Manufacturing Line Research Articles

Liquid API feeding in pharmaceutical HME: Novel options in solid dosage manufacturing

Hot melt extrusion (HME) is a common unit operation. It is broadly applicable in the pharmaceutical industry and can be implemented in a continuous manufacturing line. However, the conventional way of active pharmaceutical ingredient (API) feeding with a pre-blend consisting of a powdered API and a polymer does not allow the flexibility and agility to adjust the process parameters, which is generally an essential part of continuous manufacturing. In addition, this method of API feeding may result in the segregation of the individual powder components or agglomeration of highly cohesive materials, leading to an inhomogeneous API content in the extrudates, especially at low doses. In this study, the universal applicability of liquid side feeding in pharmaceutical HME was demonstrated using various APIs suspended or dissolved in water and fed as suspension or undersaturated, supersaturated, and highly concentrated solutions into anterior parts of the extruder. The extrudates were characterized in terms of their API content, residual moisture content, and solid-state of the API embedded in the polymer. The results show that a uniform API content without major deviations can be obtained via this method. Furthermore, the residual moisture content of the extrudates was low enough to have no significant influence on further processing of the final dosage form. In summary, this advanced way of feeding allows an accurate, flexible, and agile feeding of APIs, facilitating the production of personalized final dosage forms and a novel option to link the manufacturing of the drug substance and the drug product.

Spintronic terahertz polarization programmable system for information encoding

Terahertz (THz) wave as an important electromagnetic wave has been increasingly applied in wireless communication and imaging sensing technology. It has been an increasingly practical application. Ultra-high data rates in communication technology push toward a broadband, controllable polarization, and multiple transmission channels signal sources. Here, we propose a programmable polarization spintronics terahertz source (PP-STS), in which contains a controllable polarization spintronic THz emitter and a microcontroller unit. The PP-STS not only can generate THz waves with wide frequency bandwidth, but also has the function of switching the orthogonal linear polarization state of the generated THz waves flexibly. By coding the microcontroller unit, the designed PP-SPS can directly program the orthogonal linear polarization states as a series of information to transmit. To achieve the interconversions between digital codes and the THz polarization information, an optimum matching THz polarization beam splitter (THz-PBS) is also designed via simulations and experiments. The THz-PBS, as a decoder, can accurately split the transverse electric (TE) and transverse magnetic (TM) modes into two THz detectors. At last, a coding and decoding prototype system of spintronic THz polarization is built by integrating the PP-STS, the THz-PBS and two THz detectors. The send and receive information with high speed and accuracy are verified in this prototype system as well. We believe that our work takes THz applications for multiplexing communications one step further and sheds light on commercializing the PP-STS with exciting manufacturing lines for efficient and economical THz polarization technology.

Line balancing and task scheduling to minimise power peak of reconfigurable manufacturing systems

Energy efficiency has become a major concern for manufacturing systems, due to industry being the largest user of scarce, finite energy sources, and also to recent events which have pushed energy prices to alarming levels. In the present Industry 4.0 context, Reconfigurable Manufacturing Systems (RMS) are therefore one of the most promising manufacturing paradigm. In this paper, we investigate the suitability of one of the most common types of RMS, the Parallel-Serial manufacturing line with Crossover, to help minimise the peak of the electric power consumption. More specifically, the balancing of such a production line is studied, so as to integrate power peak minimisation from the design stage. Thus, we define the Parallel-Serial-with-Crossover Assembly Line Balancing Problem with Power Peak Minimization, a new combinatorial NP-hard problem. We also propose a suitable time-indexed Integer Linear Program that integrates balancing and scheduling decisions and a matheuristic algorithm designed to tackle large-size instances. Both approaches are tested on a wide set of instances. The computational results show that relevant power peak reductions can be achieved (33% on average), opening up promising perspectives from both algorithmic and managerial viewpoints.

