Manufacturing Process Quality Research Articles

Joint optimization of preventive maintenance and product quality improvement policies for deteriorating manufacturing systems with quality-reliability dependency

Machine reliability and product quality are usually investigated independently in most traditional models. However, in the actual manufacturing process, machine reliability and product quality are commonly interdependent due to strong coupling between the machine and the product. In this paper, three joint optimization models for preventive maintenance (PM) and product quality improvement are developed for deteriorating manufacturing systems considering quality-reliability dependency. First, the degradation process of quality-related components (QRCs) is depicted by Gamma process, and product quality deviation is obtained by considering the quantitative relationship between the degradation of QRCs and product quality. Second, joint optimization policy of age-based maintenance and product quality improvement is proposed for deteriorating manufacturing systems with quality-reliability dependency. Third, above maintenance policy is extended to manufacturing systems processing jobs with random processing time, and then maintenance first (MF) and maintenance last (ML) policies are developed, respectively. Furthermore, we compare all of the maintenance models to determine which policy should be selected for manufacturing systems under specific conditions, and all theoretical discussions in this paper are analytically made. Finally, the effectiveness of the proposed joint policies is verified by a case study on a cylindrical gear housing machining process.

D2-SPDM: Faster R-CNN-Based Defect Detection and Surface Pixel Defect Mapping with Label Enhancement in Steel Manufacturing Processes

The steel manufacturing process is inherently continuous, meaning that if defects are not effectively detected in the initial stages, they may propagate through subsequent stages, resulting in high costs for corrections in the final product. Therefore, detecting surface defects and obtaining segmentation information is critical in the steel manufacturing industry to ensure product quality and enhance production efficiency. Specifically, segmentation information is essential for accurately understanding the shape and extent of defects, providing the necessary details for subsequent processes to address these defects. However, the time-consuming and costly process of generating segmentation annotations poses a significant barrier to practical industrial applications. This paper proposes a cost-efficient segmentation labeling framework that combines deep learning-based anomaly detection and label enhancement to address these challenges in the steel manufacturing process. Using ResNet-50, defects are classified, and faster region convolutional neural networks (faster R-CNNs) are employed to identify defect types and generate bounding boxes indicating the defect locations. Subsequently, recursive learning is performed using the GrabCut algorithm and the DeepLabv3+ model based on the generated bounding boxes, significantly reducing annotation costs by generating segmentation labels. The proposed framework effectively detects defects and accurately defines them, even in randomly collected images from the steel manufacturing process, contributing to both quality control and cost reduction. This study presents a novel approach for improving the quality of the steel manufacturing process and is expected to enhance overall efficiency in the steel manufacturing industry.

Sum of defects (SOD) chart for joint monitoring of the two parameters of a zero-inflated Poisson process

Rapid technological advancements and automation in today's world have led to many high quality manufacturing processes, where most of the products are free from defects. The defects data from high quality processes are commonly modeled by twoparameter zero- inflated Poisson (ZIP) distribution. Most of the existing control charting procedures for ZIP processes are developed assuming that all the manufactured units are inspected one by one, and most often separate control charts are proposed for monitoring the two parameters of a ZIP process. However, joint monitoring of the two parameters in a single chart can offer significant operational advantages because one needs to focus on a sole chart and a single charting statistic. Again, in many manufacturing set ups 100% inspection may not always be feasible. Recently, a few one-chart schemes with group inspection of small samples are reported in literature, and all these schemes require estimation of the two parameters based on accumulated samples till the current sampling stage for computation of the monitoring statistics. This accumulation of samples introduces several limitations in these schemes. In this paper, probability mass function and the cumulative distribution function (cdf) of the sum of defects (SOD) in a sample of size are defined, and a control chart for SOD is proposed. Both the parameters of a ZIP process can be jointly monitored using the SOD chart. Again, since no accumulation of the samples is required for application of the SOD chart, it is free from all limitations of the existing one-chart schemes with group inspection. Two case studies based on past data are presented. The performance of the SOD chart is found to be very encouraging.

