Manufacturing Control Research Articles

A Knowledge Graph-based knowledge representation for adaptive manufacturing control under mass personalization

Mass personalization is an achievable manufacturing paradigm, which requires flexible and responsible manufacturing operations in response to dynamic batch sizes of personalized products. A Self-Organizing Manufacturing Network (SOMN) has been proposed to achieve mass personalization. A crucial aspect of SOMN is adaptive manufacturing control, and the Knowledge Graph, a powerful tool, has been recognized as a promising solution to enhance manufacturing intelligence. However, the current Knowledge Graph research mainly focuses on the modeling and ontology definition of the manufacturing environment, but neglects the interaction between manufacturing resources, the dynamic features of the manufacturing environment, and the application of the Knowledge Graph towards adaptive manufacturing control. Therefore, this paper proposes a Knowledge Graph-based semantic representation for adaptive manufacturing control under dynamic manufacturing environments. The proposed approach develops the Knowledge Graph based on historical and real-time scheduling data. Based on the established Knowledge Graph, Multi-Agent Reinforcement Learning has been introduced as an illustrative example of achieving adaptive scheduling control.

Current status, trends, and challenges of continuous manufacturing technology for oral traditional Chinese medicine solid preparations

In recent years, continuous manufacturing technology has attracted considerable attention in the pharmaceutical industry. This technology is highly sought after for its significant advantages in cost reduction, increased efficiency, and improved productivity, making it a growing trend in the future of the pharmaceutical industry. Compared to traditional batch production methods, continuous manufacturing technology features real-time control and environmentally friendly intelligence, enabling pharmaceutical companies to produce drugs more efficiently. However, the adoption of continuous manufacturing technology has been slow in the field of traditional Chinese medicine(TCM) pharmaceuticals. On the one hand, there is insufficient research on continuous manufacturing equipment and technology that align with the characteristics of TCM preparations. On the other hand, the scarcity of talent with diverse expertise hampers its development. Therefore, in order to promote the modernization and upgrading of the TCM pharmaceutical industry, this article combined the current development status of the TCM industry to outline the development status and regulatory requirements of continuous manufacturing technology. At the same time, it analyzed the problems with existing TCM manufacturing models and explored the prospects and challenges of applying continuous manufacturing technology in the field of TCM pharmaceuticals. The analysis focused on continuous manufacturing control strategies, technical tools, and pharmaceutical equipment, aiming to provide targeted recommendations to drive the development of the TCM pharmaceutical industry.

Design and manufacture control system for water quality based on IoT technology for aquaculture in the Vietnam

In this paper, presents solutions to apply internet of things (IoT) technology in the field of high-tech agriculture and aquaculture in coastal areas of Vietnam, which is currently a new problem. The system includes an application on a smartphone; access parameters via the web, a computer and an IoT control circuit capable of automatically drawing electricity from the solar panel to the energy storage to serve as a source of power for devices (monitoring cabinets). The product has the function of monitoring: water environmental indicators in shrimp ponds (temperature, pH, dissolved oxygen (DO), salinity, redox index at the bottom of oxygen reduction potential (ORP)), on the website 24/24h, and controllable. Automatic control of pump system and DO generator with Inverter technology. The research results in this paper have brought high economic benefits with an automatic water quality control system to improve the productivity and quality of shrimp farming in practice for people in the aquaculture area in Bach Long commune, Giao Thuy district, Nam Dinh province, Vietnam.

Influences of random imperfection distribution on the compressive properties of interlocking block wall

Block imperfections exist inevitably owing to manufacturing and construction quality control. For dry-stacked interlocking block structures, imperfections result in small gaps randomly distributed between blocks, which affect the compressive strength of the wall. In this study, stochastic analysis is conducted to predict the compressive properties of interlocking block walls with spatially varying randomly distributed block imperfections. Monte-Carlo simulation is conducted in the analysis. The number of block imperfections is assumed to follow the Binominal distribution in massive block production process; the imperfection sizes are assumed to follow the truncated normal distribution. Based on these hypotheses, the damage development mechanism and load-path of interlocking block walls with different imperfection distributions are investigated. It is found that the compressive strength of walls containing blocks with mixed imperfection levels is lower; for walls with a higher number of imperfections, a larger coefficient of variation of imperfections leads to a significant decrease in compressive strength, while the seating effect at the initial stage of compression is reduced. The results provide a guidance to the quality control of mortar-less interlocking block structures.