Balancing and scheduling human-robot collaborated assembly lines with layout and objective consideration

The recent Industry 4.0 trend, followed by the technological advancement of collaborative robots, has urged many industries to shift towards new types of assembly lines with human-robot collaboration (HRC). This type of manufacturing line, in which human skill is supported by robot agility, demands an integrated balancing and scheduling of tasks and operators among the stations. This study attempts to deal with these joint problems in the straight and U-shaped assembly lines while considering different objectives, namely, the number of stations (Type-1), the cycle time (Type-2), and the cost of stations, operators, and robot energy consumption (Type-rw). The latter type often arises in the real world, where multiple types of humans and robots with different skills and energy levels can perform the assembly tasks collaboratively or in parallel at stations. Additionally, practical constraints, namely robot tool changes, zoning, and technological requirements, are considered in Type-rw. Accordingly, different mixed-integer linear programming (MILP) models for straight and U-shaped layouts are proposed with efficient lower and upper bounds for each objective. The computational results validate the efficiency of the proposed MILP model with bounded objectives while addressing an application case and different test problem sizes. In addition, the analysis of results shows that the U-shaped layout offers greater flexibility than the straight line, leading to more efficient solutions for JIT production, particularly in objective Type-2 followed by Type-rw and Type-1. Moreover, the U-shaped lines featuring a high HRC level can further enhance the achievement of desired objectives compared to the straight lines with no or limited HRC.

Modeling and Simulation of a Digital Twin of a Production System for Industry 4.0 with Work-in-Process Synchronization

One of the main problems of modern manufacturing systems is the increasing complexity related to modern Industry 4.0 technologies that are fundamentally changing manufacturing and logistics processes and operations. Industry 4.0 includes, e.g., flexible automation and robotization, which make complex manufacturing systems difficult to analyze. Some modeling and simulation methods are being used to solve industrial problems and can serve as an interface between the production level and management level. The new trend of the Digital Twin, creating simulation models as similar as possible to the real system, and a Digital Twin framework for a manufacturing line from the automotive industry, was considered. Simulation models typically start from the empty state and some warmup time is required to achieve the stable state. The Key Performance Indicators were also analyzed for the stable state. However, there are many stochastic parameters such as machinery failures, human errors, quality issues, etc., that make the real processes differ from simulated processes, and cause the instability of production throughput and changes in the Work in Process. To analyze the Work in Process in the model, initialization of the model with proper production data is required, as the Digital Twin uses data synchronization with the production database. In this paper, the digital model of a human-robot-operated manufacturing system with Work-in-Process data synchronization is analyzed, and the results of the statistical analysis of simulation experiments are presented. The obtained results show high variability of finished production, which is related to system instability due to random failures, especially when the system starts from an empty state. However, an increase in initial Work in Process results in better efficiency and stability for the whole system. The DT simulation of the manufacturing system can be very helpful, as it becomes a repository of knowledge about the real system and enables the analysis of its dynamics. However, for proper functionality, the model should include information about the current WIP state, which enables the start of the simulation with exactly the same number of queues as in the real system. The presented method can also be used in similar enterprises from other industries, especially for those with discrete processes or high WIP variability, and for further synchronization of other DT parameters.

Graph-enabled cognitive digital twins for causal inference in maintenance processes

The increasing complexity of industrial systems demands more effective and intelligent maintenance approaches to address manufacturing defects arising from faults in multiple asset modules. Traditional digital twin (DT) systems, however, face limitations in interoperability, knowledge sharing, and causal inference. As such, cognitive digital twins (CDTs) can add value by managing a collaborative web of interconnected systems, facilitating advanced cross-domain analysis and dynamic context considerations. This paper introduces a CDT system that leverages industrial knowledge graphs (iKGs) to support maintenance planning and operations. By employing a design structure matrix (DSM) to model dependencies and relationships, a semantic translation approach maps the knowledge into a graph-based representation for reasoning and analysis. An automatic solution generation mechanism, utilising graph sequencing with Louvain and PageRank algorithms, derives feasible solutions, which can be validated via simulation to minimise production disruption impacts. The CDT system can also identify potential disruptions in new product designs, thus enabling preventive actions to be taken. A case study featuring a print production manufacturing line illustrates the CDT system's capabilities in causal inference and solution explainability. The study concludes with a discussion of limitations and future directions, providing valuable guidelines for manufacturers aiming to enhance reactive and predictive maintenance strategies.

