Production Line Research Articles

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.

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.

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.

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.

Optimization of NMC cathode inks for cost-effective and eco-friendly screen-printed lithium-ion batteries

Li-ion batteries are life-saving energy storage technology. However, the battery manufacturing process still needs complicated and expensive production lines because of the sensitive nature of active materials towards the open atmosphere. Additionally, employing hazardous N-Metil-2-Pirrolidon (NMP) solvents, which are also restricted by the World Health Organization for electrode preparation, contributes to unsafe working conditions. In this study, water-based LiNi0.8Mn0.1Co0.1O2 (NMC811) cathode inks were produced and printed by the screen-printing method, which is widely used in sensors and electronics fabrication due to its potential practical applications and low-cost production possibilities. Despite their suitability for operation at high voltage ranges, NMC811 cathodes typically exhibit rapid capacity drops over time because of side reactions and Cathode-Electrolyte Interphase (CEI) thickening. In this study, to overcome the capacity drop issue and make it possible to produce water-based cathode inks, NMC811 particles were encapsulated with reduced graphene oxide (rGO) via a hydrothermal method. Water-based Carboxymethyl cellulose (CMC) - Polyethylene oxide (PEO) binders are compared with conventional Polyvinylidene fluoride (PVDF)-NMP binders and exhibited promising electrochemical stability of 75 % after 50 cycles within a high voltage range of 2.8–4.6 V. Therefore, the water-based novel cathode ink presented in this study is not only well-suited for advancing the development of printed batteries but also stride towards more eco-friendly processes thanks to replacing the toxic NMP with water. Moreover, battery printing technologies have also a strategic importance by reducing the investment cost of battery-producing.

Private label partnerships with retailers: a risk or opportunity for producers?

PurposeThis study identifies benefits and challenges of PL partnerships, and recommendations to improve the PL partnership process.Design/methodology/approachQualitative data was collected via semi-structured interviews (n = 8) with Irish PL retail buyers (n = 4) and producers (n = 4). Data was coded and thematically analysed.FindingsThree key themes were identified. Theme 1 provides an overview of the benefits of PL partnerships for producers (e.g. volume driven orders, increased efficiencies) and for retailers (e.g. unique products, meeting consumer demand). Theme 2 presents challenges of PL partnerships specific to small and large producers (e.g. small producers may need significant investment to upgrade facilities, while larger producers may require significant volume to justify adaptation of production lines). Challenges common to both (e.g. risks related to short-term contracts, concerns about brand identity) are also discussed. Theme 3 summarised recommendations for successful PL partnerships generally (e.g. setting clear expectations and goals, building rapport and trust), and recommendations specific to producers and buyers specifically (e.g. producers should diversify customers to reduce risk, and retailers should communicate needs and direction).Originality/valueThere is currently limited research on PL partnerships between producers and retailers. This study addresses this gap by identifying key aspects for producers to consider when entering PL partnerships and key aspects for retailers to be aware of to help improve the attractiveness and success of these partnerships.

An Immersive Digital Twin Applied to a Manufacturing Execution System for the Monitoring and Control of Industry 4.0 Processes

The present research proposes the implementation of an architecture for industrial process monitoring and control for a manufacturing execution system (MES) using an immersive digital twin (DT). For the design of the proposal, cyber–physical systems (CPS), MES, robotics, the Internet of Things, augmented reality, virtual reality, and open platform communication-unified architecture (OPC UA) communication protocols were used to integrate these technologies and enhance the functionalities of the DT by providing greater performance. The proposed work is implemented in an Industry 4.0 laboratory that is composed of Festo Cyber–Physical Factory and CP-Lab stations. The implementation of the architecture is based on ISO 23247, where the following requirements were considered for the design of DTs: (1) observable attributes and 3D design and visualization of all physical production lines in all of their stages, (2) a communication entity through the OPC UA protocol for the collection of state changes of manufacturing elements, (3) a DT entity where digital models are modeled and updated based on the collected data, and (4) user entities through the use of AR and VR to make manufacturing more efficient. The experimental results showed that the architecture enables interoperability between different platforms and control subsystems. It allows for the detection and diagnosis of problems during the execution of the production line; in addition, the high-fidelity simulation and AR and VR environments provided by the DT with data obtained in real time can improve the accuracy and efficiency of manufacturing through a more detailed analysis of the process, providing advantages such as interactive creation for customized products and continuous innovation.

