Production Line Research Articles

NON-ANALYTICAL DESIGN OF THE PI CONTROLLER OPTIMAL PARAMETERS FOR A CONTINUOUS LINE

The multiple motor drives are complex and coupled MIMO nonlinear systems. Due to the complexity of their mathematical models the development of effective control systems is quite complicated task. This paper presents the design of optimal control based on the quadratic optimality criterion for the central section of the continuous production line using soft computer methods when fuzzy model of the controlled system has been used. In this case, the proposed method demonstrates non-analytical design of the optimal parameters of PI controller with emphasis on minimal knowledge on the controlled system. The realized experimental measurements on a continuous line laboratory model confirmed the effectiveness and the good dynamic properties of the optimal continuous line controller and also its applicability to others MIMO nonlinear dynamic systems.

Adaptive synchronous sliding control for a robot manipulator based on neural networks and fuzzy logic

Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for robot hands is always an attractive topic in the research community. This is a challenging problem because robot manipulators are complex nonlinear systems and are often subject to fluctuations in loads and external disturbances. This article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller ensures that the positions of the joints track the desired trajectory, synchronize the errors, and significantly reduces chattering. First, the synchronous tracking errors and synchronous sliding surfaces are presented. Second, the synchronous tracking error dynamics are determined. Third, a robust adaptive control law is designed, the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy logic. The built algorithm ensures that the tracking and approximation errors are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results. Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is significantly reduced.

Quantitative Measurement and Evaluation of High-Resolution Ultrasonic Sound Fields using a Novel Automated Ultrasonic Immersion Scanner

Ultrasonic probes are an integral part of the automated ultrasonic non-destructive testing and evaluation (NDT&E) systems to detect and size the defects in a wide range of structural materials in production lines. A complete quantitative assessment of the performance characteristics of an application-specific UT probe is needed to be evaluated on the basis of the international standard DIN EN ISO 22232-2. This requirement of full assessment not only improves the quality assurance of the manufactured probes by evaluating the acceptance criteria but also provides the useful technical information to the end-user to optimize the automated ultrasonic testing on-site. In addition, the evaluation of probe characteristics should be carried periodically throughout their service life. In this work, we developed a novel high-precision ultrasonic immersion scanner to evaluate the full ultrasonic probe characteristics which include the squint angle measurement in three different reference planes (XY, XZ, and YZ), RF-signal and its frequency spectrum at water-steel interface, focal distance, focal range, focal width, focal length and also the angle of beam divergence in different planes of the sound field distribution. In the newly developed UT immersion scanner, for the first time, we successfully integrated the high-precision Hexapod with six axes to achieve a few micrometer (~15 µm) spatial-resolution in the measured sound field patterns and a good angular resolution (~0.05°) in the squint angle measurement. In addition, we used a verified UT instrument in accordance with the standard DIN EN ISO 22232-1 to obtain more accurate amplitude values (±0.5 dB) while scanning a test block. The automated scanning, data acquisition, evaluation, visualization and the automated test report generation are carried based on the standard DIN EN ISO 22232-2. The measured sound beam characteristics of both focused and non-focused application-specific ultrasonic probes with center frequencies ranging from 0.2 - 15 MHz using the pulse-echo technique on a 3 mm half-ball steel reflector are presented. In addition, the measured sound beams of an application-specific 5 MHz multi-element transmit-receive (TR) probe using the 3 mm flat-bottomed-hole (FBH) reflector in a steel reference plate are discussed. Finally, the quantitative analysis of the measured sound field results and the acceptance criteria in accordance with DIN EN ISO 22232-2 are discussed.

Building Breastfeeding Research Relations and Beyond: An Interview With Fiona Dykes.

