Product Manufacturing Process Research Articles

Sustainable Ultrasound Assisted Extractions and Valorization of Coffee Silver Skin (CS)

The Sustainable Development Goals (SDG) encourage the efficient use of sustainable technologies. Ultrasound-assisted extraction (UAE) is one of the extraction process techniques, which are also directed towards sustainability as a goal. Coffee silver skin (CS), being a healthy raw material as well as a waste, could be utilized in the manufacturing process of new dietary products. The goal of this research was to isolate proteins and polyphenols from CS using UAE and to employ spectrophotometry to determine the yields. Three parts of the research were conducted: ultrasonic extraction, the optimization of UAE conditions for the isolation of proteins and polyphenols from CS, and the analysis of the amino acid extract obtained with the optimal use of UAE. According to the results, it was reported that the highest yields of total polyphenols isolated from the CS using UAE were obtained by applying an amplitude of 75% and a time interval of 9 min. The optimal parameters of UAE, when considering the proportions of total polyphenols and proteins, are an amplitude of 100% and a time of 9 min. The most abundant amino acids in isolated proteins (Asp, Glu, Pro, Gly, and Ala) were defined as well. Based on the use of energy, it was obvious that UAE is a promising technology. This concurs with the proposed practice that when non-thermal technologies are analyzed from an environmental point of view, the first common denominator is the use of electricity to run the equipment, in relation to resource depletion. As expected, CS poses a great waste to be recycled, being a nutritionally rich raw material with great potential. Quantitative consideration on the environmentally friendly applicability of CS in mass production should be carried out to validate the entire process of developing a new product from both economic and environmental aspects.

Abstract 4062: The discovery and development of a CRISPR/Cas9-engineered tumor-infiltrating lymphocytes product (GT316) as a next-generation TIL therapy

Abstract Adoptive cell therapy by tumor-infiltrating lymphocytes (TILs) has demonstrated promising therapeutic effects in multiple types of solid tumors and significantly prolonged the survival of late-stage patients. However, the “prone-to-exhaustion” phenotype of the final TIL product after rapid expansion during manufacture and the presence of various immunosuppressive mechanisms in tumor microenvironment (TME) compromises the persistence and anti-tumor efficacy of TIL post infusion. To discover potential immunoregulatory targets that could maximize the function of TIL, we established a genome-wide CRISPR/Cas9 screening platform, ImmuT Finder®, to unbiasedly identify functional gene targets in primary T cells. Notably, this platform employed T cells bearing a tumor-antigen specific TCR-T that were repeatedly stimulated by tumor cells with cognate tumor antigen and demonstrated similar exhaustion profile as TIL. We selected top 30 targets after several rounds of screening and quickly validated their function in a 96-well array. Then, the top targets that showed significantly enhancement in proliferation and function of TCR-T were further selected, and their combinational effects on TIL were tested both in vitro and in vivo. Finally, double KO of GT304 and GT312 (GT316) stands out as the most potent combination, which significantly improves TIL proliferation and cytokine release, eradicates tumor growth in both CDX and PDX mouse models and promotes long-term in vivo persistence with low-dependence on IL-2. Specific and potent sgRNAs targeting both targets have been selected, and manufacturing process of GT316 TIL product has been developed (KOReTIL®). In summary, these data demonstrated that our ImmuT Finder® platform enables unbiased discovery of potent and novel targets for the development of T cell therapy and support the clinical assessment of GT316 as a next-generation TIL therapy. Currently, an investigator-initiated trial of GT316 is ongoing in China. Citation Format: Yarong Liu, Jingwei Sun, Yao Sheng, Jingman Wang, Jiahui Jin, Fei Li, Lingyun Chen, Di Zhou, Yongchao Tan, Quanwei Wang, Zhao Xu, Haibao Li, Pin Wang, Jun Cui. The discovery and development of a CRISPR/Cas9-engineered tumor-infiltrating lymphocytes product (GT316) as a next-generation TIL therapy. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4062.