The Importance of Logistics 4.0 within the Scope of Industry 4.0: Evaluation of Logistics 4.0 in an Enterprise in Terms of Sustainability

Industry 4.0 and Logistics 4.0 are highly scientifically significant terms. They are linked to the common trend of digitization, virtualization and networking of data and information. Logistics 4.0 is part of Industry 4.0 and will support the development of Industry 4.0. It is clear that there is no Industry 4.0 without Logistics 4.0. Industry 4.0 has impacted not only the industrial environment but all related sectors, including services and logistics, and the change is expected to continue. The logistics sector has an important role in the entire Supply chain of industrial processes and therefore changing needs in the industrial environment directly affect the logistics sector. This research provides a basis for examining the definitions and content of Industry 4.0 and Logistics 4.0 in detail. Due to the increasing supply and demand in Supply chains today, one of the most important tasks in future supply chain planning will be to provide flexibility while avoiding complexity and uncertainty. One of the most important prerequisites for supply chain flexibility is the development of network-based collaborations and the use of Industry 4.0 applications. Within the scope of this research, a literature review including the concepts of Sustainability, Industry 4.0, Logistics 4.0 and Supply Chain Management was conducted. Sample application studies on this subject were examined. Depending on the characteristics obtained and prepared as a result of the researches carried out depending on the purpose of the study, the development model offered by a company and the development model offered by white goods manufacturers are compared in terms of sustainability. In this context, suggestions are made in terms of Sustainable Logistics 4.0. In this study, the Analytic Hierarchy Process (AHP) method was applied in an integrated manner to the decision-making problem within the scope of sustainable logistics 4.0 through the criteria of Logistics 4.0 service offerings integrated with the dimensions known as sustainable indicators (economic, environmental and social) depending on the development of production, manufacturing, investment, value chain, strategic service, functional service and quality control for a company-enterprise operating in the Marmara region among the white goods manufacturers in Turkey.

The transition from batch to continuous manufacturing for tablet manufacturing performance comparison and control system review

The pharmaceutical industry is motivated to improve tablet productivity in order to adapt to the expanding drug market. One innovative way is the transition of the traditional batch tablet manufacturing process to a continuous process. This paper aims to review the performance of continuous tablet manufacturing processes. From the comparison of continuous to traditional batch processes, the continuous tablet manufacturing process demonstrates improvements in production flexibility, robustness, and ultimately process profitability. The continuous process is proven to mix drug particles with more evenly distributed size and less segregation resulting in tablets with better quality compared to the traditional batch process. The improvement on the continuous tablet manufacturing process requires robust control system for process automation to counteract process noise and disturbances in process feed. A well-tuned Proportional Integral Derivative (PID) control is sufficient for the process to produce a desirable product. Developing a new algorithm for model predictive control (MPC) and enhancing the control system with MPC-PID control can further improve the system performance. Overall, the transition from batch to continuous tablet manufacturing is supported by pharmaceutical companies as well as organizations like Food and Drug Administration (FDA).

