Quality Of Industrial Products Research Articles

Research on the Analysis Method of Engine Assembly Quality Based on Data Driven

In the context of the country’s vigorous development of the industrial Internet, the role of big data has become increasingly prominent. Through the mining and analysis of the value of industrial big data, the efficiency and quality of industrial production will be effectively improved. However, the current research lacks direct quantitative research on the correlation between bolt tightening position data and engine quality. Based on the data of a certain production line of Weichai, the SimpleKMeans clustering and Apriori correlation analysis model for the assembly process data and product quality of a certain type of diesel engine is constructed based on the Weka tool. The mining results of the association rule between the bolt tightening data of the eight stations and the cylinder head vibration peak data show that the cylinder head main bolts, cylinder head auxiliary bolts, camshaft gear bolts and connecting rod bolts are closely related to the cylinder head vibration data, and the confidence levels are respectively 0.78, 0.70, 0.63, 0.52. Therefore, when the engine vibration is abnormal, the tightening data of the cylinder head main bolts, cylinder head auxiliary bolts, camshaft gear bolts and connecting rod bolts are detected first to effectively improve the quality and efficiency of the assembly process.

Technological assurance of the quality of deformed steel products in mechanical engineering

The article shows the influence of fibrous structure and non-metallic inclusions on the mechanical properties of steel products. The macrostructure of forging the crankshaft was investigated and the places of exit of the elimination zones to the surface of the car part were established. Based on this, there are proposed ways to improve the quality of deformable steel products in the machine building industry.

Automatic recognition of surface defects for hot-rolled steel strip based on deep attention residual convolutional neural network

Generally, the existence of surface defects in hot-rolled steel strip can lead to adverse influences on the appearance and quality of industrial products. Therefore, it is significant to timely recognize the surface defects for hot-rolled steel strip. In order to improve the efficiency and accuracy of surface defects, a deep neural network, namely, deep attention residual convolutional neural network (DARCNN), is proposed to automatically distinguish 6 kinds of hot-rolled steep strip surface defects. In this network, a channel attention mechanism is combined with residual blocks so that the network can focus on the significant feature channels without information loss. The experimental results show that the accuracy, precision and area under curve (AUC) of DARCNN reach 99.5%, 99.51% and 99.98%, respectively, and the application of DARCNN can improve the accuracy, precision and AUC for surface defect recognition tasks by 1.17%, 1.03% and 0.58%, respectively, which verifies the applicability of deep learning technologies to materials.

Research on Electric Kettle Temperature Control System Based on MATLAB/Simulink

Constant technology control has been widely used in modern agriculture and industry and daily life, control performance affects the safety, the industry productivity and product quality. Based on MATLAB/Simulink software, take the electric kettle as the control object, the simulation model of constant temperature control system is proposed in the paper, the PID controller is used to calibrate the system, and realize constant temperature control of electric kettle. The simulation results show that the PID controller is used to control temperature, which reduces the time of overshooting and adjusting, improves the control accuracy and system reliability, and has practical reference value.

Naive Bayes Algorithm Implementation Based on Particle Swarm Optimization in Analyzing the Defect Product

In the era of progressively more competitive industrial competition, especially in the manufacturing world, it is always required to develop the quality or quality of products and productivity. Each company is compete to win market share. One of the strategies carried out by the company is improving the quality of products and the production process conducted by the company. In the industrial world, product quality and productivity are the keys for success of the production process. Therefore, the purpose of this study is to analyze data for defective products at PT Mane Indonesia with the Particle Swarm Optimization (PSO) and Naïve Bayes Classifier method. The accuracy results using the Naïve Bayes algorithm get a value of 84.38% and an AUC value of 0.953. The results of the PSO-based Naïve Bayes algorithm get a value of 88.62% and AUC value of 0.945. Based on the research which has been performed by using Naïve Bayes based on PSO, it developed a contribution rate of 5,02% in predicting the defected products.

Methods for improving accuracy in measuring deviations from roundness and cylindricity

The problem of improving the technical level and quality of industrial products is now one of the most important tasks. The performance of machining workstation, machines and instruments depends on the quality of production of parts and assemblies and the accuracy of their connections. Precision parameters play a special role in machine tool construction and instrumental production of parts for the aerospace industry. In machines, the value of the torque transmitted by the spindle of the metal cutting machine to the tool, the durability of roller conical bearings, as well as the tightness of connections, the strength of press fits, the correctness of centering on conical surfaces, the accuracy of kinematic pairs and circuits, especially in the spindles of precision CNC machines, which significantly affects the quality of the manufactured products.

A spatiotemporal prediction approach for a 3D thermal field from sensor networks

Thermal fields exist widely in engineering systems and are critical for engineering operation, product quality and system safety in many industries. An accurate prediction of thermal field distribution, that is, acquiring any location of interest in a thermal field at the present and future time, is essential to provide useful information for the surveillance, maintenance, and improvement of a system. However, thermal field prediction using data acquired from sensor networks is challenging due to data sparsity and missing data problems. To address this issue, we propose a field spatiotemporal prediction approach based on transfer learning techniques by studying the dynamics of a 3 D thermal field from multiple homogeneous fields. Our model characterizes the spatiotemporal dynamics of the local thermal field variations by considering the spatiotemporal correlation of the fields and harnessing the information from homogeneous fields to acquire an accurate thermal field distribution in the future. A real case study of thermal fields during grain storage is conducted to validate our proposed approach. Grain thermal field prediction results provide a deep insight of grain quality during storage, which is helpful for the manager of grain storage to make further decisions about grain quality control and maintenance.

