Manufacturing Production Research Articles

Effect of artificial aging on optical properties of ultra-translucent zirconia ceramics of different brands.

This work aimed to evaluate the effect of artificial aging on the translucency and color difference (ΔE) of ultra-translucent zirconia and provide a reference for clinical application. The discs of ultra-translucent zirconia from six brands (Wieland, 3M ESPE, Amann Girrbach, Kuraray Noritake, Upcera, and Besmile) were cut and sintered according to each manufacturer's product instructions; the experimental groups were named ZNT, LVP, AG, KAT, UPC, and BSM, respectively. IPS e.max Press was used as the control group (PLT). The specimens (n=6) were prepared with a diameter of 14 mm and a thickness of 1 mm. The specimens were subjected to artificial aging treatment according to the following conditions: in an autoclave at 134 ℃ at 0.2 MPa for 4, 8, and 12 h. According to the CIE1976Lab system, the CIE L*, a*, and b* values of the specimens before and after aging were measured by a co-lorimeter. The translucency parameter (TP) and ΔE were calculated. The mean TP values of each group before aging inorder were PLT>KAT>AG>ZNT>BSM>UPC>LVP. We found no significant difference in translucency in all experimental groups after aging for 4 and 8 h compared with those before aging. After 12 h of aging, the TP values of the KAT and ZNT groups were not significantly different from those before aging, but the TP values of the AG, BSM, LVP, and UPC groups were significantly lower than those before aging (P<0.05). The TP value of the control group was significantly higher than that of the other experimental groups at different stages (P<0.05). ΔE=3.3 was considered the visible color difference, and ΔE was less than 3.3 in all groups after 4 h of aging. After aging for 8 h, ΔE of the UPC group was slightly higher than 3.3. ΔE of the BSM and UPC groups was greater than 3.3 after 12 h of aging. The TP and ΔE of different brands of ultra-translucent zirconia may change after various aging times. The translucency of some zirconia showed a decreasing trend and the color difference showed an increasing trend with the aging time.

Porosity of Additive Manufacturing Products Obtained by Selective Electron Beam Melting

The paper presents some investigation results of the material porosity of the samples made using the techno-logy of selective electron beam melting from the Ti-6Al-4V alloy powder. The influence of hatching mode parameters on the porosity of samples was carried out using optical microscopy and X-ray computed tomography. A qualitative and quantitative assessments of the shape and size of pores have been made, and technological factors contributing to their appearance have been identified. It has been established that discontinuities of large sizes are formed mainly in the area of overlapping contouring and hatching zones at a distance of 0.2÷2 mm from the sample surface. Selective selection of pores in the surface layer was not detected. It was found that in the range of linear electron beam energy densities of 180÷300 J/m, the minimum porosity is achieved within that range.

Xenia introduces 3D printing pellet version of unfilled PVDF and carbon-fiber reinforced compounds

Xenia® Materials, a developer of carbon fiber reinforced polymers, has presented its new range of PVDF-based compounds for 3D printing pellet-fed applications. This new group of materials is claimed to ensure the carbon fiber properties mixed with the PVDF features, which should open new opportunities for additive manufacturing production where high performance and chemical resistance is necessary.

Small-batch product quality prediction using a novel discrete Choquet fuzzy grey model with complex interaction information

With manufacturing focused on multi-variety and small-batch intelligent production, the challenge is to guarantee and ensure good product quality. This paper proposes a novel small-batch product quality prediction approach considering complex interaction information. In this paper, first, to handle the complex nonlinear correlations among the manufacturing process variables, a kernel principal components analysis is used to extract the major features from the high-dimensional data. Second, we combine the discrete multivariate grey model and generalized discrete Choquet fuzzy integral to propose a discrete Choquet fuzzy grey model so as to capture the complex interaction information among the process variables. Next, an adjoint sensitivity analysis is applied to characterize the changes in the process variables on the predicted product quality index. We conduct an experiment on six semiconductor products made in China to validate the proposed model against a library of nine other methods. Our model yields an average reduction of 11.02% on MAPE, 20.47% on MSE, 13.56% on STD, and an average increase of 46.37% on EVS for six types of products. Our results inform that the proposed model is suitable for predicting manufacturing product quality when the process variables interact significantly, and we can prioritize the process variables for remedial action accordingly.

