Inspection Costs Research Articles

Designing acceptance sampling plans for polynomial profiles using EWMA statistics based on process yield index

Inspection of raw materials, semi-finished products or final products is an important part of a quality assurance system. Acceptance sampling is a procedure used to decide either to accept or reject suppliers’ submitted lots in accordance with a specific standard. In this manuscript, designing acceptance sampling plans for lots with polynomial profile as quality characteristic is considered. To use previous lot information on deciding for accept or reject the available lot, exponentially weighted moving average (EWMA) statistic based on process yield index (Spk ) of polynomial profiles with two-sided specification limits is applied. For this purpose, three acceptance sampling plans of repetitive group sampling (RGS), resubmitted and also combined RGS-resubmitted plans are developed in this paper. The Parameters of the proposed plans are determined using non-linear optimization model. Performance of the proposed acceptance sampling plans are compared using numerical methods and Monte-Carlo simulations. Simulation results emphasize that the proposed combined sampling plan mostly requires less average sampling number (ASN) and consequently leads to decreasing inspection time and costs. The application of the proposed combined plan is also demonstrated using a real case.

Automatic Rainwater Quality Monitoring System Using Low-Cost Technology

Water quality monitoring serves diverse purposes, such as assessing water safety for drinking or agriculture. However, traditional methods are often time-consuming and costly. Additionally, assessing water quality on demand is not always feasible, and parameter variations are not consistently documented. With the advent of affordable equipment capable of efficient performance with minimal resources, remote monitoring sans physical access to the environment has become viable. This has led to reduced inspection and control costs. Understanding the applications, characteristics, and limitations of these devices is crucial for maximizing their benefits. Rainwater quality remains understudied in many areas, leaving its physical, chemical, and microbiological conditions unknown. These conditions are influenced by various atmospheric factors, including both human and natural activities. This research paper develops monitoring stations employing low-cost technology to assess rainwater quality in Morelia City. A prototype was developed based on low-cost technology implementation. Additionally, basic parameters measured include pH, total dissolved solids, turbidity, and temperature, utilizing an Arduino microcontroller for data processing. Data collected from these stations are transmitted via a Wi-Fi communication module to a web platform, enabling real-time visualization of measurements both spatially and temporally. Utilizing such a device offers several advantages compared to standard methods of assessing rainwater quality. It enables continuous measurements and provides a record of how rainwater quality is affected over time by human and natural activities.

Research on the Billing Standard of Highway and Water Transport Test and Inspection in Shandong Province

With the rapid development of China's economy, all sectors of society on the quality and safety of road and water transport projects have gradually increased the importance of road and water transport test, and inspection services market demand and cost share have also increased. Many test and inspection organizations have flooded the market, resulting in malicious competition and price confusion in the test and inspection market. Therefore, providing a set of scientific, reasonable, and standardized test and inspection billing standards is urgently needed to improve the test and inspection management of road and water transport projects and solve the existing problems. Based on this, the project will rely on Shandong Province highway and water transport engineering test and inspection billing standard research project, systematic analysis of Shandong Province highway and water transport engineering test and inspection factors. It is from the cost of scientific management and field research of the characteristics of the system research, for Shandong Province test and inspection charges to provide a more objective, accurate, scientific theoretical basis for calculating, for the further preparation of Shandong Province, highway and water transport engineering test and inspection Billing standards in Shandong Province to lay a solid theoretical foundation.

Two-Stage Life Test Acceptance Sampling Plans for the Weibull Distribution using Trinomial Distribution

In materials engineering applications, lifetime of product is one of the quality characteristics of interest. Lifetimes of products are inspected in life tests upto a predetermined time t0, which may observe early failure of some of the products; incorporation of this fact may not only reduce the cost of inspection, but also provide some more insight into the failure mechanism. In this paper, we consider a two-stage time censoring scheme by dividing test truncation time t0 into two parts [.. formula..] and observe the number of failures in each interval, considering the remainder items as censored at t0. The trinomial distribution has been used to develop life test acceptance sampling plans for establishing quantile life of items having Weibull lifetimes. The proposed life test sampling plan gives added advantage to producers and extra safeguard to consumers, as compared to the life test sampling plans available in the literature for the Weibull distribution. Tables of plan parameters are constructed through code written in R 4.1.2, and illustrated for some real-life industrial cases.. KEYWORDS :Quantile, Two-stage censoring, Trinomial distribution, Weibull distribution.