A novel interfacial polymerization assisted phase inversion process for the facile fabrication of ion-selective nanofiltration membranes

Interfacial polymerization (IP) and non-solvent induced phase inversion (NIPS) are the two most commonly used mainstream technologies to fabricate separation membranes. Taking their advantages, we developed a simple, facile and scalable method to integrate the IP and NIPS processes together for the fabrication of ion-selective nanofiltration membranes. Herein, amphiphilic polysulfone block hyperbranched polyglycerol (PSf-b-hPG) copolymer was synthesized and blended with polyethylene imide (PEI) to prepare a polymer casting solution in organic solvents. After casted into the liquid film of PSf-b-hPG and PEI blending solution, it was then immersed into a cross-linking solution with 0.2 wt.% trimethylchloride (TMC) in n-hexane for the generation of the polyamide layer first on top of the liquid film, and finally transferred into a water coagulation bath for the formation of the porous matrix of PSf-b-hPG membrane. The fabricated membrane exhibited a high water permeance of 12.57 L m-2h−1 bar−1 and a high separation factor of 25.08 towards Mg2+ and Li+ ions, which is superior to the perm-selectivity of the most reported nanofiltration membranes. This novel and facile process can be directly applied to the NIPS manufacturing line, eliminating a separate IP production line and holding immense potentials and advantages in scale-up production.

Design, Deployment, and Evaluation of an Industrial AIoT System for Quality Control at HP Factories

Enabled by the increasingly available embedded hardware accelerators, the capability of executing advanced machine learning models at the edge of the Internet of Things (IoT) triggers interest of applying Artificial Intelligence of Things (AIoT) systems for industrial applications. The in situ inference and decision made based on the sensor data allow the industrial system to address a variety of heterogeneous, local-area non-trivial problems in the last hop of the IoT networks. Such a scheme avoids the wireless bandwidth bottleneck and unreliability issues, as well as the cumbersome cloud. However, the literature still lacks presentations of industrial AIoT system developments that provide insights into the challenges and offer lessons for the relevant research and industry communities. In light of this, we present the design, deployment, and evaluation of an industrial AIoT system for improving the quality control of HP Inc.’s ink cartridge manufacturing lines. While our development has obtained promising results, we also discuss the lessons learned from the whole course of the work, which could be useful to the development of other industrial AIoT systems for quality control in manufacturing.

Regulatory Guidelines and Production Planning Strategies for Biosimilars in the USA

At present, biosimilar projects are ruling the pharma market. This article aims to show that project management is crucialin developing biosimilars. Bad project management will give a bad reputation to the organization, project cost overruns, projectschedule delays, demotivation of the project team, sustainability risk to the organization, and more over huge loss. It is the main reasonmost companies are spending more on the training of employees, particularly on project management. Biosimilars are biologicalproducts that are highly similar to a reference product despite modest changes in therapeutically inactive components. Biosimilarshave no clinically significant changes in product safety, purity, or potency compared to the reference product. With the imminentpatent expiration of certain important high-cost biologics, biosimilar manufacture is becoming increasingly profitable for firms.However, the introduction of biosimilars necessitates the adoption of critical new rules. Because of the inherent differences involvedwith the production methods used by different companies and even different sites within the same company, the existing standardsthat regulate the approval pathways for generic pharmaceuticals are insufficient for biosimilars. Biosimilars are similar to but notidentical to the original. Due to the exclusive nature of the manufacturing line and the techniques employed by these competitors,basic variances in methodology may exist between competing organizations. Even modest variations in the manufacturing process canresult in micro heterogeneity, characterized by minor differences in molecular structure, stability, production cell line behavior, growthconditions, vectors, and purification methods. For these reasons, developing biosimilars takes a lot of work.1 CBER regulatesbiosimilars in the US Market. The reference Medicine's Patent status and market potential should be considered when developingbiosimilars. The main objective of this article is to show that the project management strategies and regulatory approval process areequally important to enter biosimilars into the market. Concerning quality in the project management of biosimilar products, budget,deadlines, and scopes are major constraints. A Gantt chart has been enacted to represent a graphical illustration of a schedule. Ganttchart helps to plan, coordinate, and track specific tasks in a project.

