Nowadays, the growing complexities of manufacturing processes and systems make it difficult to identify the root causes of critical deviations in performance. Conventional methods often fall short in ...capturing the multifaceted nature of these challenges, despite a wealth of diverse untapped manufacturing data. To harness the full potential of diverse data sets and transform them into a valuable asset to guide root cause exploration, this paper presents an innovative approach that combines multimodal predictive analysis and explainable artificial intelligence (XAI) to uncover insights into system dynamics. This work contributes to a paradigm shift in industrial decision-making regarding manufacturing diagnostics.
The article investigates the development of a manufacturing route for highly porous titanium foams suitable for craniofacial surgery applications, particularly in cranioplasties. The study focuses on ...the polyurethane replication method for foam production and emphasizes reducing residual gas content, as it significantly affects the mechanical properties and suitability for approval of the foams. Various factors such as starting materials, solvent debinding, heating schedules, and hydrogen atmosphere are analyzed for their impact on residual gas content. It is shown that significant reductions in residual gas content can only be achieved by reworking each step of the process. A combination of initial solvent debinding of the PU template with dimethyl sulphoxide, reduction of suspension additives, use of coarser Gd. 1 powders, and an integrated debinding and sintering process under partial hydrogen atmosphere achieves a significant reduction in residual gas content. This way, the potential for producing titanium foams that comply with relevant standards for craniofacial implants is demonstrated.
Development of oncolytic virotherapy for cancer treatment is attracting great concerns as a next-generation innovative modality. Oncolytic virotherapy usually utilize genetically modified virus that ...is designed to multiply only in cancer cells for safety. Conducting clinical trials as well as pre-clinical studies requires large quantities of high quality viruses as to GMP. Here we present the key considerations regarding GMP compliant process development of manufacturing oncolytic virus, Coxsackie virus type B3 (CVB3), designed for clinical trial targeting triple-negative breast cancer. CVB3 is a non-enveloped, linear single-strand RNA virus, and sizing approximately 27 to 33 nm diameter.
Here we will discuss the key considerations we have gained during the manufacturing process development from the initial model using a zonal rotor centrifugal separator to the later developed systems including tangential flow filtration system and ion chromatography. Later developed system consists of all process, from upstream cell culture expansion to downstream target purification, being designed as fully closed and single-use manufacturing system. In brief, HEK293 cell suspension extended in 3L serum-free medium infected with CVB3, made total 150 mL of final products as 8.43 x 10 exp7 TCID50/mL concentration. Quality analysis of products related impurities as residual human cell protein and remnant DNA/RNA was also confirmed to be within FDA recommended standards. Thus the GMP compliant manufacturing process of oncolytic virus we established considered to be suitable for technology transfer for future commercial production.
The objective of this paper is to present a new advanced Additive Manufacturing (AM) process for the construction of concrete structures: Batiprint3dTM. The proposed advanced technology consists of ...creating a complex wall of 3D-printed materials using a mobile and polyarticulated robot: two polymer-foam printed walls are used to encase a subsequent third wall made of concrete. Once the walls were in place, the foam is maintained to provide both an internal and external insulation to the house without requiring thermal bridges. This technique of the complex wall with 3D-printed composite foam/concrete material is similar to the use of expansive-foam formwork (FW) filled by concrete or Insulated Concrete Forms (ICF) but in that case printed directly on site. By using 3D printing for the foam and extrusion of the concrete with the same robotic system, the technique creates jointly both the structure and thermal elements of the building. In the first part of this paper the composite foam/concrete 3D printing method and optimized process parameters are présented. Polyurethane (PU) foam has weak mechanical properties and the filling of the internal void with concrete can yield in high deformations and even failure of the FW, it is therefore necessary to control this phenomenon. For that, an experimental study has been conducted to determine a filling procedure capable of minimizing the deformations. The results show that spacers between the two foam walls can allow for wall heights of poured concrete up to 50 cm. The problems solved, it was decided to experiment in full scale this new walls 3D printing method with the construction of YhnovaTM, a real 95m² social housing. This technology Batiprint3dTM have been used, it is possible now to propose a synthesis of the impacts of this new advanced technology for construction.
Active pharmaceutical ingredients (APIs) are most commonly formulated and delivered to patients in the solid state. Recently, an alternative API solid-state form, namely the pharmaceutical cocrystal, ...has witnessed increasing academic and industrial interest due to its potential to deliver bespoke physical properties in the pharmaceutical drug product. This interest has been supported by advances in cocrystal discovery, development, and approval, enabled primarily by a supportive new FDA guidance in February 2018. In this review, we describe the process of developing a pharmaceutical cocrystal drug product from screening to approval, with an emphasis on significant developments over the past decade.
Cocrystallization is a promising emerging option to improve the physicochemical solid-state properties of APIs, such as solubility, dissolution rate, stability, and tabletability, to develop better medicines.
Cocrystals provide a unique opportunity for challenging APIs to make the transition to commercial drug products with relatively low risk and high return on investment.
Cocrystals have been researched over the past few decades, yet only a few cocrystals products are commercially available.
Manufacture of cocrystals has been demonstrated through established pharmaceutical industry processes (solution crystallization) and novel alternative methods (mechanochemical approaches).