Decoration of Bi2O3NPs-AgNPs-ErGO as a first electrochemical nanosensor for sensitive determination of nelarabine in pharmaceutical dosage form and human serum samples

In view of the unquestionable necessity for rapid and accurate analysis of drug molecules to monitor patient overdose, numerous nanosensor-based technologies have been developed in addition to quality control of pharmaceutical manufacture and drug administration. In this regard, sensitive detection of a potent anticancer agent, nelarabine (NEL) was examined at the bare and modified glassy carbon electrode (GCE) with the help of differential pulse (DP) and cyclic voltammetry (CV) techniques. A unique and highly effective nanosensor was developed using a combination of bismuth (III) oxide nanoparticles (Bi2O3NPs), silver nanoparticles (AgNPs), and electrochemically reduced graphene oxide (ErGO) onto the GCE surface. The modified Bi2O3NPs-AgNPs-ErGO/GCE was characterized by scanning electron microscopy (SEM), CV, and electrochemical impedance spectroscopy (EIS) investigations. Influences of various parameters viz., loading of Bi2O3NPs, AgNPs, and GO on the modified GCE, electrolyte pH (PBS 7.0), accumulation potential (–0.2 V), and time (60 s), and scan rate (50 mV s−1) were optimized for NEL response. An enhancement in the current responses toward the oxidation of NEL was observed with the Bi2O3NPs-AgNPs-ErGO/GCE compared to that noticed with bare GCE. The modified GCE affirmed high sensitivity, low limit of detection (LOD), excellent reproducibility, repeatability, and storage stability that clearly indicated the effective accuracy of the developed nanosensor. The linear behavior in the concentration range was found to be 0.02–1.0 µM, with LOD values of 0.003 nM in PBS 7.0 and 0.065 nM in serum samples. The electrochemical mechanism of NEL at the bare and modified GCEs were revealed as diffusion-controlled and adsorption-controlled mechanism processes, respectively. The linear calibration curves at both the bare and modified GCEs were noticed for increasing NEL concentrations, as constructed from the DPV measurements. Applications of the Bi2O3NPs-AgNPs-ErGO/GCE for NEL detection in pharmaceutical dosage form and human serum sample showed well-accepted recovery results of 98–99 %. The effect of interfering agents was checked on the selectivity of the developed method, and the modified electrode was found to be selective toward NEL in the presence of these interfering agents.

Invertible neural networks for real-time control of extrusion additive manufacturing

Invertible neural networks for real-time control of extrusion additive manufacturing

Prototype design education for cinematic learning mechanisms:aplications to the mechanisms to conrod crank with runner

The present investigation is related to the design, construction and manufacture of a didactic prototype of a rod-crank mechanism with a slider that allows to visualize and understand fundamental principles of its operation as well as its kinematics. Relating the traditional method of kinematics called Velocity Polygon with CAD design, additive manufacturing and automatic control. The objective of this project is to generate a controllable didactic prototype that allows us to visualize and understand fundamental concepts of the mechanism's operation as well as real-time information on the kinematics of its joints (nodes). Through the database generated by a instrumentation created in LabVIEW, movement simulation of the cad design and the velocity polygon graphic method, it was found that the speed of the sliding node coincides in the three methods, thus achieving a device that allows us to offer a more practical approach, in the development of technical skills, resolution of mechanism problems in undergraduate students in mechatronics, mechanics and Industrial

An Efficient Improved Harris Hawks Optimizer and Its Application to Form Deviation-Zone Evaluation.

Evaluation of the deviation zone based on discrete measured points is crucial for quality control in manufacturing and metrology. However, deviation-zone evaluation is a highly nonlinear problem that is difficult to solve using traditional numerical optimization methods. Swarm intelligence has many advantages in solving this problem: it produces gradient-free, high-quality solutions and is characterized by its ease of implementation. Therefore, this study applies an improved Harris hawks algorithm (HHO) to tackle the problem. The average fitness is applied to replace the random operator in the exploration phase to solve the problem of conflicting exploration strategies due to randomness. In addition, the salp swarm algorithm (SSA) with a nonlinear inertia weight is embedded into the HHO, such that the superior explorative ability of SSA can fill the gap in the exploration of HHO. Finally, the optimal solution is greedily selected between SSA-based individuals and HHO-based individuals. The effectiveness of the proposed improved HHO optimizer is checked through a comparison with other swarm intelligence methods in typical benchmark problems. Moreover, the experimental results of form deviation-zone evaluation on primitive geometries show that the improved method can accurately solve various form deviations, providing an effective general solution for primitive geometries in the manufacturing and metrology fields.