Professor Fiona Dykes is Professor Emerita of Maternal and Infant Health at the University of Central Lancashire in the United Kingdom (UCLAN). Fiona has a particular interest in the global, sociocultural, and political influences upon infant and young child feeding practices; her methodological expertise is in ethnography and other qualitative research methods. She founded the Maternal and Infant Nutrition and Nurture Unit (MAINN) in 2000 which she led until she retired from her full-time professorship in 2020. Fiona established the associated MAINN Conference in 2007. The MAINN conference is a 3 day, international, peer reviewed event held bi-annually in the United Kingdom and, more recently, in alternate years overseas (Sydney, Australia; Falun, Dalarna, Sweden; and Florida, United States). The conference draws together key researchers in the field of infant and young child feeding from around the world. Fiona was a founding member of the journal Maternal and Child Nutrition. She is author of Breastfeeding in Hospital: Mothers, Midwives and the Production Line (Routledge) and co-author, with Dr Tanya Cassidy, of Banking on Milk: An Ethnography of Donor Human Milk Relations (Routledge). She is also joint editor of several books including Infant and Young Child Feeding: Challenges to Implementing a Global Strategy (Wiley-Blackwell) and Ethnographic Research in Maternal and Child Health (Routledge). This interview was conducted on April 20, 2023, by Dr. Tanya Cassidy, and is based on a verbatim transcription and edited for readability.TC = Tanya Cassidy; FD = Fiona Dykes.

Evaluation of Copper Sulfate Electroplating Solution and Film Using a New Method of Fluorescence Measurement

Abstract Control of plating bath conditions is an important issue on production lines. In this research, we developed a method for measuring and evaluating plating solutions using Janus Green B (JGB), a common additive, as a probe. It was discovered that when electricity was applied to a copper sulfate electroplating solution containing JGB as an additive, red fluorescence was generated. This is because the azo group of JGB is reductively cleaved by monovalent copper (Cu(I)) to produce a phenazine dye with red fluorescence. The fluorescence intensity depended on the energization current and time and was proportional to the concentration of JGB added. The fluorescence intensity indicates the rate of decrease in the concentration of JGB and can predict the deterioration of the quality of the plated film produced with the solution in a very early stage, and especially the occurrence of unevenness. Fluorescence is a simple and sensitive method of measurement that facilitates on-time and in-situ measurement and evaluation of plating baths at the production site and is a useful method of bath management.

Modular language product lines: concept, tool and analysis

AbstractModelling languages are intensively used in paradigms like model-driven engineering to automate all tasks of the development process. These languages may have variants, in which case the need arises to deal with language families rather than with individual languages. However, specifying the syntax and semantics of each language variant separately in an enumerative way is costly, hinders reuse across variants, and may yield inconsistent semantics between variants. Hence, we propose a novel, modular and compositional approach to describing product lines of modelling languages. It enables the incremental definition of language families by means of modules comprising meta-model fragments, graph transformation rules, and rule extensions. Language variants are configured by selecting the desired modules, which entails the composition of a language meta-model and a set of rules defining its semantics. This paper describes: a theory for checking well-formedness, instantiability, and consistent semantics of all languages within the family; an implementation as an Eclipse plugin; and an evaluation reporting drastic specification size and analysis time reduction in comparison to an enumerative approach.

Active Learning in Feature Extraction for Glass-in-Glass Detection

In the food industry, ensuring product quality is crucial due to potential hazards to consumers. Though metallic contaminants are easily detected, identifying non-metallic ones like wood, plastic, or glass remains challenging and poses health risks. X-ray-based quality control systems offer deeper product inspection than RGB cameras, making them suitable for detecting various contaminants. However, acquiring sufficient defective samples for classification is costly and time-consuming. To address this, we propose an anomaly detection system requiring only non-defective samples, automatically classifying anything not recognized as good as defective. Our system, employing active learning on X-ray images, efficiently detects defects like glass fragments in food products. By fine tuning a feature extractor and autoencoder based on non-defective samples, our method improves classification accuracy while minimizing the need for manual intervention over time. The system achieves a 97.4% detection rate for foreign glass bodies in glass jars, offering a fast and effective solution for real-time quality control on production lines.