Novel sensorized additive manufacturing-based enlighted tooling concepts for aeronautical parts

This paper presents lightweight tooling concepts based on additive manufacturing, with the aim of developing advanced tooling systems as well as installing sensors for real-time monitoring and control during the anchoring and manufacturing of aeronautical parts. Leveraging additive manufacturing techniques in the production of tooling yields benefits in manufacturing flexibility and material usage. These concepts transform traditional tooling systems into active, intelligent tools, improving the manufacturing process and part quality. Integrated sensors measure variables such as displacement, humidity and temperature allowing data analysis and correlation with process quality variables such as accuracy errors, tolerances achieved and surface finish. In addition to sensor integration, additive manufacturing by directed energy arc and wire deposition (DED-arc) has been selected for part manufacturing. The research includes the mechanical characterisation of the material and the microstructure of the material once manufactured by DED-arc. Design for additive manufacturing" principles guide the design process to effectively exploit the capabilities of DED-arc. These turrets, equipped with sensors, allow real-time monitoring and control of turret deformation during clamping and manufacturing of aeronautical parts. As a first step, deformation monitoring is carried out within the defined tolerance of ± 0.15, which allows a control point to be established in the turret. Future analysis of the sensor data will allow correlations with process quality variables to be established. Remarkably, the optimised version of the turret after applying DED technology weighed only 2.2 kg, significantly lighter than the original 6 kg version. Additive manufacturing and the use of lightweight structures for fixture fabrication, followed by the addition of sensors, provide valuable information and control, improving process efficiency and part quality. This research contributes to the development of intelligent and efficient tool systems for aeronautical applications.

Phase-field based modeling and simulation for selective laser melting techniques in additive manufacturing

In this study, we develop a phase-field model to describe the solid–liquid phase changes, heat conduction phenomena, during the selective laser melting process. This model is based on the variational principle of minimizing the free energy functional. The proposed model integrates the phase-field equation and the energy equation, which are used to capture the dynamical behavior of the interfacial evolution. We use the semi-implicit Crank–Nicolson scheme and central difference to ensure second-order accuracy in time and space. The numerical scheme is unconditional energy stable. This paper rigorously proves the energy stability of the phase-field model of the Selective Laser Melting process, which confirms the numerical stability and the physical rationality of the solution. Various numerical experiments are performed to verify the robustness of our proposal model. This model can effectively simulate the energy transfer and shape structure changes of the products during the selective laser melting manufacturing process, which provides a reliable guarantee for predicting and optimizing the quality and performance of the selective laser melting process additive manufacturing process.

An in-silico approach towards multivariate acceptable ranges in biopharmaceutical manufacturing

Multivariate interactions between process parameters can heavily impact product quality and process performance in biopharmaceutical manufacturing processes. Thus, multivariate interactions should be identified and appropriately controlled. This article describes an in-silico approach to establish multivariate acceptable ranges; these ranges help to illustrate the combined impact of multiple input variables on product quality and process performance. Additionally, this article includes a case study for a monoclonal antibody polishing application.Proven acceptable ranges are set by changing only one input parameter at a time while keeping all others constant to understand the impact of process variability on product quality or process performance, but the impact of synergistic variables are not evaluated. Within multivariate acceptable ranges, any combination of input parameters of a unit operation yields the desired product quality and process performance. The layered approach applied in this article is based on risk assessment and statistical models to leverage prior knowledge and existing data. The risk assessment is specific for a manufacturing facility but is applicable to multiple products manufactured in the same facility. No additional wet-lab experiments are required for building the statistical models when development and process characterization are executed using a design of experiments approach, compared to a univariate evaluation of data. The established multivariate acceptable range justifies revised normal operating ranges to ensure process control. Further, the determination of multivariate acceptable ranges adds to overall process knowledge, ultimately supporting the implementation of a more effective control strategy.

Impact of Humidity and Temperature on Quality of Automotive Products

In the automotive industry, maintaining optimal quality and performance for electronic components used in vehicles is crucial to meet consumer demands and regulatory standards. Humidity and temperature are pivotal environmental factors affecting automotive systems, influencing all essential electrical components. This article provides a study elucidating their effects on reliability, durability, and customer satisfaction. Additionally, it discusses the significance of controlling temperature and humidity in production areas and quality laboratories, highlighting their crucial role in ensuring the highest standards of quality and reliability in automotive manufacturing and analysis processes.