Determination of Valbenazine related substances by liquid chromatography method complying with the current regulatory guidelines. Method robustness evaluation by Design of Experiments software

AbstractA simple, specific, rapid, sensitive reversed‐phase high‐performance liquid chromatography method was developed and validated for the determination of Valbenazine, impurities, and its degradation products. The proposed high‐performance liquid chromatography method showed optimum separation in 60 min using potassium dihydrogen phosphate buffer and ACN in the ratio of 90:10 (V/V) as mobile phase‐A whereas ACN and methanol in the ratio of 60:40 (V/V) as mobile phase‐B. YMC‐Pack ODS‐AQ (150 × 4.6 mm, 3.0 μm), column used along with security guard cartridge C18, (4.0 × 3.0 mm) at 1.1 ml/min flow. The injection volume was 10 μl and detection was carried out at 285 nm. The drug was degraded under different stress conditions and the method was validated as per International Conference on Harmonization guidelines on the basis of accuracy, precision, linearity, and robustness. A degradation study indicates that Valbenazine is prone to oxidative degradation. The method was linear over the concentration range of limit of quantitation to 200% for impurities and drug substances. The method was found robust and was studied by the design of experiments. The proposed method is applicable for the determination of the stability of Valibenazine and was successfully used in the quality control of bulk drug manufacturing and pharmaceuticals.

Six SIGMA evaluation of 17 biochemistry parameters using bias calculated from internal quality control and external quality assurance data.

Six Sigma is a popular quality management system that enables continuous monitoring and improvement of analytical performance in the clinical laboratory. We aimed to calculate sigma metrics and quality goal index (QGI) for 17 biochemical analytes and compare the use of bias from internal quality control (IQC) and external quality assurance (EQA) data in the calculation of sigma metrics. This retrospective study was conducted in Marmara University Pendik E&R Hospital Biochemistry Laboratory. Sigma metrics calculation was performed as (TEa-bias)/CV). CV was calculated from IQC data from June 2018 - February 2019. EQA bias was calculated as the mean of % deviation from the peer group means in the last seven surveys, and IQC bias was calculated as (laboratory control result mean-manufacturer control mean)/ manufacturer control mean) x100. In parameters where sigma metrics were <5; QGI=bias/1.5 CV) score of <0.8 indicated imprecision, >1.2 pointed inaccuracy, and 0.8-1.2 showed both imprecision and inaccuracy. Creatine kinase (both levels), iron and magnesium (pathologic levels) showed an ideal performance with ≥6 sigma level for both bias determinations. Eight of the 17 parameters had different sigma levels when we compared sigma values calculated from EQA and IQC derived bias% while the rest were grouped at the same levels. Sigma metrics is a good quality tool to assess a laboratory's analytical performance and facilitate the comparison of the assay performances in the same manner across multiple systems. However, we might need to design a tight internal quality control protocol for analytes showing poor assay performance.

Valbenazine isomers and enantiomer determination by chiral normal phase liquid chromatography.

A novel, simple, specific, rapid, enantioselective normal phase chiral high-performance liquid chromatographic method with amylose-based Chiral Pak IG-3(250 × 4.6 mM) 3.0 μM column was developed and validated for separation and quantification of isomers and enantiomer of Valbenazine. The mobile phase composed of n-Heptane, isopropyl alcohol, dichloromethane, ethanol, and diethylamine in the ratio of 70:10:15:5:0.1 (V/V/V/VV) with a gradient flow rate was applied. The injection volume was 10 μl, and detection was carried out using a photodiode array detector at 282 nM. The column compartment was set at 35°C. The resolution between the enantiomer and isomers was found to be more than 2.0. The method was linear over the concentration range of limit of quantitation to 250% for isomers and enantiomers. The method was found to be robust with column temperature. The proposed chiral method is applicable for the determination of isomers and enantiomer of Valibenazine and was successfully used in the quality control of bulk drug manufacturing and pharmaceuticals.

Multi-spectral snapshot diffraction-based overlay metrology.