Hybrid Inspection Method using Three Dimensional Scanning, Lock-in Thermography and Laser Shearography

The proportion of composite materials (such as CFRP) to the metal used on the modern aircrafts is rising, imposing different kind of failure modes. Since the composite structures are known to be sensitive to the impact loading, there is need for means to assess the sub-surface damage in the structure rapidly. Royal Netherlands Aerospace Centre NLR has an extensive track record on contactless non-destructive inspection (NDI) methods based on optical sensors, such as 3D surface scanning, lock-in thermography and laser shearography. The combination of these methods (multi-domain inspection) enables us to assess the structural integrity of an aircraft outer surface in a short time, reducing inspection costs and the “down time” of the aircraft. Recently, NLR is working towards a 3D oriented mesh environment of an object, enhanced with NDI data, providing sub-surface damage information. By automatically stitching the 3D object images, it is possible to expand this method for a complete scan of an aircraft surface. Furthermore, the thermographic and shearography imaging information has been integrated into the 3D surface accounting for the image distortion from the different measurement angles. In this paper, the results from the various studies will be presented involving integration of the 2D measurements with the 3D scan mesh. Keywords: Thermography, Shearography, 3D scanning, Data fusion

Optimal designing of skip-lot sampling plan with a provision for reducing the normal inspection using the Taguchi process capability index

A product with a strong track record of quality might have less inspection according to the skip-lot sampling plan. As a result, skip-lot sampling plans are created to cut down the inspection cost with desired protection. The skip-lot sampling method is also reliable, practical, and favorable from an economic standpoint. As a result, this work develops the SkSP-V plan, a novel system of skip-lot sampling plan, based on the Taguchi process capability index. The proposed plan stipulates that any lot is rejected during sampling inspection, normal inspection must resume, however, it also allows for a reduced level of normal inspection in the event that higher product quality is demonstrated. The exact sampling distribution of the estimated Taguchi capability index is used to derive the operating characteristic function of the proposed plan. An optimization model is employed to establish the optimal plan parameters, guaranteeing adherence to the specified quality levels and risk criteria. The performance measures such as probability of acceptance and average sample number of the proposed plan are examined and compared with existing plans. This article also includes real-world example to effectively demonstrate the practical implementation of the proposed SkSP-V plan based on the Taguchi process capability index.

Formation of Repetitive Group Sampling Plan for Life Tests Based on Percentiles using Odd Frechet Inverse Rayleigh Distribution

Objectives: This paper presents the idea of Repetitive Group Sampling Plans using Odd Frechet Inverse Rayleigh Distribution, when the life test is truncated at a pre-specified time. The main objective of the proposed sampling plan is to minimize the sample size because the analogous inspection time and inspection cost will be reduced. Methods: A truncated life test may be conducted to evaluate the smallest sample size to insure certain percentile lifetime of products. The minimum sample size and its ratio for the specified lifetime percentile were derived for the producer’s risk. The operating characteristic values are also obtained for the proposed sampling plan. The corresponding table values are obtained through Monto Carlo Simulation technique. Findings: The results from the sampling plan developed in this research can easily be adaptable in practical situation with small sample sizes and fewer experimental times and hence the plan yields a better result for reliability sampling plan. Novelty: The work designed and developed with the aim that sampling plan in this paper may be helpful for the engineers and statistical plan developers in the field of statistical quality control especially in attribute reliability sampling plan. The tables are generated which is useful for both producer and consumer. This plan can highly recommendable since the products are randomly sampled and also the procedure for sampling involves less experiment time and with minimum sample size. Keywords: Odd Frechet Inverse Rayleigh Distribution, Percentiles, Repetitive Group Sampling Plan (RGS), Producer’s risk

Research on the Joint Construction of a National Multi-source and Multi-resolution image Checkpoint Database

Abstract. In the process of quality inspection of Remote sensing image data results, the reuse of spatial location information of multiple units, multiple projects and multiple sources can not only overcome the problems of long time to obtain control information, high cost and difficulty in obtaining some areas, but also the basis for achieving efficient and high-precision geometric correction. From the perspective of reusability of checkpoints and saving the cost of quality inspection of remote sensing images, this paper discusses the necessity of joint construction of multi-source and multi-resolution image checkpoint database. And put forward the construction principle and management objectives of checkpoint database. At last, this paper briefly introduces and prospects the application of the national multi-source and multi-resolution image checkpoint database.