Standardization Of Rakik Maco By Ulakan Tapakis Padang Pariaman

It was found that the quality of Rakik Maco differed from one seller to another. Like Rakik Maco's texture, some are fragile, some are tough and hard, some are thin and some are thick. In terms of color, there is Rakik Maco with a golden yellow color and a brownish yellow tone. In terms of taste, some are savory and some are bland. In processing Rakik Maco, sellers usually measure the ingredients using glass and some only look at the texture and viscosity of the dough, so the resulting quality varies, both in terms of texture, taste, aroma and color. Food quality is very important for Rakik Maco sellers to pay attention to. To maintain product quality, it is necessary to have appropriate standardization regarding the materials used, tools used and the process of making Rakik Maco.The focus of this research is the standardization of the Rakik Maco recipe in Ulakan Tapakis, Padang Pariaman Regency, which includes the measurements of ingredients and tools used, the manufacturing process and product quality (shape, color, aroma, texture and taste). To maintain product quality, standardization is necessary. Standardization is carried out regarding materials, tools and manufacturing processes. The quality of Rakik Maco can be seen from several aspects such as: shape, color, aroma, texture and taste. The following are the results of the Rakik Maco quality test: Rakik Maco has a thin round shape, with the use of a mold that makes Rakik Maco uniform and neat, the color of quality Rakik Maco is packaged yellow, the aroma of Rakik Maco is created from the distinctive aroma of rice flour and supported by the aroma of turmeric leaves , Rakik Maco has a crunchy texture, and Rakik Maco has a savory taste.

Characterization of curing status of commercial tire compounds with vein graphite powder and its particle sizes—Experimental and computational study

AbstractThe use of solid tires in demanding industrial applications, subjected to substantial mechanical loads, poses significant challenges due to heat generation from hysteresis and tread friction. Managing this heat is crucial to mitigate the risk of tire blowouts and layer separation. This research investigates the impact of introducing vein graphite powder, varying in particle size, into a commercially used solid tire compound, both experimentally and computationally. The research findings presented in this paper reveal substantial changes in thermal conductivity, specific heat capacity, rate constant, induction time, and the order of reaction in solid tires. Contemporary industry trends involve predictive methods for monitoring the curing process in tire manufacturing. A tire curing simulation model based on finite (FE) element analysis is developed. FE modeling is favored due to its accuracy and adaptability, especially when dealing with the intricate geometry and multi‐layered, multi‐compound structure of tires. The complex interplay between heat transfer and curing processes is effectively addressed using user subroutines (UMATHT) in commercial FE software like ABAQUS. In this analysis, thermal conductivity, heat capacity, order of reaction, rate constant, and induction time of the commercial tire compound are considered as temperature‐dependent variables. The computational model not only demonstrates its potential to significantly enhance the efficiency and quality of tire manufacturing processes but also contributes to a deeper understanding of the curing behavior of graphite powder‐based solid tire compounds. Consequently, it provides a valuable tool for optimizing tire manufacturing procedures and ensuring the safe and reliable performance of solid tires under demanding operational conditions. This research bridges the gap between the traditional commercial tire compound and the use of vein graphite powder in tire manufacturing and advanced computational methods, facilitating improvements in tire quality and safety.

Reliability oriented key quality characteristics driven integrated built-in reliability activity chain and approach for manufacturing process

Product reliability is determined at the design phase and built into the product through manufacturing process. To effectively implement reliability activities at each stage, reliability data in product life cycle is integrated with KQC (key quality characteristics), and the concept of R-KQC (reliability-oriented KQC) is proposed. Based on the R-KQCs, this study presented an integrated Built-in reliability (BIR) approach to ensure that the product reliability formed losslessly conforms to the reliability goal determined in the design phase. First, the connotations of BIR and BIR-oriented RQR (manufacturing system Reliability, manufacturing process Quality, and product Reliability) chain are proposed by considering the evolution mechanism of product reliability in the life cycle driven by the R-KQCs. Second, a product BIR activity chain consisting of plan, synthesis, and improvement based on R-KQC is presented by the BIR-oriented RQR chain. Third, an R-KQC-driven BIR assurance technology framework based on the BIR activity chain is proposed, and a product reliability modeling considering the deviation of R-KQCs is established based on the framework. Finally, the validity of the proposed approach is verified using the reliability assurance for an automobile engine cylinder head as an example.