Technological Innovation, Productivity, and Global Value Chains Participation of Philippine Manufacturers

Using a unique dataset on Philippine manufacturers from 2007 to 2011, this study adopts the three‐stage Crépon‐Duguet‐Mairesse model to trace which trade linkages in global value chains (GVCs) contribute to Philippine firms' technological innovation and labor productivity. The results indicate that Philippine GVC suppliers primarily implement incremental process innovations using imported technology and externally sourced knowledge, while internally developed technology is less common. Accordingly, imported rather than indigenous knowledge contributes more to the labor productivity premium of GVC suppliers. Nevertheless, pure exporting is generally unrelated to process and product innovations. These results suggest that the innovation and productivity effects of GVC participation in the Philippines are mainly driven by imports and foreign‐sourced technology. Since suppliers' innovative behavior tend to complement the technological requirements of their production activities, we argue that the results are consistent with Philippine manufacturers' limited participation in technology‐ and innovation‐intensive GVC functions. The implication on long‐run upgrading is discussed.

Research and Design of Denture Manufacturing Production System Based on Integrated 3D Printing Technology

Research and Design of Denture Manufacturing Production System Based on Integrated 3D Printing Technology

A case study of a bispecific antibody manufacturability assessment and optimization during discovery stage and its implications.

The manufacturability assessment and optimization of bispecific antibodies (bsAbs) during the discovery stage are crucial for the success of the drug development process, impacting the speed and cost of advancing such therapeutics to the Investigational New Drug (IND) stage and ultimately to the market. The complexity of bsAbs creates challenges in employing effective evaluation methods to detect developability risks in early discovery stage, and poses difficulties in identifying the root causes and implementing subsequent engineering solutions. This study presents a case of engineering a bsAb that displayed a normal solution appearance during the discovery phase but underwent significant precipitation when subjected to agitation stress during 15L Chemistry, Manufacturing, and Control (CMC) production Leveraging analytical tools, structural analysis, in silico prediction, and wet-lab validations, the key molecular origins responsible for the observed precipitation were identified and addressed. Sequence engineering to reduce protein surface hydrophobicity and enhance conformational stability proved effective in resolving agitation-induced aggregation. The refined bsAb sequences enabled successful mass production in CMC department. The findings of this case study contribute to the understanding of the fundamental mechanism of agitation-induced aggregation and offer a potential protein engineering procedure for addressing similar issues in bsAb. Furthermore, this case study emphasizes the significance of a close partnership between Discovery and CMC teams. Integrating CMC's rigorous evaluation methods with Discovery's engineering capability can facilitate a streamlined development process for bsAb molecules.

Study on Preparation Optimization Technology and Surface Properties of Decorated Bamboo Filament Board

To further improve the manufacturing process and product performance of decorated bamboo filament board, the Box–Behnken response surface analysis method was used to analyze the correlation between the hot-pressing parameters and surface bonding strength, and the optimal process optimization parameters were obtained. In addition, the wettability and color of each group of samples were tested. The results show that the optimum process parameters of decorated bamboo filament boards were 130 °C, 165 s and 2.00 MPa, and the surface bonding strength was 1.58 MPa. The relative error between the measured value and the predicted value was less than 5%. The contact angle of the bamboo filament after hot pressing was higher than without hot pressing. However, there was no correlation between wettability and the hot-pressing parameters. There was no effect on the change in bamboo surface color. This indicates that the temperature range selected in this study meets the requirements of surface color control in production.

