Inspection Costs Research Articles

Drugs quality supervision strategy of different distribution channels in pharmaceutical supply chain.

Aiming at the dual-channel pharmaceutical supply chain, which consists of two distribution channels, offline medical institutions, and online e-commerce platforms, and taking into account the impact of different strategic choices made by relevant stakeholders on the drugs quality of different distribution channels, this article constructs an evolutionary game model involving the participation of government regulator, pharmaceutical enterprises, medical institutions, and pharmaceutical e-commerce companies. The stable equilibrium points of each participant's strategic choices are solved; the stability of strategic combination is analyzed by Lyapunov's first method, and MATLAB 2020b is used for simulation to verify the influence of each decision variable on the strategic choice of different participants. The results show that, first, the purpose of punishment is to ensure the drugs quality in the pharmaceutical supply chain, but when the fine is too high, it will restrain the economic behavior of pharmaceutical enterprises, which is not conducive to the performance of social responsibilities by other relevant participants. Second, the probability that government regulator strictly supervises the pharmaceutical supply chain and the probability that pharmaceutical enterprises provide high-quality drugs are negatively related to their additional cost. Third, whether medical institutions and pharmaceutical e-commerce companies choose inspection is affected by multiple factors such as inspection cost, sales price, and sales cost. Furthermore, when the penalty for non-inspection of pharmaceutical e-commerce companies is greater than the threshold Fm0, it can ensure that it chooses an inspection strategy. Finally, this article puts forward countermeasures and suggestions on the drugs quality supervision of different distribution channels in the pharmaceutical supply chain.

Enhancement and detection of objects in underwater images using image super-resolution and effective object detection model

It is imperative to build an automatic underwater object recognition system in place to reduce the costs of underwater inspections as well as the associated risks. An effective method of detecting underwater objects from underwater images of aquatic after enhancing them using the Image Super-resolution technique is proposed in this study. The proposed approach comprises of two major sections, Underwater Image Enhancement, and Object detection. To enhance the underwater images, a lightweight Reduced Cascading Residual Network (RCARN) is proposed that imposes the Image Super-resolution technique. Later, the enhanced images generated by the RCARN model are supplied for the object detection process, where a significant object detection model, YOLOv3 is employed in this study. To improve its performance, this YOLOv3 is trained on one of the largest datasets, the COCO data, followed by being fine-tuned using enhanced Underwater images. The dataset utilized in this work contains 6 classes of underwater objects namely dolphin, jellyfish, octopus, seahorse, starfish, and turtle. All these images are actual real field images collected from various sources. With this proposed approach, a better overall ACS and mAP of 95.44% and 75.33% are achieved here, which are improved by ~8.75% and ~15%, respectively when compared to actual collected low-resolution images.

A two-step machining and active learning approach for right-first-time robotic countersinking through in-process error compensation and prediction of depth of cuts

• Two-step process method for online compensation and prediction of machining errors • Data-driven models require large training datasets for accurate predictions • Training data is very expensive to obtain for high-value manufacturing processes • Active learning is used to identify the most informative data for model training • Right-first-time robotic machining and reduced inspection costs are achieved Robotic machining processes are characterised by errors arising from the limitations of the industrial robots. These robot-related errors can compromise the overall manufacturing process performance, resulting in final products with dimensions different from the nominal specifications. To avoid accumulation of errors through several manufacturing stages, a quality inspection step is usually performed after the cutting operation. This work presents an innovative two-step manufacturing method for achieving right-first-time characteristics in robotic machining operations through in-process inspection and compensation of the systematic errors, whilst collecting suitable training data for building predictive models. The key idea behind the proposed method is based on the observation that under certain conditions, the robotic machining errors remain largely consistent, and therefore by splitting the process into two similar steps and having an inspection step in between, a prediction and then compensation of the systematic errors would be possible. A Gaussian Process Regression (GPR) framework is applied for the creation of robust process models that predict the post-process inspection result from in-process signal features, with the associated confidence intervals. An active learning algorithm that makes online decisions on the inspection task based on the current confidence of the models, is also proposed. The two-step machining method and the active learning approach were both tested on a robotic countersinking process experiment. The results showed that the in-process inspection and error compensation of the proposed two-step machining method was able to achieve final countersink depths very close to the desired target, confirming the potential for right-first-time robotic machining. In addition, the active learning results highlighted the ability of the algorithm to reduce the number of required post-process inspections, thus saving both time and costs, whilst also identifying novel data relevant for the model training.