The latest FDA draft guidance on cocrystals, which recognizes cocrystals as drug substances, provides an excellent opportunity for drug manufacturers to develop commercial formulations of cocrystals.
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Due to the technological advances, sensors have found a significant role in different aspects of human life. The sensors have been fabricated via various manufacturing processes. ...Recently, additive manufacturing (AM) has become a common method for fabrication of a wide range of engineering components in many industries. This manufacturing method, commonly known as three-dimensional (3D) printing is based on melting and solidification that leads to production of a component with high dimensional accuracy and smooth surface finish. As precision and elegant techniques are needed in manufacturing of the sensors, AM has been utilized in fabrication of these parts in the last few years. In this study, we summarized and classified applications of different AM methods in manufacturing of sensors. In this context, we briefly reviewed and compared AM techniques and categorized 3D-printed sensors based on their applications. Moreover, fabrication of sensors via AM is explained in details, challenges and future prospect of this manufacturing process are discussed. Investigations on the performed studies proved that higher printing resolution, faster speed and higher efficiency are needed to reach a remarkable advance in the production of 3D-printed sensors. The presented data can be utilized not only for comparison, improvement and optimization of fabrication processes, but also is beneficial for next research in production of highly sensitive sensors.
Heavy metals are widely distributed in the environment due to the natural processes and anthropogenic human activities. Their migration into no contaminated areas contributing towards pollution of ...the ecosystems e.g. soils, plants, water and air. It is recognized that heavy metals due to their toxicity, long persistence in nature can accumulate in the trophic chain and cause organism dysfunction. Although the popularity of herbal medicine is rapidly increasing all over the world heavy metal toxicity has a great impact and importance on herbal plants and consequently affects the quality of herbal raw materials, herbal extracts, the safety and marketability of drugs. Effective control of heavy metal content in herbal plants using in pharmaceutical and food industries has become indispensable. Therefore, this review describes various important factors such as ecological and environmental pollution, cultivation and harvest of herbal plants and manufacturing processes which effects on the quality of herbal plants and then on Chinese herbal medicines which influence human health. This review also proposes possible management strategies to recover environmental sustainability and medication safety. About 276 published studies (1988–2021) are reviewed in this paper.
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•Medicinal crops suffer with multi-source heavy metals have potential security risks.•Chinese herbal medicine faces security issues related to heavy metals.•Relationship between heavy metals and Chinese herbal medicine is evident.
As a kind of direct-on-line motor, super premium efficiency (IE4) line-start synchronous reluctance motors (LS-SynRMs) were developed recently and are now used in many applications, including fans, ...pumps, and compressors. This paper presents an optimum design and comparative study of LS-SynRMs with additional losses and impact during the manufacturing process (electrical steel cutting/punching damage as well as squirrel-cage die-casting with bubble effects). The work results indicate that the LS-SynRM design with the "manufacturing process loss" effect should be considered and compensated for the design in order to achieve an IE4 class efficiency and ensure synchronization. Furthermore, the LS-SynRM rotor with multilayer flux barriers and rotor slots is investigated in detail. The influences of optimum design geometrical parameters (flux barriers thickness, segments thickness, length of rotor slots, etc.) on the performances of the basic model and optimum design model are evaluated with finite-element analysis (FEA) results. For more accurate results, the effects of saturation, saliency ratio, inductance difference, and the change in the B-H/B-P curve in damaged motor core edges are considered. Meanwhile, in the squirrel cage, the porosity rate distributions are considered. The copper loss, iron loss, starting torque, power factor, efficiency, and synchronization ability are investigated. The experimental results verify the accuracy of the process presented in this paper.
Modeling the uncertainty from data is an essential quest in the learning of neural network models but has not been well addressed. A probabilistic neural network with Gaussian-mixture distributed ...parameters is developed in this work to provide an efficient and high-fidelity solution for learning multimodal uncertainties in neural networks. An adaptive Gaussian mixture scheme is adopted to refine the Gaussian mixture probability distributions and ensure the fidelity of uncertainty propagation in both linear and nonlinear transformations through the network. As its predictive distribution can be inferred analytically, this probabilistic network can be trained efficiently using a backpropagation method based on gradient descent. The proposed network not only achieves a state-of-the-art performance when benchmarked on a series of public datasets but also improves the accuracy and uncertainty quantification quality in two manufacturing process monitoring schemes. In a tool wear monitoring scheme for machining, it reduced the root mean square error (RMSE) by 44% and narrowed the confidence intervals of tool wear prediction by 35% compared to a neuro-fuzzy model. In a porosity monitoring system for additive manufacturing, the proposed network improved the porosity detection accuracy by 2% to 93.6% and quantified confidence intervals that were not available in conventional deep learning models. All these successes prove that the proposed probabilistic neural network can be a promising solution to address practical problems subject to significant uncertainties.
•General-form probability distributions of uncertainty are characterized with Gaussian mixtures.•Predictive distributions of the network can be inferred directly and analytically.•A sampling-free backpropagation method to learn probabilistic parameters is presented.•Remarkable improvement was achieved on two manufacturing process monitoring schemes.
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