibration syndrome due to hand-held tools. Therefore, we propose a collaborative approach to assist the human in carrying out such tasks with the help of two case studies: Human–Robot-Collaborative edge chamfering and polishing tasks, based on an impedance controlled collaborative curve tracing technique. We propose a collaborative framework, where the robot assists an operator to guide the end-effector/tool along a pre-defined parametric curve. The algorithm is demonstrated in two scenarios. In the first case, we address a collaborative chamfering task whereas the second case focuses on a polishing application (for straight edges). For these kinds of tasks, the curve to be traced assumes the shape of a straight line along the edge. We make use of the compliant feature of a cobot, which allows the user to physically guide the robot in the task space, to generate a mathematical model for the tool path. From the end-user perspective, this is more intuitive than the classical programming-based path planning approaches. In the process of machining, to enhance the path tracking accuracy and to ensure constant tool-surface contact, we implement guidance virtual fixtures through impedance control. As a result, the machining error is reduced.

Microfluidic processing of stem cells for autologous cell replacement.

Autologous photoreceptor cell replacement is one of the most promising approaches currently under development for the treatment of inherited retinal degenerative blindness. Unlike endogenous stem cell populations, induced pluripotent stem cells (iPSCs) can be differentiated into both rod and cone photoreceptors in high numbers, making them ideal for this application. That said, in addition to photoreceptor cells, state of the art retinal differentiation protocols give rise to all of the different cell types of the normal retina, the majority of which are not required and may in fact hinder successful photoreceptor cell replacement. As such, following differentiation photoreceptor cell enrichment will likely be required. In addition, to prevent the newly generated photoreceptor cells from suffering the same fate as the patient's original cells, correction of the patient's disease‐causing genetic mutations will be necessary. In this review we discuss literature pertaining to the use of different cell sorting and transfection approaches with a focus on the development and use of novel next generation microfluidic devices. We will discuss how gold standard strategies have been used, the advantages and disadvantages of each, and how novel microfluidic platforms can be incorporated into the clinical manufacturing pipeline to reduce the complexity, cost, and regulatory burden associated with clinical grade production of photoreceptor cells for autologous cell replacement.

Toward a Simple Design and Manufacturing Pipeline for Additive Manufacturing

A novel design and manufacturing pipeline for Additive Manufacturing is presented. The architecture of the pipeline is motivated by the observation that the conventional pipeline is unnecessarily complex. Most of the time, only a small set of programming steps suffices for 3D design and manufacture. In particular, the proposed method requires no complex hardware or software, and it generates the geometric data on the fly. This is demonstrated using a simplified evaluation of general volumetric sweeps. The method side-steps many of the problems of the conventional Additive Manufacturing pipeline. Several scripts are provided that illustrate the capabilities and the advantages of the proposed approach. These scripts are processed on a single-board computer and the parts are manufactured on a Fused Filament Fabrication type of 3D printer.

Microbial Toxins in E-Liquid: A Potential New Vaping-Related Exposure to Explore.

Microbial Toxins in E-Liquid: A Potential New Vaping-Related Exposure to Explore.

Towards the global elimination of cervical cancer

Two very effective prevention strategies for cervical cancer exist – vaccination against the human papillomavirus (HPV) and cervical screening with primary HPV testing followed by treatment of precancerous lesions. In 2018, the World Health Organisation called for action towards achieving the global elimination of cervical cancer, and a strategic plan encompassing elimination goals and targets for the scale-up of HPV vaccination, cervical screening and precancer and cancer treatment, particularly in low and middle income countries, will be presented to the 2020 World Health Assembly. The first published estimates suggest that achieving rapid scale-up of both vaccination and twice lifetime cervical screening in all countries would avert up to 13.4 million cervical cancer cases over the next half century, with the majority (but not all) countries achieving incidence of <4 per 100,000 women by 2100. However, there are significant challenges - (i) including vaccine manufacturing pipeline, supply, delivery and hesitancy, (ii) cervical screening HPV self-collection and point-of-care evaluation, acceptability, and scaling up effective precancer treatment processes, (iii) configuration of appropriate referral pathways, cancer treatment services and palliative care for those women who do develop cervical cancer, as well as (iv) the effective financing of both HPV vaccination and cervical screening on a large scale. It is hoped and anticipated that the WHO elimination initiative will galvanise concerted action to address these issues.

Manufacturing Application-Driven Foveated Near-Eye Displays.

Traditional optical manufacturing poses a great challenge to near-eye display designers due to large lead times in the order of multiple weeks, limiting the abilities of optical designers to iterate fast and explore beyond conventional designs. We present a complete near-eye display manufacturing pipeline with a day lead time using commodity hardware. Our novel manufacturing pipeline consists of several innovations including a rapid production technique to improve surface of a 3D printed component to optical quality suitable for near-eye display application, a computational design methodology using machine learning and ray tracing to create freeform static projection screen surfaces for near-eye displays that can represent arbitrary focal surfaces, and a custom projection lens design that distributes pixels non-uniformly for a foveated near-eye display hardware design candidate. We have demonstrated untethered augmented reality near-eye display prototypes to assess success of our technique, and show that a ski-goggles form factor, a large monocular field of view (30o×55o), and a resolution of 12 cycles per degree can be achieved.