Product-part level digital twin modeling method for digital thread framework

Digital twin (DT) is not only a core technology of smart manufacturing but also a dynamic intelligent system. By organically combining virtual and realistic models with multiple data sources, DT is used in monitoring product operation status and in predicting product life. A systematic modeling method with high fidelity, punctuality, and efficiency has an important meaning to DT model. However, most of the current DT modeling approaches focus on individual objects and individual aspects of products, and lack of modeling approaches for the whole life cycle and multiple objects, which is not conducive to the mining and utilization of data from all aspects of products. On account of this, a part-level DT modeling approach is proposed based on PLM/PDM theory, integrating the property model, the process model, the simulation model, and the feedback model within a digital thread communication framework, with their modeling methods and application algorithms investigated separately. The results of the Part Digital Twin Model (PDTM) are presented by virtue of a visualization platform. There is interaction among customers, designers, and processors throughout the entire system, ensuring real-time monitoring of product manufacturing process. Finally, a test case of hanging axle is implemented, aiming at validating the proposed part-level DT modeling method. According to the result of this case, the proposed method in this paper can be used in generating the property of hanging axle, and giving real-time monitoring to the whole lifecycle of hanging axle, thereby demonstrating its feasibility.

PEMBERDAYAAN TTG UNTUK MENINGKATKAN PRODUKSI AMPIANG PADA MITRA DI KOTA PANGKALPINANG

The aim of the Community Service Program for the Ampiang manufacturing group partners in the Selindung Lama sub-district, Gabek District, Pangkalpinang City is to increase production capacity and quality and improve the partner's business management system. The survey results found problems in the fish grinding production process carried out by partners still using the manual method. As a result, partners' production capabilities are limited. In addition, from a business management perspective, partners are still not optimal. The average fish grinding ability of partners is 7-10 kg in 4 hours. From the results of the discussion, the production system was improved by holding a fish grinding machine and marketing management training. Making alternative concepts based on data from literature studies, interviews with partners. The alternative concept chosen as the machine design is assessed based on the results of discussions with partners which are seen in the aspects of frame dimensions, machine height, grinder dimensions, motor power used, machine plate size used. The manufacturing production process is carried out in the Mechanic workshop of the Bangka Belitung State Polytechnic of Manufacturing (Polman Babel). The trial results of the fish grinding machine obtained a very fast time of 10 kg/5 minutes. So that Ampiang's production, which was previously only 15 kg, increased to 45 kg. The business management training that was carried out included small/medium business bookkeeping training, and small industry business management training. As a result, the bookkeeping and marketing process is more focused and orders can be made online.

Modeling product carbon footprint for manufacturing process

To reduce carbon intensity and improve energy efficiency in product manufacturing, a comprehensive carbon emission model is essential. Most of the existing researches focus on specific aspects of part machining and lack a quantitative model of the product manufacturing process. Therefore, this paper decomposes and evolves the carbon footprint of product manufacturing from the workshop layer, forming a systematic carbon emission quantitative model. The product manufacturing process is divided into manufacturing equipment layer, part layer, product layer and workshop layer. The sources of carbon emissions in each layer are analyzed and modeled. Finally, take a certain type of wind turbine gearbox as an example. The carbon emissions model for manufacturing process of wind turbine gearbox is established, which provides directions for carbon emission reduction of related manufacturing enterprises. The results show that the rear housing, front housing, and 1st plant carrier are the main carbon emission parts of wind turbine gearbox.