Research on Product Positioning of New Energy Vehicles - Taking Tesla and Build Your Dream as Examples

This study focuses on the product positioning strategy of new energy vehicles, taking Tesla and Build Your Dream as examples. As a global leading electric vehicle manufacturer, Build Your Dream is committed to technological innovation and has launched a variety of new energy vehicle models, including pure electric vehicles, hybrid vehicles, and plug-in hybrid vehicles. Its product line covers multiple market segments, ranging from small city electric vehicles to large luxury electric vehicles, as well as special-purpose vehicles and commercial vehicles. In contrast, Tesla adopts a development model of first launching high-end models and then gradually expanding into the low-end market. From now on, Tesla's export volume is greater than Build Your Dream's, and it occupies a larger share of the global market. In terms of price, sales volume, sales channels, sales positioning, marketing strategies, imports, and exports, the two companies differ. Generally speaking, Tesla focuses on brand building and technological innovation, while Build Your Dream pays more attention to product cost-effectiveness and market share.

Flexible and robust detection for assembly automation with YOLOv5: a case study on HMLV manufacturing line

AbstractAutomating assembly processes in High-Mix, Low Volume (HMLV) manufacturing remains challenging, especially for Small and Medium-sized Enterprises (SMEs). Consequently, many companies still rely on a significant amount of manual operations with an overall low degree of automation. The emergence of artificial intelligence-based algorithms offers potential solutions, enabling assembly automation compatible with multiple products and maintaining overall production flexibility. This paper investigates the application of the YOLO (You Only Look Once) object detection algorithm in an HMLV production line within an SME. The performance of the algorithm was tested for different cases, namely, (a) on different products having similar product features, (b) on completely new products, and (c) under different lighting conditions. The algorithm achieved precision and recall greater than 98$$\%$$ % and mAP50:95 greater than 97$$\%$$ % .

Investigating the relationship between body structure status, work activity type, and the prevalence of musculoskeletal disorders among detergent industry workers.

The physical condition of workers' body structure and assigned duties, can contribute to the prevalence of musculoskeletal disorders. This study aimed to investigate the relationship between body structure status, type of work activity, and the prevalence of musculoskeletal disorders among workers in the detergent industry. This cross-sectional study involved 148 industrial workers selected based on inclusion criteria and their medical checkup records. Data collection for the study included a demographic information questionnaire, a body map questionnaire, and an assessment of the workers' musculoskeletal system conducted by three physiotherapists simultaneously. 54.1% of the participants had a total body structure score classified as poor or fair. The neck region showed the highest prevalence of musculoskeletal disorders (51.4%), followed by the lower back region (35.1%). Significant associations were found between abnormalities in the upper and middle limbs of the body and the prevalence of pain in the right shoulder region (Fisher/F = 9.29, P≤0.05) as well as the intermediate back region (F = 10.28, P≤0.01). Office workers experienced a higher prevalence of neck pain than workers in the product line and technical roles, with a statistically significant Odds Ratio (OR) ranging between 2.7 and 6.6 times. Conversely, industrial workers who operate powered machinery showed a higher prevalence of pain in the left shoulder (OR = 3.93) and left foot (OR = 4.07). Meanwhile, workers involved in loading and unloading tasks had a higher prevalence of pain in the middle back (OR = 3.61) and right foot (OR = 4.5) compared to office workers. The prevalence of pain in the right shoulder and middle back may be due to abnormalities in the upper and intermediate body structure. Production line workers reported a higher prevalence of pain in the left shoulder, middle back, and foot compared to office workers.