Benchmarking the quality improvement strategies of wire arc additive manufacturing process using fuzzy QFD approach

PurposeThe purpose of this paper is to improve the productivity and quality of the wire arc additive manufacturing process by benchmarking the strategies from the selected six strategies, namely, heat treatment process, inter pass cooling process, inter pass cold rolling process, peening process, friction stir processing and oscillation process.Design/methodology/approachTo overcome the lack of certainty associated with correlations and relationships in quality functional deployment, fuzzy numbers have been integrated with the quality functional deployment framework. Twenty performance measures have been identified from the literature under five groups, namely, mechanical properties, physical properties, geometrical properties, cost and material properties. Using house of quality weights are allocated to performance measures and groups, relationships are established between performance measures and strategies, and correlations are assigned between strategies. Finally, for each strategy, relative importance, score and crisp values are calculated.FindingsInter pass cold rolling process strategy is computed with the highest crisp value of 15.80 which is followed by peening process, heat treatment process, friction stir processing, inter pass cooling process,] and oscillation process strategy.Originality/valueTo the best of the authors’ knowledge, there has been no research in the literature that analyzes the strategies to improve the quality and productivity of the wire arc additive manufacturing process.

Experimental investigation and optimization of the effect garnet vibratory tumbling as a post-process on the surface quality of 3D printed PLA parts

The method known as additive manufacturing causes high surface roughness between layers depending on the technique used at the end of the product development process. This can be an important problem in three-dimensional (3D) manufacturing depending on the usage area. To solve this problem, in this experimental study, the effect of vibratory tumbling (VT) on surface roughness in 3D printing was investigated using garnet abrasive particles. Optimization with the best parameters was also performed and the results were analyzed. This experimental study investigated the effect of vibratory tumbling on surface roughness in 3D printing produced from Polylactic acid (PLA) material using garnet abrasive particles. The surface roughness (Ra) values were measured at different vibration durations for each mesh size. The results provide insights into the impact of vibratory tumbling on surface roughness in 3D-printed parts. The study involved subjecting the printed parts to vibratory tumbling using garnet abrasive particles of various mesh sizes (80, 90, 100, 120, 150, 180, and 220 mesh). Surface roughness measurements were taken at different vibration durations (2, 4, 6, 8, 10, and 12 hours) for each mesh size. A surface roughness measuring device was used to obtain the roughness values. The findings reveal that vibratory tumbling with garnet abrasive particles effectively reduces surface roughness in 3D printed parts. As the vibration duration increased, smoother surfaces were achieved. The data collected for each mesh size and vibration duration offer valuable insights into the relationship between vibratory tumbling and surface roughness in 3D printing. The surface roughness of the printed samples was reduced by 60% on average by using the optimum values after post-process. This research highlights the potential of vibratory tumbling as a viable method for improving surface roughness in 3D printing applications. Emphasis is placed on optimizing the vibration duration and selecting the appropriate mesh size to achieve the desired surface quality. Overall, this study contributes to our understanding of the effect of vibratory tumbling on surface roughness in 3D printing and provides considerable insights for enhancing surface quality in additive manufacturing processes.

Reliability-oriented product manufacturing quality improvement approach based on R-KQC and DMAIC

ABSTRACT Reliability-oriented product quality improvement is of great significance to assurance the fitness of quality control and management activities in the manufacturing process. A novel reliability-oriented product manufacturing quality improvement approach based on DMAIC (i.e. define, measure, analysis, improvement, control) and driven by reliability-oriented key quality characteristic (R-KQC) is proposed to prevent reliability degradation and improve product fitness quality in the manufacturing process. First, with the manufacturing reliability based on RQR chain taken into consideration, the connotation of R-KQC is defined. Second, a reliability-oriented DMAIC process driven by R-KQC is proposed. The R-KQC identification method based on feature selection and Shaply additive explanations (SHAP) is proposed. In order to improve the quality of manufacturing process effectively, R-KQC improvement method of response surface and R-KQC control method based on time-between-events (TBE) control chart are proposed. Finally, an illustrated example of an insulating base is provided to verify the effectiveness of the proposed approach, the average failure rate of the latest batch is reduced by 37.8%.