P2P Cloud Manufacturing Based on a Customized Business Model: An Exploratory Study

To overcome the problems of long production cycle and high cost in the product manufacturing process, a P2P (platform to platform) cloud manufacturing method based on a personalized custom business model has been proposed in this paper by integrating different technologies such as deep learning and additive manufacturing (AM). This paper focuses on the manufacturing process from a photo containing an entity to the production of that entity. Essentially, this is an object-to-object fabrication. Moreover, based on the YOLOv4 algorithm and DVR technology, an object detection extractor and a 3D data generator are constructed, and a case study is carried out for a 3D printing service scenario. The case study selects online sofa photos and real car photos. The recognition rates of sofa and car were 59% and 100%, respectively. Retrograde conversion from 2D data to 3D data takes approximately 60 s. We also carry out personalized transformation design on the generated sofa digital 3D model. The results show that the proposed method has been validated, and three unindividualized models and one individualized design model have been manufactured, and the original shape is basically maintained.

Research on the Application of Computer-Aided Industrial Design and 3D Printing Technology: A Comprehensive Perspective Analysis

With the continuous development and popularization of computer-aided industrial design technology and 3D printing technology, the integration of the two is also receiving more and more attention. Starting from the basic principles and development history of computer-aided industrial design technology and 3D printing technology, this paper introduces the advantages and current situation of the integration of the two, and focuses on exploring the integration of computer-aided industrial design and 3D printing technology in product design, process analysis, and manufacturing process optimization. Finally, the development trend and future prospects of integration application are summarized.

Surrogate model based prediction of transmission error characteristics based on generalized topography deviations

Simulation models of the tooth contact can provide valuable information of the gearbox operational behavior for deviated gears. It is therefore possible to gather geometry and deviation information of the two gears in contact and then estimate how they will perform in the assembled unit before even mounting them. This procedure could save time and costs used to disassemble gear boxes which fail the end of line tests because of acoustic reasons. The major challenge using sophisticated simulation models, which can describe the tooth contact is the tremendous amount of calculation time they need to produce suit-able results. In a productive gearbox manufacturing process this time is just not available. Therefore, a method to predict the operational behavior faster, than with the current used simulation models is needed. Firstly the size of the problem is downsized by introducing the sum deviation surface. It allows to reduce the number of necessary input parameters for a gear topography description to eighteen factors. With the help of that sum deviation surface, 3000 variants within a given variation space are calculated. The resulting training dataset is used to develop a deep neural network meta-model of the gear contact, which can predict the characteristics of the transmission error under load. With the help of that meta-model, the excitation of a gear pair can be predicted faster than real-time.

The China effect on the productivity in Nordic countries

Using a linear panel data model with a fixed-effect estimator, this article investigates the causal effect of the growing use of Chinese intermediates on the labour productivity growth in Nordic manufacturing production processes. The main result – based on changes within more than 70 global value chains during the period 2000–2014 – is that the effect is positive and economically relevant. This productivity effect exists before and after the financial crisis, in all Nordic countries, and is well-spread among the sub-sectors of the manufacturing industry. The effect is mainly caused by reduced employment, not increased value added. This employment effect appears both in the manufacturing sub-sectors themselves and along their domestic supply chains. Finally, China does not seem to be special: the productivity effect of the growing use of Eastern European intermediates is equally pronounced.

Detection Method for Shrinkage Cavities Inside Spheroidal Graphite Cast Iron Using Vibration Measurement by Electromagnetic Force Excitation

The inspection of shrinkage cavities formed inside spheroidal graphite cast iron during the manufacturing process of spheroidal graphite cast products, including large cast structures and machine parts, is considered to be important for quality assurance of cast products. X-ray and ultrasonic methods are generally used for this inspection. However, the former inspection method takes a long time and considerable equipment costs, the latter method requires the use of water as a contact couplant and to polish the surface of the inspection site of the cast iron. In an actual manufacturing factory, there is a demand for a simple and quick method for inspecting shrinkage cavities inside cast iron. In this research, as a simple and high-speed inspection method to evaluate shrinkage cavities inside cast iron by measuring of the vibration of the electromagnetic force is proposed. Three-dimensional electromagnetic field analysis using the finite element method and displacement analysis was performed to analyze the phenomenon, and the usefulness of this method was confirmed by verification experiments.