Robotic photogrammetric underwater inspection of hydropower plants

Hydropower is the most important domestic source of renewable energy in Switzerland, accounting for around 57 % of domestic electricity production. The high intensity usage of hydropower plants results in high costs for regular maintenance and inspection, which are currently carried out through manual inspections by professional divers. The aim of this work is to develop a workflow for underwater photogrammetry with Remotely Operated Vehicles (ROV) to supplement or replace these dangerous and expensive dives. A calibration frame was developed to provide a scale reference for the data collected during the inspection. With different acquisition missions, investigations on camera calibration and 3D reconstruction were performed. The reconstruction of underwater objects was successfully implemented. Well- distributed control points provide accurate results as a point cloud with a sub-centimetre accuracy at object distances of up to 6 m. 80 % of the point cloud differ less than 1 cm from the reference scan.

Dynamic sparse PCA: a dimensional reduction method for sensor data in virtual metrology

Virtual metrology (VM) is the ideal technique for predicting the quality of every wafer and reducing overall inspection costs in semiconductor manufacturing. The data from numerous monitoring sensors in semiconductor production poses challenges in terms of data dimensionality. Thus, dimensionality reduction techniques are required before building the prediction model in VM. Additionally, to reduce the sensor cost, it is crucial to determine the sensors that contribute significantly to the output of the prediction model. Therefore, in this paper, we propose a dimensionality reduction technique called dynamic sparse principal component analysis to establish global sparsity in the first few principal components. The proposed method seeks extracted features that involve a limited number of sensors in the VM to reduce sensor cost and increase the interpretability of the features while reasonably capturing data variability. The experimental results demonstrate that the proposed method is not sensitive to the scale of the problem and can solve the large-scale high-dimensional problems that are commonly encountered in semiconductor manufacturing.

A dynamic risk assessment model based on multidimensional and quantitative inference theory

With the continuous increase in petroleum consumption and the trend towards more refined management, crude oil storage tanks are playing an increasingly crucial role in the petrochemical industry. However, the corrosion issue of crude oil storage tanks has consistently been a major factor limiting their service life, and leaks in crude oil tanks are often attributed to corrosion of the tank bottom. Due to the high cost and complexity associated with tank opening inspections, in this paper, directly observable information and physical quantities are utilized as indirect evidence, combined with Bayesian network, to indirectly determine the probability of tank corrosion. This approach allows for selective internal tank inspections, enabling effective risk assessment while also facilitating cost savings. Some parameters in the Bayesian network model are obtained through a probability estimation model combining expert experience and fuzzy set theory. Finally, the feasibility of the method is demonstrated by citing examples. The current model can offer a relatively reliable reference for evaluating the risks associated with tanks and their ancillary facilities, while also reducing the costs of tank inspections to some extent.

Triple sampling X̄ control chart for gamma process

Monitoring production processes to provide potential customers with quality products while utilising fewer resources and reducing the cost of inspection is a challenge to quality and industrial engineers. Aiming at developing an improved design using reduced sample sizes, this study proposes a triple sampling (TS) X ¯ chart to monitor shifts in the process mean when the quality characteristic of interest follows the gamma distribution. Optimal parameters are determined by employing three optimisation procedures. The detection ability of the TS X ¯ chart is evaluated and contrasted with competing charts via the average number of observations to signal (ANOS), average run length (ARL) and expected ANOS (EANOS) criteria. When the TS X ¯ chart is designed for detecting small and large shifts, it is noticed that the sample sizes at the first, second and third sampling stages are larger for detecting small shifts. Compared to its competitors, the TS X ¯ chart requires fewer observations on the average to signal a shift; hence, it uses resources more efficiently and incurs lower inspection costs. To aid in the implementation of the TS X ¯ chart, an illustrative example of monitoring the wafer process is provided. Overwhelming evidence based on reported results supports the TS X ¯ chart.

Uncertainty Quantification of Data-driven Quality Prediction Model For Realizing the Active Sampling Inspection of Mechanical Properties in Steel Production