Optimizing dispensing performance of needle-type piezoelectric jet dispensers: a novel drive waveform approach

The dispensing performance of needle-type piezoelectric jet dispenser constitutes a crucial factor that ensures the quality of additive manufacturing processes. In this paper, a novel approach is proposed to enhance the dispensing performance of needle-type piezoelectric jetting dispensers by introducing a more adaptable driving waveform based on Bézier curves. Initially, the approach considers the electromechanical coupling effect of the needle-type piezoelectric dispenser and constructs a high-precision fluid–solid coupling model of the dispensing process. Subsequently, a multi-physics field joint simulation platform combining Matlab and Fluent is established to systematically analyze control strategies in real service conditions. Next, a new driving waveform based on Bézier curves is introduced, and the control parameters are optimized using a genetic algorithm to address issues such as air bubbles in the droplets and instability of the dispensing process. The optimized waveform based on the Bézier curve reduces the volume of air suction during the dispensing process by over 20% compared to the traditional waveform and eliminates the uncontrolled vibration state of the needle in the fluid, ensuring the stability of the entire fluid refill process. Finally, the optimized control strategy is verified through experiments and compared with traditional methods. The experiment demonstrates its advantages in addressing issues with no air bubbles in the droplets and consistency of the droplets. This study provides valuable insights into optimizing the dispensing performance of needle-type piezoelectric jetting dispensers regarding control strategy.

Integrated web portal for non-destructive salt sensitivity detection of Camelina sativa seeds using fluorescent and visible light images coupled with machine learning algorithms.

Climate change has created unprecedented stresses in the agricultural sector, driving the necessity of adapting agricultural practices and developing novel solutions to the food crisis. Camelina sativa (Camelina) is a recently emerging oilseed crop with high nutrient-density and economic potential. Camelina seeds are rich in essential fatty acids and contain potent antioxidants required to maintain a healthy diet. Camelina seeds are equally amenable to economic applications such as jet fuel, biodiesel and high-value industrial lubricants due to their favorable proportions of unsaturated fatty acids. High soil salinity is one of the major abiotic stresses threatening the yield and usability of such crops. A promising mitigation strategy is automated, non-destructive, image-based phenotyping to assess seed quality in the food manufacturing process. In this study, we evaluate the effectiveness of image-based phenotyping on fluorescent and visible light images to quantify and qualify Camelina seeds. We developed a user-friendly web portal called SeedML that can uncover key morpho-colorimetric features to accurately identify Camelina seeds coming from plants grown in high salt conditions using a phenomics platform equipped with fluorescent and visible light cameras. This portal may be used to enhance quality control, identify stress markers and observe yield trends relevant to the agricultural sector in a high throughput manner. Findings of this work may positively contribute to similar research in the context of the climate crisis, while supporting the implementation of new quality controls tools in the agri-food domain.

Fault Analysis of 35kV Outdoor Vacuum Circuit Breaker Pillar Porcelain Sleeve

In order to further clarify the failure mechanism of the pillar porcelain insulator, the field inspection, the microscopic morphology analysis and material analysis of the ceramic shell of the pillar under a 35kV outdoor vacuum circuit breaker were carried out. The results show that the direct cause of the failure is the discharge between the high voltage conductor inside the porcelain sleeve and the weak part of the porcelain sleeve, and the high temperature arc leads to the bursting of the porcelain sleeve. The indirect cause of the failure is the loose porcelain of the porcelain sleeve, the existence of internal layering, uneven composition and micro-pores and other macro and micro defects, and the original defects of the inner surface of the porcelain sleeve, such as local unglazed and poor quality of the porcelain sleeve manufacturing process. In the long-term outdoor operation, the external moisture and gas penetrate into the porcelain sleeve, which leads to the deterioration of the insulation performance of the porcelain sleeve, and eventually leads to failure.