Ceramics Copyright Protection: in China’s Perspective

Developed countries and regions are gradually shifting their labor-intensive industries to developing countries due to energy and raw material constraints and rising human resource costs. Companies keep their technological development and pattern design in their home countries and transfer their raw materials and product manufacturing processes to developing countries. As a result of these factors, the production of ceramic products in developing countries has increased significantly. From the Chinese side, in 2013, China proposed the "One Belt, One Road" foreign development strategy, which has brought more business opportunities for the ceramics industry and promoted the development of China's ceramics industry. However, behind the strengthening of competitiveness, the weakness of insufficient protection of intellectual property rights in ceramics has also been exposed. As of February 2022, a total of 3,029 court decisions. Therefore, the legislature needs to think about how to strengthen the protection of intellectual property rights in ceramics. This study analyzes the current state of the ceramics industry, and the protection of intellectual property rights in ceramics and incorporates a survey of data on Jingdezhen as a center for the protection of intellectual property rights in ceramics in China. It will provide a good external environment for ceramic enterprises to develop and innovate, achieve a benign mechanism for market competition, protect the legitimate interests of rights holders, stimulate enthusiasm for ceramic innovation and creation, and promote the development of the ceramic industry.

Current status and research progress of occupational health monitoring in welding fume operations

Welding operations are widely present in the manufacturing production process, involving a large number of occupational groups, and are the key occupations where work injuries and occupational diseases occur in China. For different welding processes and welding materials, the content and focus of occupational health monitoring are different. At present, the item of occupational health examination in welding operation is in poor consistency with the on-site exposure of occupational hazard factors, and it is mainly concentrated in the stage of disease development, which can not reflect the early health damage caused by welding dust exposure in time. The emergence of biomarkers of welding dust can make up for this defect. Therefore, it is of great significance to describe the current situation of occupational health monitoring of welding dust and summarize the research progress of related biomarkers for the early prevention of diseases caused by welding dust and the practice of occupational health monitoring.

Robust optimization design method for structural reliability based on active-learning MPA-BP neural network

PurposeMechanical products usually require deterministic finite element analysis in the design phase to determine whether their structures meet the requirements. However, deterministic design ignores the influence of uncertainties in the design and manufacturing process of mechanical products, leading to the problem of a lack of design safety or excessive redundancy in the design. In order to improve the accuracy and rationality of the design results, a robust design method for structural reliability based on an active-learning marine predator algorithm (MPA)–backpropagation (BP) neural network is proposed.Design/methodology/approachThe MPA was used to obtain the optimal weights and thresholds of a BP neural network, and an active-learning function applicable to neural networks was proposed to efficiently improve the prediction performance of the BP neural network. On this basis, a robust optimization design method for mechanical product reliability based on the active-learning MPA-BP model was proposed. Random moving quadrilateral sampling was used to obtain the sample points required for training and testing of the neural network, and the reliability sensitivity corresponding to each sample point was calculated by subset simulated significant sampling (SSIS). The total mass of the mechanical product and the structural reliability sensitivity of the trained active-learning MPA-BP model output were taken as the optimization objectives, and a multi-objective reliability-robust optimization design model was constructed, which was solved by the second-generation non-dominated ranking genetic algorithm (NSGA-II). Then, the dominance function was used in the obtained Pareto solution set to make a dominance-seeking decision to obtain the final reliability-robust optimization design solution. The feasibility of the proposed method was verified by a reliability-robust optimization design example of the bogie frame.FindingsThe prediction error of the active-learning MPA-BP neural network was smaller than those of the particle swarm optimization (PSO)-BP, marine predator algorithm (MPA)-BP and genetic algorithm (GA)-BP neural networks under the same basic parameter settings of the algorithm, which indicated that the improvement strategy proposed in this paper improved the prediction accuracy of the BP neural network. To ensure the reliability of the bogie frame, the reliability sensitivity and total mass of the bogie frame were reduced, which not only realized the lightweight design of the bogie frame, but also improved the reliability and robustness of the bogie.Originality/valueThe MPA algorithm with a higher optimization efficiency was introduced to find the weights and thresholds of the BP neural network. A new active-learning function was proposed to improve the prediction accuracy of the MPA-BP neural network.