Low-Carbon Flexible Job Shop Scheduling Problem Based on Deep Reinforcement Learning

As the focus on environmental sustainability sharpens, the significance of low-carbon manufacturing and energy conservation continues to rise. While traditional flexible job shop scheduling strategies are primarily concerned with minimizing completion times, they often overlook the energy consumption of machines. To address this gap, this paper introduces a novel solution utilizing deep reinforcement learning. The study begins by defining the Low-carbon Flexible Job Shop Scheduling problem (LC-FJSP) and constructing a disjunctive graph model. A sophisticated representation, based on the Markov Decision Process (MDP), incorporates a low-carbon graph attention network featuring multi-head attention modules and graph pooling techniques, aimed at boosting the model’s generalization capabilities. Additionally, Bayesian optimization is employed to enhance the solution refinement process, and the method is benchmarked against conventional models. The empirical results indicate that our algorithm markedly enhances scheduling efficiency by 5% to 12% and reduces carbon emissions by 3% to 8%. This work not only contributes new insights and methods to the realm of low-carbon manufacturing and green production but also underscores its considerable theoretical and practical implications.

Effect of addition of γ-poly glutamic acid on bacterial nanocellulose production under agitated culture conditions.

Bacterial nanocellulose (BNC), a natural polymer material, gained significant popularity among researchers and industry. It has great potential in areas, such as textile manufacturing, fiber-based paper, and packaging products, food industry, biomedical materials, and advanced functional bionanocomposites. The main current fermentation methods for BNC involved static culture, as the agitated culture methods had lower raw material conversion rates and resulted in non-uniform product formation. Currently, studies have shown that the production of BNC can be enhanced by incorporating specific additives into the culture medium. These additives included organic acids or polysaccharides. γ-Polyglutamic acid (γ-PGA), known for its high polymerization, excellent biodegradability, and environmental friendliness, has found extensive application in various industries including daily chemicals, medicine, food, and agriculture. In this particular study, 0.15g/L of γ-PGA was incorporated as a medium additive to cultivate BNC under agitated culture conditions of 120rpm and 30℃. The BNC production increased remarkably by 209% in the medium with 0.15g/L γ-PGA and initial pH of 5.0 compared to that in the standard medium, and BNC production increased by 7.3% in the medium with 0.06g/L γ-PGA. The addition of γ-PGA as a medium additive resulted in significant improvements in BNC production. Similarly, at initial pH levels of 4.0 and 6.0, the BNC production also increased by 39.3% and 102.3%, respectively. To assess the characteristics of the BNC products, scanning electron microscopy, Fourier transform infrared spectroscopy, and thermogravimetric analysis were used. The average diameter of BNC fibers, which was prepared from the medium adding 0.15g/L γ-PGA, was twice thicker than that of BNC fibers prepared from the control culture medium. That might be because that polyglutamic acid relieved the BNC synthesis from the shear stress from the agitation. This experiment held great significance as it explored the use of a novel medium additive, γ-PGA, to improve the production and the glucose conversion rate in BNC fermentation. And the BNC fibers became thicker, with better thermal stability, higher crystallinity, and higher degree of polymerization (DPv). These findings lay a solid foundation for future large-scale fermentation production of BNC using bioreactors.

Use of machine learning models in condition monitoring of abrasive belt in robotic arm grinding process

AbstractAlthough the aspects that affect the performance and the deterioration of abrasive belt grinding are known, wear prediction of abrasive belts in the robotic arm grinding process is still challenging. Massive wear of coarse grains on the belt surface has a serious impact on the integrity of the tool and it reduces the surface quality of the finished products. Conventional wear status monitoring strategies that use special tools result in the cessation of the manufacturing production process which sometimes takes a long time and is highly dependent on human capabilities. The erratic wear behavior of abrasive belts demands machining processes in the manufacturing industry to be equipped with intelligent decision-making methods. In this study, to maintain a uniform tool movement, an abrasive belt grinding is installed at the end-effector of a robotic arm to grind the surface of a mild steel workpiece. Simultaneously, accelerometers and force sensors are integrated into the system to record its vibration and forces in real-time. The vibration signal responses from the workpiece and the tool reflect the wear level of the grinding belt to monitor the tool’s condition. Intelligent monitoring of abrasive belt grinding conditions using several machine learning algorithms that include K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Decision Tree (DT) are investigated. The machine learning models with the optimized hyperparameters that produce the highest average test accuracy were found using the DT, Random Forest (RF), and XGBoost. Meanwhile, the lowest latency was obtained by DT and RF. A decision-tree-based classifier could be a promising model to tackle the problem of abrasive belt grinding prediction. The application of various algorithms will be a major focus of our research team in future research activities, investigating how we apply the selected methods in real-world industrial environments.