Pre-production quality defect inspection is a crucial step in industrial manufacturing, and many traditional inspection strategies suffer from inefficiency issues. This is especially true for tasks such as mechanical performance testing of steel products, which involve time-consuming processes like offline sampling, specimen preparation, and testing. The inspection volume significantly impacts the production cycle, inventory, yield, and labor costs. Constructing a data-driven model for predicting product quality and implementing proactive sampling inspection based on the prediction results is an appealing solution. However, the prediction uncertainty of data-driven models poses a challenging problem that needs to be addressed. This paper proposes an active quality inspection approach for steel products based on the uncertainty quantification in the predictive model for mechanical performance. The objective is to reduce both the sampling frequency and the omission rate on the production site. First, an ensemble model based on improved lower and upper bound estimation is established for interval prediction of mechanical performance. The uncertainty of the specific value prediction model is quantitatively estimated using interval probability distributions. Then, a predictive model for the mechanical performance failure probability is built based on the prediction interval size and probability distribution. By determining an appropriate probability threshold, the trade-off between prediction accuracy and defect detection accuracy (recall rate) is balanced, enabling the establishment of an active sampling strategy. Finally, this functionality is integrated into the manufacturing execution system of a steel factory, realizing a mechanical performance inspection approach based on proactive sampling. The proposed approach is validated using real production datasets. When the probability threshold is set to 30%, the prediction accuracy and recall rate for failure mechanical performance samples are 75% and 100%, respectively. Meanwhile, the sampling rate is only 5.33%, while controlling the risk of omission. This represents a 50% reduction in sampling rate compared to the inspection rules commonly used in actual production. The overall efficiency of product quality inspection is improved, and inspection costs are reduced.

Determining the Most Efficient Method of Strengthening the Reinforced Concrete Columns under Different Loading Levels

Introduction. The method of strengthening the reinforced concrete structures is chosen depending mainly on the cost and timeframe of the performed work. In each specific case, the decision is made depending on the load, type of defects, operational features, etc. Apart from the direct costs of inspection, strengthening project design, materials, lifting and transport services and the cost of strengthening works proper, there is a number of additional costs related to the technological features of the work performed. Among the new methods of strengthening, the method of external strengthening with the composite carbon materials can be particularly emphasised. Compared to the previously developed methods of strengthening with the reinforced concrete and metal, the composite systems are easily transportable and do not incur additional costs. The aim of this study is to determine the method of strengthening most rational for the compressed structures.Materials and methods. The authors have examined three methods of strengthening the compressed reinforced concrete structures. In the frame of the research a numerical experiment has been carried out, in which a reinforced concrete column was strengthened at different strengthening coefficients from 1.1 to 2.0, i.e., from 10 to 100% of strength increase. The estimations of the cost of strengthening the column by three methods of strengthening have been carried out: jacketing using the reinforced concrete, metal and composite materials. After carrying out thirty variants of calculation, a table presenting all specifications and quantities of the materials has been drawn up. At the final stage of the research, the total cost of each method under investigation has been estimated based on the prices of the selling companies.Results. The analysis of strengthening with the reinforced concrete revealed that at most of the strengthening coefficients the achieved structural reinforcement is minimal. Under the high loading levels strengthening with the metal is not economically expedient, as it implies the use of the large-section angles. The composite material systems of external strengthening are very material-consuming at all load increasing coefficients. Taking into account the high cost of materials, this method is basically the least economically expedient.Discussion and conclusion. Based on the results obtained, the efficiency of three most common methods of strengthening the reinforced concrete compressed columns was analysed. Proposals on the rational design of the strengthening systems for the eccentrically compressed reinforced concrete poles under the different eccentricities of load applied have been developed.

Voting based ensemble for detecting visual faults in photovoltaic modules using AlexNet features

This study proposes a novel approach utilizing a voting-based ensemble technique to diagnose visible faults in photovoltaic (PV) modules from aerial images captured by unmanned aerial vehicles (UAVs), leveraging AlexNet features. The proposed method focuses on classifying commonly occurring visual faults such as glass breakage, snail trails, burn marks, delamination and discoloration. Two voting-based ensemble models, a two-class ensemble (combining support vector machines and k-nearest neighbor) and a three-class ensemble (integrating support vector machines, J48, and k-nearest neighbor) were developed and evaluated against individual machine learning classifiers. Results indicate that the two-class ensemble outperforms the three-class ensemble and other individual classifiers, achieving an accuracy of 98.30%. This approach not only enhances fault diagnosis accuracy but also reduces inspection costs and instrument monitoring efforts contributing to the sustainable and efficient operation of PV systems.

Pandora’s Problem with Deadlines

Pandora’s problem is a fundamental model that studies optimal search under costly inspection. In the classic version, there are n boxes, each associated with a known cost and a known distribution over values. A strategy inspects the boxes sequentially and obtains a utility that equals the difference between the maximum value of an inspected box and the total inspection cost. Weitzman (1979) presented a surprisingly simple strategy that obtains the optimal expected utility. In this work we introduce a new variant of Pandora’s problem in which every box is also associated with a publicly known deadline, indicating the final round by which its value may be chosen. This model captures many real-life scenarios where alternatives admit deadlines, such as candidate interviews and college admissions. Our main result is an efficient threshold-based strategy that achieves a constant approximation relative to the performance of the optimal strategy for the deadlines setting.