Diffraction-based overlay (DBO) metrology has been successfully introduced to deal with the tighter overlay control in modern semiconductor manufacturing. Moreover, DBO metrology typically needs to be performed at multiple wavelengths to achieve accurate and robust measurement in the presence of overlay target deformations. In this Letter, we outline a proposal for multi-spectral DBO metrology based on the linear relation between the overlay errors and the combinations of off-diagonal-block Mueller matrix elements ΔM = Mij - ( - 1)jMji (i = 1, 2; j = 3, 4) associated with the zeroth-order diffraction of overlay target gratings. We propose an approach that can realize snapshot and direct measurement of ΔM over a broad spectral range without any rotating or active polarization component. The simulation results demonstrate the capability of the proposed method for multi-spectral overlay metrology in a single shot.

Quality assurance and quality control for traditional manufacturing and additive manufacturing

This paper discusses the difference between quality assurance and quality control for additive manufacturing (AM) and traditional manufacturing (SM). It has been noted that the complex geometry and internal lattice structure of AM parts create problems with mechanical suitability. Multifaceted quality requirements and standards complicate the development of quality assurance strategies. Defects in AM parts (porosity, cracks, inclusions, voids, lumpiness and surface roughness) can adversely affect mechanical properties, and some problems such as volume mismatch, layer removal and unwanted internal surface finish cannot be detected by typical mechanical tests . It is also stated that SM continues to dominate AM in terms of quality, accuracy and reliability. The manufacturing industry and investors are always looking to improve technology to reduce costs, energy and expand their capabilities. Beginning in the 1960s, additive manufacturing has since then developed rapidly and continuously, discovering new methods to expand manufacturing capabilities and reinvent the bicycle.

The role of roughness parameters in grading the machined surface quality in Ti-alloys

Titanium alloys are extensively employed in machining to produce high-value components, where surface roughness is crucial for their functionality. In this study, the roughness of Ti6Al4V and Ti6Al7Nb turned surfaces was analysed. The goal was to compare the alloy’s roughness behaviour when machined under the same conditions. Therefore, hypothesis testing was performed to assess the equality of the expected values and population median. It was found that, for some parameters, the mean ( RzI, Rt) and median ( Rq, RzD, Pt) roughness was statistically different when changing alloys. However, for others, it did not present a significant difference, in terms of mean ( RmD, Rp) and median ( Ra, Rpm). Consequently, a relevant insight from this study was that the phenomenon interpretation was affected by roughness parameter selection. On the other hand, as expected, the feed rate played a critical role in roughness control. It was observed that Ti6Al7Nb had a smoother topography ( Ra was 0.15 ± 0.01 μm and 0.62 ± 0.04 μm) than Ti6Al4V (0.31 ± 0.02 μm and 0.67 ± 0.03 μm) at 0.05 and 0.15 mm/rev. Yet, at 0.1 mm/rev, this behaviour changed, as Ti6Al4V had a smoother surface ( Ra was 0.40 ± 0.02 μm) than Ti6Al7Nb ( Ra was 0.51 ± 0.03 μm). Finally, the multi-optimization indicated that a low feed rate (0.05 mm/rev) and medium (60 m/min) and high (90 m/min) cutting speed led to lower roughness and higher material removal rate for turning Ti6Al7Nb and Ti6Al7V, respectively. The insights from this study may have a practical application in the manufacturing and quality control of biomedical components.

Investigating a three-way interaction of manufacturer control, retail control and dependence on salesforce performance. Does the role of frontline employees matters ?