Coverage Path Planning for Robotic Quality Inspection With Control on Measurement Uncertainty

The optical scanning gauges mounted on the robots are commonly used in quality inspection, such as verifying the dimensional specification of sheet structures. Coverage path planning (CPP) significantly influences the accuracy and efficiency of robotic quality inspection. Traditional CPP strategies focus on minimizing the number of viewpoints or traveling distance of robots under the condition of full coverage inspection. The measurement uncertainty when collecting the scanning data is less considered in the free-form surface inspection. To address this problem, a novel CPP method with the optimal viewpoint sampling strategy is proposed to incorporate the measurement uncertainty of key measurement points (MPs) into free-form surface inspection. At first, the feasible ranges of measurement uncertainty are calculated based on the tolerance specifications of the MPs. The initial feasible viewpoint set is generated considering the measurement uncertainty and the visibility of MPs. Then, the inspection cost function is built to evaluate the number of selected viewpoints and the average measurement uncertainty in the field of views of all the selected viewpoints. Afterward, an enhanced rapidly exploring random tree algorithm is proposed for viewpoint sampling using the inspection cost function and CPP optimization. Case studies, including simulation tests and inspection experiments, have been conducted to evaluate the effectiveness of the proposed method. Results show that the scanning precision of key MPs is significantly improved compared with the benchmark method.

Optimal joint inspection and mission abort policy for a partially observable system

his paper presents a joint optimization of inspection and condition-based mission abort policy for a partially observable safety-critical system. The system deterioration is modeled by a hidden continuous-time Markov process. Condition monitoring can only reveal partial information about the hidden state of the system. A joint inspection and mission abort policy is developed by using multi-variate Bayesian control approach. The posterior probability of the system being in warning state is updated at each inspection by Bayes’ rule. The mission is aborted and the rescue procedure is initiated once the posterior probability exceeds the control limit. The problem is formulated and solved within the framework of Markov decision process which aims to minimize the expected total cost including inspection, mission failure and system failure cost. Some structural properties of the control limit are presented. Finally, numerical examples are provided to demonstrate the model.

Challenging the throwaway culture in hospitals: Scheduling the mix of reusable and Single-Use bronchoscopes

Optimal material resource planning is crucial to run safe and cost-efficient hospital services. In this article, we investigate a real problem in hospitals, motivated by an environmental and economically inefficient use of disposable, single-use, endoscopes. We develop a mathematical model and create a decision support tool to determine when reusable, multi-use, bronchoscopes should be sent for inspection including information to what extent single-use bronchoscopes can cover the remaining demand. The results show that the proposed approach can contain operational costs which consist of costs for buying single-use devices, inspection costs and reprocessing costs, i.e., sterilization of reusable devices. Our tool can assist hospitals to predict when reusable bronchoscopes should undergo inspection and whether the current inventory of reusable devices is sufficient to cover the demand. Finally, we evaluate the impact of variation in demand on total costs.

Zero-Defect Manufacturing and Automated Defect Detection Using Time of Flight Diffraction (TOFD) Images

Ultrasonic time-of-flight diffraction (TOFD) is a non-destructive testing (NDT) technique for weld inspection that has gained popularity in the industry, due to its ability to detect, position, and size defects based on the time difference of the echo signal. Although the TOFD technique provides high-speed data, ultrasonic data interpretation is typically a manual and time-consuming process, thereby necessitating a trained expert. The main aim of this work is to develop a fully automated defect detection and data interpretation approach that enables predictive maintenance using signal and image processing. Through this research, the characterization of weld defects was achieved by identifying the region of interest from A-scan signals, followed by segmentation. The experimental results were compared with samples of known defect size for validation; it was found that this novel method is capable of automatically measuring the defect size with considerable accuracy. It is anticipated that using such a system will significantly increase inspection speed, cost, and safety.

Domain Feature Mapping with YOLOv7 for Automated Edge-Based Pallet Racking Inspections

Pallet racking is an essential element within warehouses, distribution centers, and manufacturing facilities. To guarantee its safe operation as well as stock protection and personnel safety, pallet racking requires continuous inspections and timely maintenance in the case of damage being discovered. Conventionally, a rack inspection is a manual quality inspection process completed by certified inspectors. The manual process results in operational down-time as well as inspection and certification costs and undiscovered damage due to human error. Inspired by the trend toward smart industrial operations, we present a computer vision-based autonomous rack inspection framework centered around YOLOv7 architecture. Additionally, we propose a domain variance modeling mechanism for addressing the issue of data scarcity through the generation of representative data samples. Our proposed framework achieved a mean average precision of 91.1%.