A systematic reliability-centred maintenance framework with fuzzy computational integration – a case study of manufacturing process machinery

PurposeThis study presents a systematic framework for maintenance strategy development of manufacturing process machinery. The framework is developed based on the reliability-centred maintenance (RCM) approach to minimise the high downtime of a production line, thus increasing its reliability and availability. A case study of a production line from the ghee and soap manufacturing industry in Taiz, Yemen, is presented for framework validation purposes. The framework provides a systematic process to identify the critical system(s) and guide further investigation for functional significant items (FSIs) based on quantitative and qualitative analyses before recommending appropriate maintenance strategies and specific tasks.Design/methodology/approachThe proposed framework integrates conventional RCM procedure with the fuzzy computational process to improve FSIs criticality estimation, which is the main part of failure mode effect criticality analysis (FMECA) applications. The framework consists of four main implementation stages: identification of the critical system(s), technical analysis, Fuzzy-FMECA application for FSIs criticality estimation and maintenance strategy selection. Each stage has its objective(s) and related scientific techniques that are applied to systematically guide the framework implementation.FindingsThe proposed framework validation is summarised as follows. The first stage results demonstrate that the seaming system (top and bottom systems) caused 50% of the total production line downtime, indicating it is a critical system that requires further analysis. The outcomes of the second stage provide significant technical information on the subject (seaming system), helping team members to identify and understand the structure and functional complexities of the seaming system. This stage also provides a better understanding of how the seaming system functions and how it can fail. In stage 3, the application of FMECA with the fuzzy computation integration process presents a systematic way to analyse the failure mode, effect and cause of items (components of the seaming system). This stage also includes items’ criticality estimation and ranking assessment. Finally, stage four guides team members in recommending the appropriate countermeasures (maintenance strategies and task selection) based on their priority level.Originality/valueThis paper proposes an original maintenance strategies development framework based on the RCM approach for production system equipment. Specifically, it considers a fuzzy computational process based on the Gaussian function in the third stage of the proposed framework. Adopting the fuzzy computational process improves the risk priority number (RPN) estimation, resulting in better criticality ranking determination. Another significant contribution is introducing an extended item criticality ranking assessment process to provide maximum levels of criticality item ranking. Finally, the proposed RCM framework also provides detailed guidance on maintenance strategy selection based on criticality levels, unique functionality and failure characteristics of each FSI.

Future of Pharmaceutical Industry: Role of Artificial Intelligence, Automation, and Robotics

Background The future of smart factories and pharmaceutical industries has evolved significantly since the 19th century. Computers have been used in the pharmaceutical field since the 1980s with the emergence of artificial intelligence (AI). In addition, automation and robotics are used in the pharmaceutical industry to improve the efficiency of pharmaceutical development and production. Purpose The present review article covers the future roles of AI, automation and robotics in pharmaceutical industries. Methods The current review article employed a comprehensive search strategy across relevant databases, including PubMed, Scopus and Web of Science, utilizing keywords such as AI, Automation, Robotics and Pharmaceutical Industries. The articles considered a focus on recent advancements and emerging trends in the intersection of AI, automation and robotics within pharmaceutical sectors. Results AI, incorporating predictive machine learning and reasoning techniques, aids in the preclinical identification of molecules and forecasting potential lead compounds before conducting clinical trials. automation offers significant benefits in monitoring and predictive maintenance of production lines, power distribution and control machines. Robotic process automation improves efficiency by connecting computer terminals to handle various manufacturing process elements. Conclusion Artificial intelligence, automation and Robotics have sparked innovations in the healthcare business, benefiting the global ecosystem and healthcare delivery. Incorporating these advanced tools in pharmaceutical industries from raw material selection to final product development could improve the quality and safety of the pharmaceutical products and reduce the time and cost.

Zeaxanthin dipalmitate-enriched wolfberry extract improves vision in a mouse model of photoreceptor degeneration.