A Finite State Automaton for Green Data Validation in a Real-World Smart Manufacturing Environment with Special Regard to Time-Outs and Overtaking

Since data are the gold of modern business, companies put a huge effort into collecting internal and external information, such as process, supply chain, or customer data. To leverage the full potential of gathered information, data have to be free of errors and corruptions. Thus, the impacts of data quality and data validation approaches become more and more relevant. At the same time, the impact of information and communication technologies has been increasing for several years. This leads to increasing energy consumption and the associated emission of climate-damaging gases such as carbon dioxide (CO2). Since these gases cause serious problems (e.g., climate change) and lead to climate targets not being met, it is a major goal for companies to become climate neutral. Our work focuses on quality aspects in smart manufacturing lines and presents a finite automaton to validate an incoming stream of manufacturing data. Through this process, we aim to achieve a sustainable use of manufacturing resources. In the course of this work, we aim to investigate possibilities to implement data validation in resource-saving ways. Our automaton enables the detection of errors in a continuous data stream and reports discrepancies directly. By making inconsistencies visible and annotating affected data sets, we are able to increase the overall data quality. Further, we build up a fast feedback loop, allowing us to quickly intervene and remove sources of interference. Through this fast feedback, we expect a lower consumption of material resources on the one hand because we can intervene in case of error and optimize our processes. On the other hand, our automaton decreases the immaterial resources needed, such as the required energy consumption for data validation, due to more efficient validation steps. We achieve the more efficient validation steps by the already-mentioned automaton structure. Furthermore, we reduce the response time through additional recognition of overtaking data records. In addition, we implement an improved check for complex inconsistencies. Our experimental results show that we are able to significantly reduce memory usage and thus decrease the energy consumption for our data validation task.

Research Note: Reducing food loss in the manufacturing process of chickens by reconditioning dropped raw poultry carcasses with peroxyacetic acid and sodium hypochlorite (chlorine) solution

Food waste and food loss has been a growing concern in the manufacturing industry with a gap between identifying the problem and implementing a solution. The manufacturing process of chicken is largely automated by conveyor belts and machines in which initial application of either peroxyacetic acid (PAA) or sodium hypochlorite (chlorine) solution is utilized to reduce the microbial load and prevent food borne illnesses on the chicken products as they are processed and packaged for distribution. However, during this automated process whole chickens can drop from the manufacturing line and become contaminated leading to the disposal and waste of the product. A solution to reduce food waste was to analyze a reconditioning procedure within the manufacturing process. The study evaluated the aerobic microbial growth on salvaged marinated deli raw whole chickens without giblets (WOGs) from conveyor belt loss reconditioned in either PAA or sodium hypochlorite (chlorine) solution to undropped chicken WOGs. Chicken rinsate and segmented samples were collected from each parameter and tested for microbial growth using Petrifilm aerobic plate count (APC) plates and converting results into log colony forming units (CFU). A difference (P < 0.05) was observed with the reconditioning of the WOGs in PAA (0.71 log10 CFU/mL) compared to the control (1.45 ± 0.26 log10 CFU/mL), for rinses. Of the segmented samples, the trussing strings displayed a significant decrease in APC counts for both chlorine (2.30 ± 0.49 log10 CFU/g) and PAA (2.3 ± 0.49 log10 CFU/g) reconditioning compared to the control (2.72 ± 0.39 log10 CFU/g). Reconditioning of salvaged deli chicken WOGs in chlorine or PAA is comparable to or better than the conventional process for the reduction of APC, it is an effective strategy to reintroduce dropped marinated deli chicken WOGs to the manufacturing line and can reduce food waste at a manufacturing level.

Optimal BESS Sizing for Industrial Facilities Participating in RTP DR

The predictability of their manufacturing lines allows industrial facilities to optimize their production scheduling and to participate in demand response (DR), in day-ahead, real-time pricing (RTP) electricity markets. Battery energy storage systems (BESSs) make the electrical demand of industrial facilities more flexible and increase their potential to benefit from DR. The BESS sizing problem, for industrial facilities participating in RTP DR, is complex due to the discreteness of their manufacturing lines and the stochastic nature of electricity pricing. In this paper, an approach to BESS sizing is proposed. Scenario extraction using k-means clustering is used to reduce the problem complexity, and the extracted scenarios are preprocessed to reduce the search space for the optimal size of the BESS. The steps involved in the proposed approach are demonstrated, in detail, through a case study that uses a generic model of an industrial unit. The results of the case study show the effectiveness and validity of the problem reduction techniques used and highlight the role of electricity storage in maximizing the profits of the industrial unit. Finally, a sensitivity analysis is carried out to illustrate the impact of the BESS installation cost on the results.