Sequential Optimal Trajectory Planning Scheme for Robotic Manipulators along Specified Path Based on Direct Collocation Method

Robotic manipulators play a pivotal role in modern intelligent manufacturing and unmanned production systems, often tasked with executing specific paths accurately. However, the input of the robotic manipulators is trajectory which is a path with time information. The resulting core technology is trajectory planning methods which are broadly classified into two categories: maximum velocity curve (MVC) methods and multiphase direct collocation (MPDC) methods. This paper concentrates on addressing challenges associated with the latter methods. In MPDC methods, the solving efficiency and accuracy are greatly influenced by the number of discretization nodes. When dealing with systems with complex dynamics, such as robotic manipulators, striking a balance between solving time and path discretization errors becomes crucial. We use a mesh refinement (MR) algorithm to find a suitable number of nodes under the premise of ensuring the path discretization error. So, the actual device can effectively implement the planned solutions. Nonetheless, the conventional application of the MR algorithm requires solving the original problem in each iteration; these processes are extremely time-consuming and may fail to solve when dealing with a complex dynamic system. As a result, we propose a sequential optimal trajectory planning scheme to solve the problem efficiently by dividing the original optimal control (OC) problem into two stages: path planning (PP) and trajectory planning (TP). In the PP stage, we employ a DC method based on arc length and an MR algorithm to identify key nodes along the specified path. This aims to minimize the approximation error introduced during discretization. In the TP stage, the identified key nodes serve as boundary conditions for an MPDC method based on time. This facilitates the generation of an optimal trajectory that maximizes motion performance, considering constant velocity in Cartesian space and dynamic constraints while keeping the path discretization error. Simulation and experiment are conducted with a six-axis robotic manipulator, ROCR6, and show significant potential for a wide range of applications in robotics.

Estimating Who Benefits from Productivity Growth: Local and Distant Effects of City Productivity Growth on Wages, Rents, and Inequality

Abstract We first estimate the direct effects on local workers’ earnings and housing costs from increases in local labor demand caused by gains in city-level manufacturing productivity. We find that local workers benefit from productivity growth, even after subtracting increases in housing costs. These gains are larger for local less educated workers, such that productivity growth reduces local inequality. We then propose and implement a new transparent method of estimating indirect effects of local productivity growth on earnings and housing costs of workers in other cities. We find that these general equilibrium effects are economically important and disproportionately benefit college-educated workers.

Forming a view: a human factors case study of augmented reality collaboration in assembly

Industry 4.0 technology is promoted as improving manufacturing flexibility, and competitiveness; though Australia has been slow to adopt. The Australian Navy shipbuilding program provides opportunities for accelerating technology adoption, revitalising manufacturing productivity and competitiveness. Adopting a sociotechnical systems lens, our research sought to identify usability, workload, and user experience of an augmented reality head-mounted display (AR-HMD) deployed to complete multiple work tasks in a workflow (electrical assembly, collaborative robot (cobot) mediated inspection, and remote troubleshooting using video call). Usability was rated ‘average’ (System Usability Scale mean = 69.8) and workload ‘acceptable’ (NASA Task Load Index mean = 25.8) for the AR-HMD alone, with usability of the integrated work system (IWS) rated ‘good’ (SUS mean = 79.2). Results suggest software interfaces, tracking, and gesturing methods for the AR-HMD require improvement. This trial shows the AR-HMD provides a versatile platform for integrating multiple digital technologies without hindering effectiveness of end-user performance, potentially benefiting productivity and quality.