Simulation uncertainty determination of single flank rolling tests using monte carlo simulation and skin model shapes for zero defect manufacturing of micro gears

Simulative gear rolling enables the integration of function-oriented quality assurance into production processes for Zero Defect Manufacturing (ZDM). A simulated single flank rolling test can be carried out using inline measurements. With the aim of integration for the control of production parameters and a functional forecast, this paper aims at an uncertainty evaluation of an analytic tooth contact analysis software. Based on optical measurements of focus variation technology and actual micro gear test facility procedures, task-specific simulation uncertainties for the four parameters of the single flank rolling inspection are evaluated. This approach uses an optimized Latin Hypercube Sampling (LHS) approach to generate artificial gear point clouds with specified deviations for a Monte Carlo simulation (MCS) based on an adaptive scheme of the Guide to the Expression of Uncertainty in Measurement (GUM) for determining model uncertainties. It was shown that the fast simulative results have a relatively low expanded uncertainty of less than 6 µm. The use case is a high-volume micro-production.

FDA/M-CERSI Co-Processed API Workshop Proceedings

These proceedings contain presentation summaries and discussion highlights from the University of Maryland Center of Excellence in Regulatory Science and Innovation (M-CERSI) Workshop on Co-processed API, held on July 13 and 14, 2022. This workshop examined recent advances in the use of co-processed active pharmaceutical ingredients as a technology to improve drug substance physicochemical properties and drug product manufacturing process robustness, and explored proposals for enabling commercialization of these transformative technologies. Regulatory considerations were discussed with a focus on the classification, CMC strategies, and CMC documentation supporting the use of this class of materials from clinical studies through commercialization. The workshop format was split between presentations from industry, academia and the FDA, followed by breakout sessions structured to facilitate discussion. Given co-processed API is a relatively new concept, the authors felt it prudent to compile these proceedings to gain further visibility to topics discussed and perspectives raised during the workshop, particularly during breakout discussions.Disclaimer: This paper reflects discussions that occurred among stakeholder groups, including FDA, on various topics. The topics covered in the paper, including recommendations, therefore, are intended to capture key discussion points. The paper should not be interpreted to reflect alignment on the different topics by the participants, and the recommendations provided should not be used in lieu of FDA published guidance or direct conversations with the Agency about a specific development program. This paper should not be construed to represent FDA's views or policies.

Development and Preliminary Application of a Droplet Digital PCR Assay for Quantifying the Oncolytic Herpes Simplex Virus Type 1 in the Clinical-Grade Production.

Oncolytic herpes simplex virus (oHSV) is a type of virus that selectively targets and kills cancer cells, leaving normal cells unharmed. Accurate viral titer is of great importance for the production and application of oHSV products. Droplet digital PCR (ddPCR) is known for having good reproducibility, not requiring a standard curve, not being affected by inhibitors, and being precise even in the detection of low copies. In the present study, we developed a droplet digital PCR assay for the quantification of HSV-1 and applied it in the oHSV production. The established ddPCR showed good specificity, linearity, a low limit of quantification, great reproducibility, and accuracy. The quantification result was well-associated with that of plaque assay and CCID50. Amplification of the purified virus without DNA extraction by ddPCR presented similar results to that from the extracted DNA, confirming the good resistance against PCR inhibitors. With the ddPCR, viral titer could be monitored in real time during the production of oHSV; the optimal harvest time was determined for the best virus yield in each batch. The ddPCR can be used as a useful tool for the quantification of oHSV and greatly facilitate the manufacturing process of oHSV products.