Attribute statistical process control under nonconstant process deterioration

AbstractA statistical process control (SPC) model is introduced that incorporates sample data on the number of defectives and allows the probability that an assignable cause of variation in each time interval of a finite production process to be nonconstant. A Limited Memory Influence Diagram (LIMID) model is implemented to facilitate the decision in each time interval on whether or not to investigate the potential presence of the assignable cause of variation and restore the process to working condition. The method determines control limits that minimize the costs of inspection, repair, sampling, and operating under the assignable cause of variation. Sample size and sampling intervals can also be adjusted to further reduce the costs of maintaining quality control. This method expands on the capabilities of models that assume that the conditional probability of an assignable cause occurring remains constant throughout the production horizon.

Optimal classification trees with leaf-branch and binary constraints

Using empirical models to predict whether sections within pipes have defects can save inspection costs and, potentially, avoid oil spills. Optimal Classification Tree (OCT) formulations offer potentially desirable combinations of interpretability and prediction accuracy on unseen pipes. Approaches based on powerful state-of-the-art OCT formulations have enabled researchers to solve decision tree problems optimally instead of using traditional sub-optimal greedy approaches. Yet, the recently proposed formulations also have limitations. Some of the most recent formulations require a large number of decision variables and constraints leading to computational inefficiencies. Previous formulations have optimal solutions with undesirable or invalid tree structures which may depend on the particular software implementation. Additionally, some formulations always grow a full tree even when desirable parsimonious tree options are available. This article proposes the Modified Optimal Classification Tree (M-OCT) formulation with novel leaf-branch-interaction constraints, which could stabilize the previous formulation and reduce the chance of invalid tree structures when generating optimal trees. By incorporating the idea of binary encoding of thresholds from a previous article, we reduce the total number of binary variables. We then extend M-OCT to construct a novel formulation called Binary Node Penalty Optimal Classification Tree (BNP-OCT) with binary splits and node complexity constraints, which support efficiency in standard branch-and-cut solvers and prevents the overfitting issue when learning the optimal tree models. We compare the proposed methods with alternatives including standard formulations using 15 standard data sets. In addition, we use 750 test cases to compare the computational stability of pre-existing formulations to those involving the proposed leaf-branch constraints. We demonstrate that the proposed formulation offers advantages in accuracy, computational efficiency, and structural stability. We also describe how the proposed methods are able to achieve 94% classification accuracy on balanced test sets for unseen pipes.

Joint optimization of inspection and condition-based maintenance for a deteriorating product under extended warranty

Condition-based maintenance (CBM) has been widely applied in engineering practice, but it has been rarely integrated into extended warranty policies. When the inspection cost is high, it becomes important to schedule the inspection plan when making CBM decisions. This paper jointly optimizes inspection and CBM for a deteriorating product under extended warranty. The deterioration of the product is characterized as a non-stationary gamma process. At each inspection epoch, the decision maker needs to determine whether to repair the product and when to perform the next inspection. The objective is to simultaneously optimize the inspection policy and the maintenance policy that minimize the warranty servicing cost. The joint optimization problem is formulated by stochastic dynamic programming. The optimal policy is proven to be of a control-limit form. A backward induction algorithm integrating the obtained structural properties is proposed to find the optimization results. A numerical example is conducted to illustrate the effectiveness of the proposed approach. A graphical representation of the obtained policy is provided to support practical maintenance implementation. The comparison with a popular CBM policy confirms the superiority of the proposed policy.

Diagnostic indexes and pharmacological treatment of Alzheimer’s disease

The most sensible and scientific way to control Alzheimer’s disease (AD) is to diagnose and treat it early and change lifestyle and diet, so diagnostic modalities rely primarily on imaging analysis of biomarkers and examination and evaluation of serum and cerebrospinal fluid to determine AD type and disease progression in patients by several characteristic biomarkers (e.g., protein dysfunction, oxidative stress, metal ion vascular disease, mitochondrial population changes). However, there are preferences for different biomarkers in the aspect of inspection cost and patient type. As one of the signature pathologies of AD, β-site amyloid precursor protein (APP) cleaving enzyme 1 (BACE1) plays a crucial role in diagnosis and treatment. The difficult transmission of neurotransmitters in the synaptic gap caused by Aβ deposition is the breakthrough point for treatment and detection, which also means that detecting Aβ’s specific PET ligand in cerebrospinal fluid is more meaningful. For treatments, APP-making BACE1 becomes the key point in the AD clinical treatment. The review summarizes the diagnosis and treatment of AD based on BACE1’s mechanism and the process that causes AD.