Analysis of Force Sensing Accuracy by Using SHM Methods on Conventionally Manufactured and Additively Manufactured Small Polymer Parts.

Fabricating complex parts using additive manufacturing is becoming more popular in diverse engineering sectors. Structural Health Monitoring (SHM) methods can be implemented to reduce inspection costs and ensure structural integrity and safety in these parts. In this study, the Surface Response to Excitation (SuRE) method was used to investigate the wave propagation characteristics and load sensing capability in conventionally and additively manufactured ABS parts. For the first set of the test specimens, one conventionally manufactured and three additively manufactured rectangular bar-shaped specimens were prepared. Moreover, four additional parts were also additively manufactured with 30% and 60% infill ratios and 1 mm and 2 mm top surface thicknesses. The external geometry of all parts was the same. Ultrasonic surface waves were generated using three different signals via a piezoelectric actuator bonded to one end of the part. At the other end of each part, a piezoelectric disk was bonded to monitor the response to excitation. It was found that hollow sections inside the 3D printed part slowed down the wave travel. The Continuous Wavelet Transform (CWT) and Short-Time Fourier Transform (STFT) were implemented for converting the recorded sensory data into time–frequency images. These image datasets were fed into a convolutional neural network for the estimation of the compressive loading when the load was applied at the center of specimens at five different levels (0 N, 50 N, 100 N, 150 N, and 200 N). The results showed that the classification accuracy was improved when the CWT scalograms were used.

Single-shot optical surface profiling using extended depth of field 3D microscopy.

The measurement of three-dimensional samples at high speed is essential for many applications, either due to the requirement for measuring samples that change fast over time, or due to the requirement of reducing the scanning time, and therefore inspection cost, in industrial environments. Conventionally, the measurement of surface topographies at high resolution typically requires an axial scanning of the sample. We report the implementation of a technique able to reconstruct surface topographies at high resolution, only from the acquisition of a single camera shot, dropping the need to perform an axial scan. A system prototype is reported and assessed as an ultra-fast optical surface profiler. We propose robust calibration and operation methods and algorithms to reconstruct surface topographies of optically-rough samples, and compare the experimental results with a reference confocal optical profiler.

Breast Surgery Cost Savings Through Surgical Tray Instrument Reduction

IntroductionRemoving unnecessary instruments from surgical trays used in the operating room conserves resources and time. We aimed to assess the cost savings impact of breast surgical tray instrument reduction. MethodsBreast surgeons at a single institution reviewed the standard surgical tray used for lumpectomies and mastectomies and removed underutilized instruments to create a breast-specific tray. This tray was used for all breast surgeries performed throughout the 2019 calendar year. Data for breast-specific tray usage, instrument reprocessing costs, and instrument maintenance costs for inspection, sharpening, aligning, and lubricating were retrospectively obtained. ResultsThe breast-specific tray was reduced from 82 to 65 instruments. The cost of reprocessing each instrument is $1.69. After 30 tray sterilizations, each tray was sent for maintenance at a cost of $2.00 per instrument. With 10 breast-specific trays in circulation, the trays were used a total of 656 times during the calendar year. Each tray was sent for maintenance an average of two times during this time period. Thus, instrument reduction resulted in $18,847 in instrument reprocessing and $680.00 in maintenance savings, with total annual cost savings of $19,527. ConclusionsOptimizing surgical trays by removing unused instruments yields significant cost savings and contributes to improved efficiency in the sterile processing department. As efforts to eliminate wasteful practices and reduce costs within the health care system continue, opportunities remain for standardization of trays across all surgical departments and institutions.

A two-module automated scanning inspection planning methodology for complex surfaces on coordinate measuring machine

Coordinate measuring machine (CMM) plays an essential role in the high precision measurement of complex surfaces. With the emergence of the continuous-contact scanning probe, it becomes possible to inspect large-scale scanning lines rapidly without losing accuracy. Focusing on scanning line distribution and inspection path planning, this paper proposes a two-module scanning inspection planning methodology. Firstly, a Cobweb-Like adaptive Sampling strategy (CLS) is designed to sample form errors by distributing scanning lines in a cobweb-shape mode. Secondly, an inspection path planner is utilized to plan local and global inspection paths. An Improved Ant Colony Optimization (IACO) is proposed to generate time-saving paths for discrete scanning lines. The CLS simulations and scanning experiments are conducted. The methodology can capture large form errors with the minimum inspection cost. Unnecessary transition distances are reduced by 31%-75%. This work enhances the inspection capability of the scanning probe on CMM.