Zeaxanthin dipalmitate (ZD) is a chemical extracted from wolfberry that protects degenerated photoreceptors in mouse retina. However, the pure ZD is expensive and hard to produce. In this study, we developed a method to enrich ZD from wolfberry on a production line and examined whether it may also protect the degenerated mouse retina. The ZD-enriched wolfberry extract (ZDE) was extracted from wolfberry by organic solvent method, and the concentration of ZD was identified by HPLC. The adult C57BL/6 mice were treated with ZDE or solvent by daily gavage for 2 weeks, at the end of the first week the animals were intraperitoneally injected with N-methyl-N-nitrosourea to induce photoreceptor degeneration. Then optomotor, electroretinogram, and immunostaining were used to test the visual behavior, retinal light responses, and structure. The final ZDE product contained ~30mg/g ZD, which was over 9 times higher than that from the dry fruit of wolfberry. Feeding degenerated mice with ZDE significantly improved the survival of photoreceptors, enhanced the retinal light responses and the visual acuity. Therefore, our ZDE product successfully alleviated retinal morphological and functional degeneration in mouse retina, which may provide a basis for further animal studies for possible applying ZDE as a supplement to treat degenerated photoreceptor in the clinic.

From Deterministic to Data-Driven: AI and Machine Learning for Next-Generation Production Line Optimization

From Deterministic to Data-Driven: AI and Machine Learning for Next-Generation Production Line Optimization

Crack Catcher AI – Enabling smart fracture mechanics approaches for damage control of thin silicon cells or wafers

Silicon has been one among a few key technologically important materials especially in our modern world. Silicon, especially in its current most useful forms (thin layers), is a brittle material by nature. Thin silicon cells crack easily during manufacturing assembly. Every crack is a defect in any PV manufacturing lines, and if let to grow/propagate it will lead to reliability and quality issues with time. Nevertheless, the global silicon solar PV industry is aggressively reducing the cost of solar PV systems through cutting down the thickness of silicon solar cells. Cell cracks immediately lower PV module efficiency and can lead to premature aging of the entire module. The “Crack Catcher AI” was our research joint collaboration's entry in the Department of Energy (DOE)'s American-Made Solar Innovation competition in 2022 (Round 6) which later was selected as the national semifinalists and won an award announced by DOE in December 2022. It uses smart stress sensing and smart fracture prediction approaches utilizing fundamental fracture mechanics and big data analytics to reduce crack defects and enhance long-term durability/reliability of PV products/devices. Smart Stress Sensing uses a novel laser-based curvature methodology for fast in-line stress metrology in manufacturing lines. Smart Fracture Prediction uses Artificial Intelligence (AI) for defect control metrology and yield enhancement in PV production lines. It is based on monocrystalline silicon used in solar PV industries, but all scientific principles utilized and technological innovations developed in this article would be applicable as well for single crystal silicon semiconductor wafers.

Analysis of sales demand and production capacity to determine the need for direct labour

This study consists of meeting the sales demand using the existing production capacity in an electronic goods industry and thus determining the amount of direct labor (MOD) necessary to meet the needs of customers. Since obtaining the correct number of operators on a production line is extremely important in the pursuit of productivity and cost reduction. Therefore, using concepts such as takt time, cycle time and line efficiency and other information related to the product or products that will be assembled on a production line, it will be possible to quickly and assertively determine the number of operators that must serve directly the client's demand, thus guaranteeing productive efficiency and the correct application of labor resources using as assumptions the capacity and equipment already existing in the production line. Thus, based on the company's sales projection, it will be possible to determine month by month the number of operators, number of shifts and the appropriate volume to be produced to meet the requested demand.

Blockchain design for optimal joint production and maintenance over multiple periods for oil-filling production lines

Joint maintenance and production planning result in enhanced system efficiency and a significant reduction in expensive costs. Therefore, this research proposed an integrated blockchain design of two optimization models for the joint scheduling and sequencing of production orders and maintenance jobs over multiple periods with probabilistic and stochastic parameters for the oil-filling process. In the scheduling model, the objective function was to minimize total production and maintenance costs. Further, the sequencing model aimed to minimize the total overtime cost and the sum start times of jobs and orders. The proposed models were implemented on five production lines of five-liter lube oil bottles. Results showed that the developed optimization models effectively achieved concurrent production and maintenance planning at minimal total cost. In practice, this results in effective resource utilization, and saving costly production and maintenance costs. In conclusion, the developed blockchain design is valuable in developing, managing, and controlling optimal joint production and maintenance planning of oil-filling production lines.