Enhancing manufacturing productivity: A review of AI-Driven supply chain management optimization and ERP systems integration

This abstract delves into the realm of manufacturing productivity enhancement through the review of AI-driven supply chain management (SCM) optimization and Enterprise Resource Planning (ERP) systems integration. As industries strive for operational excellence, the convergence of artificial intelligence (AI) and supply chain management emerges as a transformative force in driving efficiency, agility, and competitiveness. Through a comprehensive analysis, this abstract examines the synergistic relationship between AI-driven SCM optimization and the integration of ERP systems, elucidating their collective impact on manufacturing productivity. AI-driven SCM optimization encompasses a spectrum of technologies and methodologies, including predictive analytics, machine learning, and autonomous decision-making systems, aimed at optimizing various facets of the supply chain, from demand forecasting and inventory management to production planning and logistics optimization. By harnessing the power of AI, manufacturers can enhance forecasting accuracy, reduce lead times, optimize inventory levels, and mitigate supply chain disruptions, thereby improving overall productivity and customer satisfaction. Integration of ERP systems plays a complementary role in manufacturing productivity enhancement by providing a centralized platform for data management, process automation, and cross-functional collaboration. Through seamless integration with AI-driven SCM optimization tools, ERP systems enable real-time data exchange, actionable insights, and end-to-end visibility across the supply chain, facilitating informed decision-making and agile response to dynamic market conditions. Drawing insights from case studies and industry examples, this abstract highlights best practices, challenges, and emerging trends in AI-driven SCM optimization and ERP systems integration. Strategies for successful implementation, including organizational readiness assessment, change management, and stakeholder engagement, are discussed to guide manufacturers in unlocking the full potential of these transformative technologies. In conclusion, the convergence of AI-driven SCM optimization and ERP systems integration offers a compelling pathway for enhancing manufacturing productivity, driving operational excellence, and sustaining competitive advantage in the digital era.. Keywords: Artificial Intelligence, Supply Chain Management, Enterprise Resource Planning, Manufacturing Productivity, AI Integration, Predictive Analytics.

Boosting business agility with additive digital molding: An Industry 5.0 approach to sustainable supply chains

This study explores the use of Additive Digital Molding, an Industry 5.0-driven approach, to improve business agility and sustainability within supply chains. By integrating digital reverse engineering, additive manufacturing, and plastic injection molding, this methodology streamlines product development processes, particularly for highly customized products in isolated environments. Additive Digital Molding provides cost-effective solutions for overcoming sourcing challenges, meeting customization requirements, and ensuring confidentiality. It also empowers SMEs and individuals to take greater control of their supply chains and foster entrepreneurial ventures. By enabling new business models such as direct manufacturing and home production, additive digital molding contributes to achieving the Sustainable Development Goals of the 2030 Agenda. This research highlights the transformative potential of Additive Digital Molding to drive innovation and sustainability across industries.

A MOGA and extended RPN based approach for heuristic key reliability characteristics analysis in manufacturing

The manufacturing process is the last opportunity to build an ideal design reliability index into a product. With the advancement of intelligent manufacturing technology, the concept of quality evolves from conformance to fitness for use, which emphasizes that reliability should be built into product with quality control. To effectively implement reliability assurance in the manufacturing process, it is necessary to accurately identify the vital few characteristics that are critical to reliability. Thus, a heuristic key reliability characteristic (KRC) analysis in manufacturing model fusing big quality data is proposed. First, on the basis of the fusion big quality data in manufacturing-by-manufacturing system Reliability-operational process Quality- output product Reliability (RQR) chain, a data driven KRC analysis model is proposed, and a reliability proactive control framework in manufacturing driven by KRC is expounded. Second, considering mass quality and reliability data, an effective KRC identification method based on data mining using multi-objectives genetic algorithm (MOGA) is established. Third, considering manufacturing data and product failure risk, an extended risk priority number (RPN) for KRC ranking is proposed. Finally, an example of an insulating base of subway locomotive is provided to verify the proposed approach.