The Role of Business Management in the Innovative Development of the Cultural Tourism and Education Industry

As China's comprehensive strength continues to grow, along with the rapid development of information technology, new opportunities have emerged for the development of the cultural tourism and education industry. With an increasing demand for cultural tourism and lifelong learning, this industry is facing unprecedented development opportunities. The market size is continuously expanding, and the range of cultural tourism and education products and services is becoming more diverse, meeting the needs of various consumer groups. Innovation has become mainstream; companies and institutions within the industry are constantly exploring new development models, which promote the transformation and upgrading of the cultural tourism and education industry. Increased policy support has provided a solid guarantee for the innovative development of this industry. Thus, in the process of formulating operational strategies, it is essential to first conduct an in-depth market analysis to understand consumer needs, competitive situations, and industry trends. Subsequently, product strategies that align with market laws should be developed, including product positioning, unique feature creation, and the enhancement of product lines. Simultaneously, a reasonable pricing strategy must be established to ensure that product pricing is competitive while achieving the profit goals of the enterprise. Through the formulation and implementation of these strategies, business management can help the cultural tourism and education industry achieve sustainable development.

VENDOR MANAGEMENT IN FACTORIES

PROCEDURE FOR SUPPLIER DEVELOPMENT The Vendor Development Process is carried out by the Purchase Department in close consultation with User Three Department 1) R&D 2) Material Testing 3) QA Department. First Step: - VENDOR REGISTRATION • Any vendor i.e., manufacturer/ authorized agents/distributors of the manufacturer /service providers and firms undertaking job works can be registered the Vendor Development Protocol. To register, the vendor should have GST registration (if applicable). The vendor must maintain an office/ shop/ show room registered in its own name, and should have a bank account wherein payments can be sent directly. • Vendors interested in registration shall submit an application furnishing details of their business and product line. On receipt of the application, they will be issued a vendor development questionnaire. The vendor should submit the duly filled questionnaire, along with their credentials, and details of manufacturing capacity, quality control facilities, past performance, after-sales service, financial background etc. • The duly filled development document received is submitted to the VENDOR DEVELOPMENT COMMITTEE. The Committee consists of Head of User Dept., Head of Material Testing/R & D Dept. & Head of Purchase and has the following powers: ○ after examining the documents and based on the Protocol for Vendor Development of that material, it, can advise the Purchase Department to call for samples for evaluation / plant trial of the material, or call for additional details / clarifications, if required. ○ In case the sample confirms to the specification, the Committee can advise Purchase Department to include the vendor in the list of approved vendors/recommend to issue plant trial order. ○ The Committee can also depute an officer for facility audit, if necessary. ○ Based on the facility audit, and plant trial reports, the Committee can advise the Purchase Department to include the vendor in the list of approved vendors. • Registered firms can be removed from the list of approved firms if they continuously fail to abide by the terms and conditions of the tender / contract or fail to supply goods on time or supply sub-standard items /goods or any false declaration made to company. Suppliers not taking part in the Tender / not supplying to HLL for a continuous period of 5 years will be removed from the list of approved suppliers Second Step: - VENDOR RATING PROCEDURE FOR SUPPLIER RATING Supplier rating is done based on the following criteria. 1) Quality of the product/service 2) Cost rating 3) Adherence to delivery schedule QUALITY RATING (QR) Quality rating comprises of two factors. 1) Inspection rating: Based on value of parameters of materials inspected. 2) Performance rating: Based on value of parameters of materials inspected during performance inspection. PRICE RATING This criterion compares the relationship of the price of vendors with the effective price of those materials. PRICE RATING = (Vendor’s effective price- Effective price of material)/ Effective price of material*100 DELIVERY RATING (DR) Delivery Rating of raw materials is calculated by Purchase Department on the basis of contracts entered into by concerned User Departments as mentioned below: • delivery in time as per schedule: Full credit 2 deliveries after the scheduled date: a grace period of 10 days is given if the material has been dispatched within the delivery date, and the party would get full credit for delivery. • if the material is received within 15 days from the date of delivery mentioned in the Order, a score of 25 % will be deducted. • if the material is delivered between the 16th day and 30th day stipulated in the Order, the score will go down by another 25 % i.e. a total deduction of 50 % • this will continue for every fortnight for the next two fortnights. • the total score thus obtained forms the basis for the grading. Frequency of supplier rating: The vendor rating shall be done on annual basis, or as detailed in Quality Management System (QMS) manual.

SMART CONVEYOR BELT WITH PRODUCTION COUNT DISPLAY

Today's world is very competitive. Technology is an important part of this. The country has a lot of innovations and dominates the world. It is important to survive in technology. The country's economy can be impacted by trade. There should be production where there is work. Business needs technological development to get better products. It's important for businesses to have automation. It is used on the production line. The items can be sorted by height. The product must be placed on the conveyor belt. After checking the height of the product, the product with the correct height will be thrown into the product box, the product with the incorrect height will be left in the waste from the automatic process. They dash aren't counted as legitimate users. Keywords: Belt, Microcontroller, Sensors, Simulation.