Adapting viral safety assurance strategies to continuous processing of biological products.

There has been a recent drive in commercial large-scale production of biotechnology products to convert current batch mode processing to continuous processing manufacturing. There have been reports of model systems capable of adapting and linking upstream and downstream technologies into a continuous manufacturing pipeline. However, in many of these proposed continuous processing model systems, viral safety has not been comprehensively addressed. Viral safety and detection is a highly important and often expensive regulatory requirement for any new biological product. To ensure success in the adaption of continuous processing to large-scale production, there is a need to consider the development of approaches that allow for seamless incorporation of viral testing and clearance/inactivation methods. In this review, we outline potential strategies to apply current viral testing and clearance/inactivation technologies to continuous processing, as well as modifications of existing unit operations to ensure the successful integration of viral clearance into the continuous processing of biological products. Biotechnol. Bioeng. 2017;114: 21-32. © 2016 Wiley Periodicals, Inc.

Worst-case structural analysis

Direct digital manufacturing is a set of rapidly evolving technologies that provide easy ways to manufacture highly customized and unique products. The development pipeline for such products is radically different from the conventional manufacturing pipeline: 3D geometric models are designed by users often with little or no manufacturing experience, and sent directly to the printer. Structural analysis on the user side with conventional tools is often unfeasible as it requires specialized training and software. Trial-and-error, the most common approach, is time-consuming and expensive. We present a method that would identify structural problems in objects designed for 3D printing based on geometry and material properties only , without specific assumptions on loads and manual load setup. We solve a constrained optimization problem to determine the "worst" load distribution for a shape that will cause high local stress or large deformations. While in its general form this optimization has a prohibitively high computational cost, we demonstrate that an approximate method makes it possible to solve the problem rapidly for a broad range of printed models. We validate our method both computationally and experimentally and demonstrate that it has good predictive power for a number of diverse 3D printed shapes.

A retrosynthetic biology approach to metabolic pathway design for therapeutic production

BackgroundSynthetic biology is used to develop cell factories for production of chemicals by constructively importing heterologous pathways into industrial microorganisms. In this work we present a retrosynthetic approach to the production of therapeutics with the goal of developing an in situ drug delivery device in host cells. Retrosynthesis, a concept originally proposed for synthetic chemistry, iteratively applies reversed chemical transformations (reversed enzyme-catalyzed reactions in the metabolic space) starting from a target product to reach precursors that are endogenous to the chassis. So far, a wider adoption of retrosynthesis into the manufacturing pipeline has been hindered by the complexity of enumerating all feasible biosynthetic pathways for a given compound.ResultsIn our method, we efficiently address the complexity problem by coding substrates, products and reactions into molecular signatures. Metabolic maps are represented using hypergraphs and the complexity is controlled by varying the specificity of the molecular signature. Furthermore, our method enables candidate pathways to be ranked to determine which ones are best to engineer. The proposed ranking function can integrate data from different sources such as host compatibility for inserted genes, the estimation of steady-state fluxes from the genome-wide reconstruction of the organism's metabolism, or the estimation of metabolite toxicity from experimental assays. We use several machine-learning tools in order to estimate enzyme activity and reaction efficiency at each step of the identified pathways. Examples of production in bacteria and yeast for two antibiotics and for one antitumor agent, as well as for several essential metabolites are outlined.ConclusionsWe present here a unified framework that integrates diverse techniques involved in the design of heterologous biosynthetic pathways through a retrosynthetic approach in the reaction signature space. Our engineering methodology enables the flexible design of industrial microorganisms for the efficient on-demand production of chemical compounds with therapeutic applications.

Smoke, mirrors, and manufacturable displays

Most development efforts toward the display technology focus on the initial demonstration, with little consideration of the demo's manufacturability. Efforts to make the product commercially available generally hit tremendous obstacles and die out within a few years. Unfortunately, researchers who build and successfully demonstrate prototypes usually are not aware of the difficulties that arise in bringing them to market. In many cases, these displays remain prototypes and will never make it to become a product. The hard part of a manufacturable solution is the cost: bandwidth, video encoding, circuit design and yield, display uniformity, reliability, and image quality all take their toll. When developing the ultimate display, an organization should focus on the manufacturing pipeline, leveraging its existing capacity to get from demo to product more quickly.

Analysis and design of an adaptive minimum reasonable inventory control system

A Master Production Scheduling Decision Support System within a multi-product medical supplies market has the dual task of providing good customer service levels while maintaining minimum reasonable levels of finished goods stock in the face of considerable internal manufacturing lead time and customer demand uncertainty. This paper examines the critical design parameters within an adaptive model highlighting how the total system orders in the internal pipeline are utilized in the decision-making process for assessing how much to load the internal manufacturing pipeline. Two different methods for tracking manufacturing lead times within the adaptive loop are also considered. Classical control concepts are applied within the Decision Support System (DSS) to avoid any long-term drift in finished stocks. Finally scenario analysis is performed via simulation for a set of design parameters and a range of stimuli typical of company operating situations. An effective decision support system design in terms of architecture and parameter settings is recommended based upon the ability of the model to maintain high customer service levels. The DSS readily interfaces between marketing and production functions to enhance company competitive advantage across a wide range of products.