Dependence and retailer control on manufacturer's salesforce imply several challenges in retail channels. This research addresses how they affect the relationship between manufacturer control and salesforce performance due to the high level of salesforce investment. We conduct a moderated-moderation analysis.We investigate the moderating roles of retailer control and dependence on the relationship between manufacturer control and salesforce performance. To take into account the importance of the store frontline employee, we next studied the influence of his perception of the salesforce on the sales control systems in retailing. First, our results show that salesforce performance is influenced directly by manufacturer process control and, retail control and dependence moderate this relationship in two-way and three-way interactions. The salesforce subject to process control by manufacturer improves his performance under strong retailer outcome control while the manufacturer is in a low relative dependence. Second, employee perception of the salespeople mediates the link between manufacturer process and retailer outcome controls. We test our model in two studies and theoretically integrate retailer control, dependence, and employee perception of the salesforce, in the sales control literature. In the end, we provide practical implications to further improve salesforce performance and employee perception of the salespeople and future research suggestions.

Dysbiosis and Alzheimer's disease: role of probiotics, prebiotics and synbiotics.

Alzheimer's disease (AD) is a neurodegenerative disease characterized by dementia and the accumulation of amyloid beta in the brain. Recently, microbial dysbiosis has been identified as one of the major factors involved in the onset and progression of AD. Imbalance in gut microbiota is known to affect central nervous system (CNS) functions through the gut-brain axis and involves inflammatory, immune, neuroendocrine and metabolic pathways. An altered gut microbiome is known to affect the gut and BBB permeability, resulting in imbalance in levels of neurotransmitters and neuroactive peptides/factors. Restoration of levels of beneficial microorganisms in the gut has demonstrated promising effects in AD in pre-clinical and clinical studies. The current review enlists the important beneficial microbial species present in the gut, the effect of their metabolites on CNS, mechanisms involved in dysbiosis related to AD and the beneficial effects of probiotics on AD. It also highlights challenges involved in large-scale manufacturing and quality control of probiotic formulations.

Designing distributed decision-making authorities for smart factories – understanding the role of manufacturing network architecture

The availability of cyber-physical systems (CPS) in modern manufacturing networks provides a multitude of interesting opportunities from a manufacturing control perspective. Providing sensors, data gathering, local computation and communication capabilities modern CPS fulfil the technical requirements to act completely autonomously in a manufacturing network. While the distribution of decision-making authority to autonomous entities is feasible given such requirements, practice often sees the monopolisation of decision-making authority for centralised control. However, distributed production control approaches might be better suited given current manufacturing challenges, ranging from unreliable supply chains over highly volatile markets, to the demand for increasingly efficient and highly customisable production. In this article, we extend an existing scheduling complexity framework which enables practitioners and researchers alike to assess the aptitude of given manufacturing networks for both centralised and distributed control. In particular, we study the influence of a manufacturing network's topology ranging from assembly line to job shops on the aforementioned aptitude, with total production costs as objective. We utilise a multi-agent-based discrete-event simulation comparing an MILP-based centralised control approach and an autonomy based distributed control approach with weighted costs as decision function to evaluate this framework. Our results provide novel insights regarding the influence of manufacturing network topologies on the scheduling complexity of manufacturing networks.

Interconnected digital twins and the future of digital manufacturing: Insights from a Delphi study

AbstractDigital twins (DTs) are virtual representations of real‐world entities like production assets, processes, or products. They are updated at a defined fidelity and frequency along the entire life cycle from development and engineering over the production or implementation of a product or process until its usage stage. Interconnected digital twins (IDTs) are DTs shared and connected across organizations with the objective to create holistic simulation and decision models of an entire physical system. In this paper, we investigate how IDTs shape future digital manufacturing scenarios and impact innovation management. We present the results of a real‐time Delphi study, analyzing quantitative and qualitative estimates on a set of 24 projections, forecasting the future of digital manufacturing with a projection horizon towards 2030. Using this data and 22 additional use cases of IDTs in manufacturing companies, we present a baseline scenario where our Delphi panel reached a consensus, representing a likely future of digital manufacturing in 2030. By analyzing projections where our expert panels' evaluations vary widely, we identify key design decisions that may impact innovation management along the dimensions of variation, choice, and control in digital manufacturing. We explain how IDTs will impact external knowledge inflows, the emergence and governance of industrial data spaces, and the potential of data‐driven and AI‐enabled applications for prediction and regulation to drive better decision‐making and continuous innovation.