A machining error tracing method based on MEA-BP neural network for quality improvement of gear hubs

ABSTRACT The machining quality of workpieces is greatly influenced by the performance of an equipment. Furthermore, it is difficult to establish an error tracing model with high tracing accuracy using a mathematical method. In this study, the machining quality of gear hubs for an automobile synchronizer produced on an intelligent manufacturing line was evaluated. The main sources of machining errors were analyzed, and the machining error tracing model for the gear hub was established through a back propagation (BP) neural network. To improve the performance of the error tracing model, the weights and thresholds of the BP neural network were optimized using the mind evolutionary algorithm (MEA). The MEA-BP error tracing model was trained and tested using online measurement results and historical data of the production line. The results showed that the average tracing accuracy of the MEA-BP method was 97.4%, which was 12.1% higher than that of the BP method. The average running time of the MEA-BP was far less than that of a genetic algorithm (GA) improved BP method. These comparisons prove that the proposed MEA-BP error tracing method is both feasible and effective. The proposed method can improve the machining quality and error tracing in intelligent manufacturing applications.

Laser Triangulation Based In-Line Elastic Recovery Measurement for the Determination of Ribbon Solid Fraction in Roll Compaction

Process Analytical Technology (PAT) plays a crucial role in the design of today's manufacturing lines as continuous manufacturing becomes more important. Until now PAT tools to measure the ribbon solid fraction (SFribbon) in-line are not commonly used in roll compaction. The aim of this study was therefore to establish a new approach as PAT for in-line ribbon solid fraction determination. Different placebo formulations with different binders and one formulation containing active pharmaceutical ingredient were investigated using in-line laser triangulation measurement to detect the ribbon thickness after compaction. With this the ribbon elastic recovery was determined in-line (ERin−line) while the ribbons are attached to the roll surface. It was found that the ratio (ERratio) between the total elastic recovery and ERin−line is formulation specific and not influenced by any process parameters. This enables ERratio as prediction tool for SFribbon, if the solid fraction at gap (SFgap) width is known. SFgap was determined with ribbon mass flow measurement or based on the Midoux model, a simplified Johanson model, gaining two prediction models for SFribbon. Both models showed good agreement of the predicted SFribbon and the measured one.

A Simulation Model to Analyze Different Automation Scenarios in a Mixed-Assembly Manufacturing Line: Timber-Frame Prefabrication Industry

A Simulation Model to Analyze Different Automation Scenarios in a Mixed-Assembly Manufacturing Line: Timber-Frame Prefabrication Industry

Measurement and Simulation of Ultra-Low-Energy Ion-Solid Interaction Dynamics.

Ion implantation is a key capability for the semiconductor industry. As devices shrink, novel materials enter the manufacturing line, and quantum technologies transition to being more mainstream. Traditional implantation methods fall short in terms of energy, ion species, and positional precision. Here, we demonstrate 1 keV focused ion beam Au implantation into Si and validate the results via atom probe tomography. We show the Au implant depth at 1 keV is 0.8 nm and that identical results for low-energy ion implants can be achieved by either lowering the column voltage or decelerating ions using bias while maintaining a sub-micron beam focus. We compare our experimental results to static calculations using SRIM and dynamic calculations using binary collision approximation codes TRIDYN and IMSIL. A large discrepancy between the static and dynamic simulation is found, which is due to lattice enrichment with high-stopping-power Au and surface sputtering. Additionally, we demonstrate how model details are particularly important to the simulation of these low-energy heavy-ion implantations. Finally, we discuss how our results pave a way towards much lower implantation energies while maintaining high spatial resolution.