Manufacturer’s contract choice and retailer’s returns management strategy

Managing customer returns is a supply chain issue; manufacturers typically manage them through buyback contracts, while retailers can attempt to recover a product’s salvage value, or sell it as an open-box item. This paper investigates returns management strategies by developing a game theoretical model for a supply chain with a manufacturer (Stackelberg leader) and a retailer (the follower) facing customer returns. The manufacturer chooses either a buyback or a wholesale-price contract. Under a wholesale-price contract, the retailer either salvages or resells returned products. We identify the optimal returns management strategy. We show that each of the three strategies may achieve Pareto improvement for both supply chain members, and for the customers and society. The retailer’s choice to resell or not depends on its inspection cost, or the system efficiency of the supply chain in reselling a returned product relative to that in selling a new product. The manufacturer’s choice of contract depends on whether it can salvage a returned product more efficiently than the retailer, and on the retailer’s unit inspection cost of the returned product. We capture a few identifiable quantities and thresholds that help the manufacturer and the retailer to simplify decision making.

Study on Green Building Promotion Incentive Strategy Based on Evolutionary Game between Government and Construction Unit

Green buildings are considered to be an effective way to save energy, reduce emissions, and protect the environment. As one of the main bodies of green building construction, the construction unit’s willingness to build seriously affects the promotion of green building. Therefore, based on the viewpoints of natural persons and bounded rationality, this study constructs an evolutionary game analysis model, analyzes the impact of local government subsidy policies on the application strategies of construction units, and analyzes the steady-state and selection strategies. The system dynamics model is established using a flow chart, and the simulation results show that, in the long run, increasing the government subsidy and inspection cost cannot improve the application probability of the construction unit. Furthermore, the inspection intensity of the government and the indirect income of the construction unit has a direct influence on the application probability of the construction unit. The results show that the government should adjust the amount of the subsidy reasonably, improve the penalty mechanism, reduce development costs, strengthen publicity, and encourage construction units to actively apply for green buildings, so as to realize the transformation and upgrade of China’s construction industry.

The Study of Artificial Intelligent in Risk-Based Inspection Assessment and Screening: A Study Case of Inline Inspection

Abstract The work reports the systematic approach to the study of artificial intelligence (AI) in addressing the complexity of inline inspection (ILI) data management to forecast the risk in natural gas pipelines. A recent conventional standard may not be sufficient to address the variation data of corrosion defects and inherent human subjectivity. Such methodology undermines the accuracy assessment confidence and is ineffective in reducing inspection costs. In this work, a combination of unsupervised and supervised machine learning and deep learning has profoundly accelerated the probability of failure (PoF) assessment and analysis. K-means clustering and Gaussian mixture models show direct relevance between the corrosion depth and corrosion rate, while the overlapping PoF value is scattered in three clusters. Logistic regression, support vector machine, k-nearest neighbors, and ensemble classifiers of AdaBoost, random forest, and gradient boosting are constructed using particular features, labels, and hyperparameters. The algorithm correctly predicted the score of PoF from 4790 instances and confirmed the 25% metal loss at a location of 13.399 m. The artificial neural network (ANN) is designed with various layers (input, hidden, and output) architecture. It is optimized using an activation function to predict that 74% of the pipeline's anomalies that classified at low-medium and medium-high risk. Furthermore, it provides a quick and precise prediction about the external defects at 13.1 m and requires the personnel to conduct wrapping composite. This work can be used as a standard guideline for risk assessment based on ILI and applies to industry and academia.