Development of descriptive analysis module within a cloud-based quality analyser: Case study in a guitar industry

Along with the revolution of the manufacturing world, companies face a new challenge to continuously improving their production processes to meet the escalating demand for high-quality goods in a fiercely competitive market. The previous concept of Cloud-Based Quality Analyzer (CQA) can be used as a real-time monitoring software that provides information of the on-going process. This concept can be implemented as an effort to achieve high quality manufacturing. This study aimed to develop the descriptive analysis module to realise the concept of CQA and evaluate its ability in realising online quality monitoring process. By employing the waterfall methodology, this study developed and implemented the descriptive analysis module in the CQA environment. The module was implemented in a case study in guitar manufacturing. The result showed that this module worked perfectly in displaying the multivariate process control chart and successfully gave the warning when some processes were in out-ofcontrol state. Furthermore, the user acceptance test also showed a positive response from the users. The descriptive analysis module was believed able to enhance the quality of manufacturing process while reducing the dependencies to the human quality engineers. However, more case studies in various industries were required to evaluate the benefit of implementing this module.

Analisis Pengaruh Clearance terhadap Hasil Potong pada Proses Stamping Produk Member Floor Side Inner LH

The development of production demands in the automotive industry has resulted in an increase in the need for various shapes and types of metal materials. Components used in automotive industrial production require the strength and durability of metal materials. The products produced in this sector are very diverse, including engine components, vehicle body parts and various vehicle accessories. To be able to meet customer demands for quality and efficiency, adequate production process support tools are needed, of course, such as dies. This tool is very suitable for making products from sheet metal with the same shape and in large quantities in a relatively short time. However, in making printing equipment (Dies) it does not require operation, it requires precise calculations so that accurate process results will be obtained, especially in calculations and clearance accuracy. Efforts to improve quality and efficiency in the product manufacturing process, for this reason, an analysis and improvement of the clearance of the cut results on SPC440 material with a thickness of 1 mm for the Member Floor Side Inner Lh product was carried out. The results of the improvement in the clearance of the part were able to optimize the clearance value from 0.06mm to 0.075mm.

Quality inspection of specific electronic boards by deep neural networks

Reliability and lifetime of specific electronics boards depends on the quality of manufacturing process. Especially soldering splashes in some areas of PCB (printed circuit board) can cause change of selected electrical parameters. Nowadays, the manual inspection is massively replaced by specialized visual systems checking the presence of different defects. The research carried out in this paper can be considered as industrial (industry requested) application of machine learning in automated object detection. Object of interest—solder splash—is characterized by its small area and similar properties (texture, color) as its surroundings. The aim of our research was to apply state-of-the art algorithms based on deep neural networks for detection such objects in relatively complex electronic board. The research compared seven different object detection models based on you-look-only-once (YOLO) and faster region based convolutional neural network architectures. Results show that our custom trained YOLOv8n detection model with 1.9 million parameters can detect solder splashes with low detection speed 90 ms and 96.6% mean average precision. Based on these results, the use of deep neural networks can be useful for early detection of solder splashes and potentially lead to higher productivity and cost savings.

Predicting product quality in continuous manufacturing processes using a scalable robust Gaussian Process approach

This work describes an Artificial Intelligence (AI)-based solution that predicts product quality when applied to a continuous manufacturing process. The proposed solution uses process parameters and product quality measurements that are obtained from a production line. The work detailed herein is problem-driven, showing an application within one of the UK’s foundation industries and identifying five key criteria an AI solution should ideally satisfy in continuous manufacturing applications; scalability, modularity, stable out-of-data performance, uncertainty quantification and robustness to unrepresentative data. The shortcomings, relative to these five criteria, of available AI approaches are discussed before a potential solution is presented. The proposed approach involves the application of a generalised product-of-expert Gaussian process whose noise model is constructed from a Dirichlet process. The ability of the model to fulfil the five key criteria and its performance when applied to the foundation industry case study is demonstrated.