A step closer to sustainable CO2 conversion: Limonene carbonate production driven by ionic liquids

This work aims to advance the biocarbonate concept by developing the first process to produce limonene carbonate based on ionic liquids (ILs). Conventional petroleum-derived cyclic carbonates are recognized products partially composed of CO2 with the main drawback of the high energy consumption that imposes epoxide production. In contrast, natural epoxidable compounds, such as terpenes, stand out in the literature as a more sustainable open research line to enable CO2 conversion processes with better sustainable indicators. Limonene, an abundant natural compound, is identified as a key terpene in the aforementioned discussion. Ammonium-based ILs are experimentally selected and optimized, after covering massive anion and cation substituent tuning, achieving high selectivity and competitive yields to limonene carbonate. The reaction-extraction platform concept, which recovers the catalyst by liquid-liquid extraction, is envisioned and developed using a rational experimental-computational approach. The work concludes with a novel process to effectively produce limonene carbonate by using an IL catalyst and water as an extracting solvent. Ultimately, taking advance of the process simulation, a CO2 emissions assessment was conducted. Using renewable raw materials enhances the sustainability of CO2 conversion to cyclic carbonate, reducing global warming impact compared to fossil-based epoxides. However, limonene extraction and epoxidation have a tangible impact on the overall result of CO2 balance, turning efforts outside battery limits of the CO2 conversion process in the search of a limonene carbonate production line with negative neat CO2 emissions that may change the paradigm in the fixation of CO2 through cycloaddition reactions.

Speculative computing for AAFM solutions in large-scale product configurations.

Parallel computing is a current algorithmic approach to looking for efficient solutions; that is, to define a set of processes in charge of performing at the same time the same task. Advances in hardware permit the massification of accessibility to and applications of parallel computing. Nonetheless, some algorithms include steps that require or depend on the results of other steps that cannot be parallelized. Speculative computing allows parallelizing those tasks and reviewing different execution flows, which can involve executing invalid steps. Speculative computing solutions should reduce those invalid flows. Product configuration refers to selecting features from a set of available options respecting some configuration constraints; a not complex task for small configurations and models, but a complex one for large-scale scenarios. This article exemplifies a videogame product line feature model and a few configurations, valid and non-valid, respectively. Configuring products of large-scale feature models is a complex and time-demanding task requiring algorithmic solutions. Hence, parallel solutions are highly desired to assist the feature model product configuration tasks. Existing solutions follow a sequential computing approach and include steps that depend on others that cannot be parallelized at all, where the speculative computing approach is necessary. This article describes traditional sequential solutions for conflict detection and diagnosis, two relevant tasks in the automated analysis of feature models, and how to define their speculative parallel version, highlighting their computing improvements. Given the current parallel computing world, we remark on the advantages and current applicability of speculative computing for producing faster algorithmic solutions.

Motor online novelty detection scheme based on one-class hyperdisk

Online novelty detection is of great importance in the series production of motors. This study developed an online novelty detection scheme for motors based on a one-class hyperdisk (OCHD) model. In the OCHD approach, the decision boundary is estimated using a hyperdisk (HD), which is derived from the training sample set. The HD model addresses the underestimation issue commonly associated with convex-hull-based methods by providing a more accurate estimation of the class region. Furthermore, an optimal separating hyperplane is constructed at the nearest point on the HD by solving a quadratically constrained quadratic program problem. Statistical features refined by the Laplacian score are employed in the proposed novelty detection scheme. This study introduces an online novelty detection scheme for assessing motor quality in actual series production. The test results from the offline experiment demonstrate the superiority of the OCHD method. Datasets collected at the end of the production line were evaluated using the proposed novelty detection scheme. The inspection results for motor components confirm that the proposed method effectively identifies faulty motors during the series production process.