How to stimulate Chinese energy companies to comply with emission regulations? Evidence from four-party evolutionary game analysis

Based on the current organization of China's energy regulatory system, we have established a four-party evolutionary game model involving the central energy regulatory agencies, local energy regulatory agencies, energy companies and whistleblowers. The game strategies and behavior evolution of each party have been analyzed. There are at least 25 equilibrium points in the four-party game system, including 16 pure-strategy equilibrium solutions, 6 dual-strategy equilibrium solutions, 2 three-strategy equilibrium solutions, and 1 mixed strategy equilibrium solution. According to the Lyapunov stability condition, we obtain 8 possible conditional stability points and their evolutionarily stable strategy, which represent the possible strategic equilibrium of various stakeholders in the supervision of energy pollution and reporting. We also simulated and analyzed the behavior differences and optimization paths of various stakeholders, and the final evolution results and influencing factors have also been deduced. The main conclusions are: (1) Public supervision and whistleblowing could reduce the opportunity cost of inspection by central energy regulatory agencies. (2) There is a significant positive correlation between energy supervision and the penalty cost on energy companies for non-compliant pollution emission. (3) There is a significant positive correlation between energy supervision and long-term social welfare. Finally, policy implications and corresponding recommendations are provided.

Design and Development of Radius Measurement Gauge for Inner and Outer Crescent Radius of Low-Pressure Pump

Abstract: A coordinate measuring machine, also known as CMM, in CMM machine uses a control panel, software various probes that will measure the geometries of physical objects & give results. But it is also a time-consuming and costlier process of measurement. The primary objective of our project is to reduce inspection time and cost of radius measurement of crescent radius of low-pressure pump housing by using the radius measurement gauge that we designed and developed. In this gauge we kept the workpiece on the gauge Then Steel probe will be in contact directly to the crescent radius which is Linked with linkage and guideways will carry that deflection to the dial gauge, therefore inspection time is reduced by 70%.

An optimization model for fresh-food electronic commerce supply chain with carbon emissions and food waste

ABSTRACT This study contributes to the fresh food supply chain system due to the lack of carbon emissions and food waste reduction that the prior studies neglected. This study examines a fresh-food supply chain system with multiple farmers, a single processor, multi-distributor, customers, and multiple periods. This paper presents a mixed-integer linear programming to optimize total purchasing, inspection, food waste, packing, cold storage, transportation, and carbon emission costs by optimizing product inventories and deliveries. The model is also validated to provide insights into relevant industries. The results show that the total distribution cost from distributor to customers, the total purchasing cost, and total packaging cost are 38.68%; 25.87%; and 16.84%, respectively. In addition, sensitivity analyzes show that the total costs and emissions are significantly affected by the variance in temperature control, carbon punishing cost, and vehicle types. Furthermore, this research can guide the decision-makers by considering the influence of these parameters.

Optimum specification limit coefficient setting based on the smaller-the-better quality characteristics

In this work, the authors propose the application of type I continuous sampling plan (CSP-1 plan) with expected inspection cost, expected replacement cost, expected warranty cost, expected quality loss for conforming product, and expected quality loss for nonconforming product per item. The quality characteristics of product is considered as the normal distribution with smaller-the-better characteristics. The quality loss function of product addresses the quadratic quality loss function. The CSP-1 plan is applied in the process control. The optimal combination of parameters is obtained from the minimum expected total cost per item produced under the specified average outgoing quality limit (AOQL) protection. One needs to have the optimal clearance number, sampling frequency, and specification limit coefficient.

Turn tree into graph: Automatic code review via simplified AST driven graph convolutional network

Automatic code review (ACR), which can relieve the costs of manual inspection, is an indispensable and essential task in software engineering. To deal with ACR, existing work is to serialize the abstract syntax tree (AST). However, making sense of the whole AST with sequence encoding approach is a daunting task, mostly due to some redundant nodes in AST hinder the transmission of node information. Not to mention that the serialized representation is inadequate to grasp the information of tree structure in AST. In this paper, we first present a new large-scale Apache Automatic Code Review (AACR) dataset for ACR task since there is still no publicly available dataset in this task. The release of this dataset would push forward the research in this field. Based on it, we propose a novel Simplified AST based Graph Convolutional Network (SimAST-GCN) to deal with ACR task. Concretely, to improve the efficiency of node information dissemination, we first simplify the AST of code by deleting the redundant nodes that do not contain connection attributes, and thus deriving a Simplified AST. Then, we construct a relation graph for each code based on the Simplified AST to properly embody the relations among code fragments of the tree structure into the graph. Subsequently, in the light of the merit of graph structure, we explore a graph convolution networks architecture that follows an attention mechanism to leverage the crucial implications of code fragments to derive code representations. Finally, we exploit a simple but effective subtraction operation in the representations between the original and revised code, enabling the revised difference to be preferably learned for deciding the results of ACR. Experimental results on the AACR dataset illustrate that our proposed model outperforms the state-of-the-art methods.