Real-time detection of surface cracking defects for large-sized stamped parts

This study presents a framework for the real-time detection of surface cracking in large-sized stamped metal parts. The framework aims to address the challenges of low detection efficiency and high error rates associated with manual cracking detection. Within this framework, a novel network, SNF-YOLOv8, is proposed to efficiently detect cracking while ensuring that the detection speed matches the production speed. The network incorporates a convolutional spatial-to-depth module to enhance the detection of small-sized cracking and mitigate surface interference during inspections. Furthermore, a visual self-attention mechanism is introduced to improve feature extraction. A combination of standard convolutional and depth-wise separable convolutional layers in the neck network enhances speed without compromising accuracy. Experimental validation conducted using a dataset from actual production lines, in collaboration with a multi-national corporation, demonstrates that SNF-YOLOv8 achieves an average precision of 85.2% at a detection speed of 164 frames per second. The framework achieves an accuracy rate of 98.8% in detecting large-sized cracking and 96.4% in detecting small-sized cracking, meeting the requirements for high-precision and real-time detection applications.

Multi-view fringe projection profilometry for surfaces with intricate structures and high dynamic range

Fringe projection profilometry plays an important role for quality control in production line. However, it is facing challenges in the measurement of objects with intricate structures and high dynamic range that involved in precision manufacturing and semiconductor packaging. In this paper, a multi-view fringe projection profilometry system, which deploys a vertical telecentric projector and four oblique tilt-shift cameras, is presented to address the “blind spots” caused by shadowing, occlusion and local specular reflection. A flexible and accurate system calibration method is proposed, in which the corrected pinhole imaging model is used to calibrate the telecentric projection, and the unified calibration is performed by bundle adjustment. Experimental results show that the 3D repeated measurement error and standard deviation are no more than 10 μm within a measurable volume of 70 × 40 × 20 mm3. Furthermore, a group of experiments prove that the developed system can achieve complete and accurate 3D measurement for high dynamic range surfaces with complex structures.

Bridging the gap: integrating AR and VR production line training to enhance furniture carpentry students’ understanding of production processes

ABSTRACT Large-scale furniture production involves complex manufacturing processes and methods. However, Taiwanese schools face limitations in terms of space and equipment, restricting students from engaging in furniture prototype creation or small-batch production. As a result, students often struggle to grasp practical aspects of large-scale furniture production, leading to a partial understanding of manufacturing processes and methods. To bridge this gap, this study introduces Augmented Reality (AR) technology and miniaturized furniture production lines in a school’s woodshop. AR can be used to guide students through miniaturized production stages, emphasizing semi-automated furniture production features. Additionally, Virtual Reality (VR) technology is combined to provide an immersive experience of real-world, large-scale furniture production, allowing students to understand processing scenarios and conditions in facilities of other countries. In an experiment involving 30 first-year students, an experimental group (15 participants) received mixed AR and VR instruction, while a control group (15 participants) received traditional teaching methods. Findings reveal that AR and VR integration enhanced students’ comprehension of differences between the school workship and overseas large-scale furniture production facilities. This strategy improved their furniture production knowledge, and fostered adaptability to various production conditions and methods.

Controllable Fabrication of Silicon Solar Cells with Inverted or Upright Pyramid Structures by a One-Step Copper Catalysis Method

Abstract Silicon has garnered significant attention as the primary material for solar cell preparation. Traditional alkaline etching solutions are limited to creating an upright pyramid structure on monocrystalline silicon surfaces. However, research indicates that an inverted pyramid structure exhibits superior light-trapping properties compared to the upright pyramid structure. In this study, we employed a one-step copper ion metal-assisted chemical etching process to fabricate an inverted pyramid structure on monocrystalline silicon wafers. This method allows for the customization of either inverted or upright pyramid structures by adjusting the concentration of specific solution components. Characterization of the textured silicon wafers reveals that the inverted pyramid structure exhibits lower reflectivity than both the upright pyramid structure and polished silicon. By integrating this texturing technique into the solar cell production line, we successfully produced solar cells with both inverted and upright pyramid structures. Evaluation of various solar cell parameters demonstrates that the inverted pyramid structure outperforms the upright pyramid structure, showcasing lower reflectivity and higher photoelectric conversion efficiency.

Fusion method for digital twin model of a production line

Fusion method for digital twin model of a production line