Additive manufacturing as a resource-saving alternative in the manufacturing process of marine gearbox housing

ABSTRACT Marine gearbox housings are large components of several metres in length and tonnes of weight. The conventional manufacturing process is a time-consuming and elaborate process with the necessity to oversize the components from a load point of view. This results in a redundant amount of energy and resources for manufacturing. In contrast, wire arc additive manufacturing (WAAM) offers the possibility to realize a load- and weight-optimized design and provides an energy- and resource-efficient alternative in the manufacturing of marine gearbox housings. It has been shown that this additive manufacturing process provides the potential of 41% energy reduction and 36% material reduction compared to the iron cast manufacturing process. This is due to the modification of the load flow and the use of the hybrid manufacturing production as well as the possibility to include bionic structures in the gearbox design.

Role of Cultural Intelligence and Customer Involvement on Behavioral Loyalty Toward Manufactured Products in Nigeria

Evidence from the literature reveals that customer loyalty research is growing and is becoming more widely integrated into related disciplines. This study evaluates the role of sales representatives’ cultural intelligence (CI) in the behavioral loyalty (BL) of customers of SMEs in Nigeria, through the mechanism of customer involvement (CuI). Two sets of questionnaires were administered directly to the sales representatives, and indirectly to the customers, via the sales representatives. Three hundred and fifty copies of the sales representatives’ questionnaire were administered, based on the population of 65 manufacturing SMEs and an average of 5.4 copies per SME, while 384 copies of the customers’ questionnaire were sent to the unknown pool of customers. Data were col- lected from the two sets of respondents, which were analyzed through a variance-based structural equation modeling using Smart-PLS software. The results reveal a positive and significant relationship between cultural intelligence and behavioral loyalty, while the in- direct relationship reveals customer involvement does strongly mediate the relationship between the antecedent and the outcome. The findings imply that behavioral loyalty is engendered when sales representatives possess high cultural intelligence, and customers are strategically involved through consultations and product co-creation.

Mixotrophic cultivation of microalgae using agro-industrial waste: Tolerance level, scale up, perspectives and future use of biomass

Alternative culture media make large-scale microalgae biomass production more viable and cost-effective. The aim of this work was to study the addition of whey as an alternative source of organic nutrients in fed-batch mode for the cultivation of microalgae (Spirulina platensis, Chlorella homosphaera and Scenedesmus obliquus). The microalgae demonstrated acceptance of whey at low concentrations, causing the increasing in the maximum specific growth rates (μmax) and biomass concentrations ([X]max) compared with the cultivation without whey in standard medium (μmax of 0.168 d−1 versus 0.075 d−1, and [X]max of 2.274 g L−1 versus 0.970 g L−1, respectively, for S. platensis). Therefore, a cost savings in cultivation media of up to 32 %, 57 %, and 73 % were possible in the cultivation of S. obliquus, S. platensis, and C. homosphaera, respectively, considering the production of 1 kg of biomass. The addition of whey presented different effects in the levels of intracellular protein considering the species of microalgae: an increasing was observed in C. homosphaera (24.18 to 43.96 g/100 g of biomass) and a decreasing in S. platensis (73.75 to 44.06 g/100 g of biomass), comparing with the controls. Scale-up with a natural light source can affect microalgal productivity and biomass composition in a medium supplemented with whey, because of the influence of irradiation. Optimizing the large-scale production of microalgae using agro-industrial residues can lead to significant advancements in biomass production at reduced costs. These biomasses can be integrated into bioenergy production, biofertilizer manufacturing, and animal feed production. By utilizing waste as a nutritional source and optimizing cultivation processes and biomass generation, a closed-loop cycle is established, aligning with the principles of circular bioeconomy and enhancing the sustainability of the process.