Comparison between Physics-Based Approaches and Neural Networks for the Energy Consumption Optimization of an Automotive Production Industrial Process

The automotive production sector plays a significant role in the energy consumption of all the industrial sphere, which currently represents approximately 38% of the total global energy use. Especially in production sites with several manufacturing lines working in parallel, the occurrence of failures and anomalies or sudden changes in the production volume may require a re-scheduling of the entire production process. In this regard, a digital twin of each phase of the process would give several indications about the new re-scheduled manufacture in terms of energy consumption and the control strategy to adopt. Therefore, the main goal of this paper is to propose different modeling approaches to a degreasing tank process, which is a preliminary phase at automotive production sites before the application of paint to car bodies. In detail, two different approaches have been developed: the first is a physics-based thermodynamic approach, which relies on the mass and energy balances of the system analyzed, and the second is machine learning-based, with the calibration of several artificial neural networks (ANNs). All the investigated approaches were assessed and compared, and it was determined that, for this application and with the data at our disposal, the thermodynamic approach has better prediction accuracy, with an overall mean absolute error (MAE) of 1.30 °C. Moreover, the model can be used to optimize the heat source policy of the tank, for which it has demonstrated, with historical data, an energy saving potentiality of up to 30%, and to simulate future scenarios in which, due to company constraints, a re-scheduling of the production of more work shifts is required.

Modeling of the impact of mechanical stress resulted from wafer probing and wire bonding on circuit under pad

Circuit Under Pad (CUP), also known as Bond Pad Over Active (BPOA), has been gaining popularity in the semiconductor industry. However, such a design concept amplifies the effect of stress-inducing processes, which are detrimental to the chip's electrical performance and mechanical reliability. The said processes are wire bonding and wafer probing, and they are unavoidable in the manufacturing of a semiconductor. Although the Finite Element Analysis (FEA) of these processes has been carried out in previous research, the focus was not on the polysilicon resistor or metallization layers in CUP, but on the bond pad only. On the other hand, wire bonding is carried out after wafer probing in the manufacturing line. However previous studies, either numerically or experimentally, investigated the processes separately. Specifically, those studies did not link the stress and deformation history of the wafer-CUP structure resulting from the wafer probing to the stress and deformation induced in the subsequent wire bonding process. Therefore, this project scoped both simulations in a continuous manner and using a CUP design as the chip's structure. ANSYS Mechanical was utilized to simulate the dynamic wafer probing and thermosonic wire bonding. The result showed that copper bonding exerted more stress compared to gold bonding. Meanwhile, the Cu/low-k chip is more sensitive to stress-inducing factors than the Al/SiO2 chip.

In Shortage: Understanding Global Antibiotic Supply Chains Through Pharmaceutical Trade Fairs

Many countries have reported supply shortages of antibiotics in recent years. The COVID-19 pandemic has sparked interesting discussions among government officials, public health practitioners, and scholars on how to maintain the security of the global pharmaceutical supply chain and how to decrease dependency on countries such as China. This article discusses the experiences and initial findings of tracing global pharmaceutical supply chains through pharmaceutical trade fairs and events at multiple locations. I argue that the reasons behind antibiotic supply shortages are multifold. Although the geographical concentration of the production of key raw materials and Active Pharmaceutical Ingredients (APIs) in China is considered the main reason for the unstable and disrupted supply chain, it is also important to recognize that the same forces that have driven Western pharmaceutical companies to shift some of their less profitable manufacturing lines to China are also challenging Chinese pharmaceutical companies to adjust and reorient their strategies. As such, antibiotics are facing a conundrum in which the governance of excessive use is complicated by a shortage problem that hampers access to essential drugs.

IoT-based milk-run routing for manufacturing system: an application case in an automotive company

The Internet of Things (IoT) provides new opportunities to improve manufacturing lines’ performance and in-plant logistic processes. The digital milk-run system represents the new frontier to optimize material handling strategies but is still not fully exploited to address material distribution depending on the time slots required by the manufacturing lines. Therefore, to fill this gap, this paper investigates the actual integration of the milk-run system with an IoT system. An analytical model for planning a dynamic routing strategy for tugger trains to deliver the materials to different workstations of a production line has been developed. The proposed model provides a materials distribution consistent with the time slot required by the manufacturing line, ensuring the minimisation of the total distance of the routes. An algorithm developed in Python is proposed to solve the NP-hard problem (nondeterministic polynomial time problem). The model has been applied to a real case of a worldwide automotive company to validate and prove its efficacy and efficiency. Indeed, compared to the current in-plant logistic strategy, (i) the inventory stock of each workstation was ensured, (ii) the average utilization rate of the tugger trains’ fleet was improved, and (iii) the daily path was minimized.