A New Hybrid Fermatean Fuzzy Set and Entropy Method for Risk Assessment

Risk evaluation is an important part of the product design and product manufacturing process; it entails the pursuit of the highest product quality and preventing failure under the constraints of limited resources. The failure mode and effects analysis approach is one of the most widely applied risk evaluation tools that uses the product of the three risk elements of product failure items, severity, occurrence probability, and detection probability, to calculate the risk priority number, the priority of failure risk. However, the typical failure mode and effects analysis method ignores the consideration of objective weights, which may lead to incorrect evaluation results. Moreover, the method of expressing information about product failure provided by experts also directly affects the results of risk assessment. To comprehensively assess the risk of product failure, in this study, the hybrid of the Fermatean fuzzy set and entropy method was used to prioritize product failure items risk. This study used a service failure mode and effects analysis numerical example of self-service electric vehicles to illustrate and test the correctness of the proposed new hybrid Fermatean fuzzy set and entropy method. The mathematical operation results were also compared with the listing of different calculation methods. The test results prove that the proposed new hybrid Fermatean fuzzy set and entropy method can fully consider the cognitive information provided by experts to provide more accurate risk ranking results of failure items.

New manufacturing process of cylindrical body products with thickened edge part

The manufacturing process of body products by combining the operations of reverse extrusion and simultaneous formation of thickening at the edges of the workpiece is considered. The simulation of the operation is performed, during which the effect of deformation modes on forces and stress-strain state is determined. According to the results of the study, rational parameters of the process are identified.

Influence of surface roughness on fatigue performace of additively manufactured metal matrix composite Al2024–RAM2 alloy

Numerous studies on Laser Powder Bed Fusion (L-PBF) have demonstrated the necessity to refine microstructure of additive manufactured (AM) parts to eliminate hot cracking and anisotropy to improve L-PBF processability. It has been shown that ceramic nanoparticles based on Ti can be extremely effective inoculant resulting in fine grains structure for high strength aluminum alloys without hot cracking. The company ELEMENTUM 3D has patented production process of reactive additive manufacturing (RAM) with a low content of ceramic nanoparticles as Al2024-RAM2. This contribution presents results of an experimental determination of high cycle fatigue life of L-PBF Al2024-RAM2 alloy with as-built (i.e., rough) and polished surface. Experiments were done on miniature specimens printed on a SLM 280 HL system with a layer thickness of 60 µm. The specimens were tested in cyclic plane bending at a load ratio of R = 0 at a frequency of 25 Hz. Moreover, the microstructure was investigated by means of scanning and transmission electron microscopy.

Microstructure and Fatigue Properties of Al2024-RAM2 Aluminum Alloy Obtained by Laser Powder Bed Fusion

The wrought 2024 aluminum alloy is an established aluminum alloy with copper as the primary alloying element. Due to its high strength and fatigue resistance, it is widely used for aircraft primary structures. Development of the AM counterpart of this high-strength alloy starting from gas atomized powder has been challenged by its tendency to hot tearing during solidification thus compromising mechanical properties. Recently, the innovation by Elementum 3D made the 2024 alloy compatible with the Laser Powder Bed Fusion technology (L-PBF). The key is a patented production process of reactive additive manufacturing (RAM), which utilizes exothermic chemical reactions to synthesize product materials during the additive process to improve printability while achieving mechanical properties comparable to those of a conventionally produced alloy.This contribution examines the Al2024-RAM2 alloy recently qualified for industrial production by the service company BEAM IT (Fornovo Taro, Italy). The powder was processed in a SLM 280 HL Twin system (SLM Solutions GmbH, DE) to fabricate specimens for fatigue testing which then underwent a solution-plus-aging heat treatment (T6). Specimens oriented in three different directions with respect to the build direction were left in the as-built surface state and used for the characterization of microstructure and surface quality. Fatigue data obtained under high cycle fatigue loading conditions are presented and discussed in terms of directional effect and surface integrity (as-built vs. polished surfaces).