Surface Defects Detection Using Non-convex Total Variation Regularized RPCA With Kernelization

Surface defects have an adverse effect on the quality of industrial products, and vision-based defect detection is widely researched due to its objective and stable performance. However, the task is still challenging due to diversified defect types and complex background texture. The robust principal component analysis (RPCA) has proven applicable in defect inspection by regarding nondefective background as the low-rank part and defective area as the sparse part. However, such methods cannot sufficiently detect defects due to complex cluttered background, noise interference, and limited features available. To address these issues, in this article, we proposed an unsupervised surface defect detection method based on nonconvex total variation (TV) regularized RPCA with kernelization, named KRPCA-NTV. Specifically, the kernel method is integrated into RPCA to better handle complex cluttered background lying in a nonstrict low-rank subspace. Furthermore, nonconvex TV regularization is introduced to prevent the noise pixel from being separated into the defect region; meanwhile, nonconvex optimization promotes higher solution accuracy. In addition, the kernel canonical correlation analysis (KCCA) is utilized to fuse complementary features for boosting feature representation ability. To demonstrate the superiority and robustness of the proposed method, we compare it with the state of the art on five defect data sets; the results show that the proposed method outperforms competing methods in terms of accuracy and generalizability.

Design of manually operated multiple-seed planting machine for an Ethiopian environment

The advancement of technology has been influenced and enhances the production rate and quality of industrial and agricultural products. This study aimed to design and manufacture portable and multiple seed planting machines from locally available materials. The paper deals principally with the design and manufacturing process of the seeder machine. The design feature considers the geometrical components, driving power determination, and stress and forces Analysis of each part. Pro-Engineer and solid work software were used for part details and assembly drawing. Materials were selected based on functionality, durability, cost, and local availability. Hence, Sheet metal steel was used for the fabrication of the seed and fertilized boxes, seed, and fertilizer metering disc were fabricated from polyvinylchloride (PVC) and the power transitions elements like transitions shafts and gears also fabricated from round steel bar. The seeder machine was assembled by the permanent joining of the frame (Arc welding), and the remaining components were bolted to each other in their places. Experimental tests have been performed to evaluate the performance and functionality of the seeder. It was observed that the machine had been put (dropped) the seed within the design space gap between the consecutive plants and the required amount of fertilizer were placed with the designed depth.

System of technical regulation and conformity assessment of Ukraine and China. Comparative analysis of legislation on product quality and safety

The article presents the results of monitoring the situation regarding technical regulation and regulatory requirements in Ukraine and China. The essence of asymmetries that are present in the legislative, technical and practical areas of activity in matters of product quality is explained. The analysis of discrepancies between normative requirements of quality and safety of industrial and food production is given. The main obstacles to the free transfer of goods and services between the two countries are indicated. The evolution of the formation of the system of technical regulation in China and the measures that ensured the reform of the system of technical regulation in Ukraine are traced. The results of this monitoring will harmonize the processes of economic policy implementation in the direction of integrating Ukrainian goods into Chinese markets. The main conclusion of the article is the proposals to remove technical barriers to trade between Ukraine and China. This will regulate the harmonization of technical regulation systems in both countries, as well as modernize and increase the competitiveness of the state economy as a whole.

A FRAMEWORK TO DEVELOP INTELLIGENT SYSTEM FOR CAPTURING PRODUCT FEATURES USING OPEN CV TECHNIQUE

This research aims to propose an innovative framework using ISO14649 standard to detect defects in manufactured shaped objects or geometric surfaces of non-linear products of the CNC machine. The significant importance in order to recognize the potential to improve industry product quality inspection and encourage the waste of timing machines and product rejection. Open Computer Vision (Open CV) offers a smart, non-contact measurement and cost-effective technique to fulfil the requirements. The framework depends on the new technique of Open CV, which includes two parts: an intelligent selection of work-piece capturing the image for a particular inspection of the planar interfaces such as the hole, rectangular, pocket one, and the symmetric lighting model comparison approach for measurement of defects in the matched images. The contribution of this study is to build a structure in the computer vision method with a convolution neural network that predicts the classification of the feature for better accuracy and emphasizes the significant characteristics of the image processing technique coupled with experimental data on demanding image datasets and quality inspection measures.

Dynamics Analyses of Rigid-Flexible Coupling of Spot-Welding Robot

With the development of electronics, mechanical automation, computer and other related disciplines, and the improvement of product efficiency and quality in modern industry, welding robots are born and play an increasingly important role in industrial production lines. 6R welding robot is most commonly used in industrial production lines, so the research on 6R welding robot has practical application values. In this paper, MS165 Yaskawa robot is selected as the target robot. SolidWorks software is used to establish the three-dimensional model of Yaskawa robot, which is imported into Adams. Dynamics analyses of the rigid–flexible coupling system of 6R spot welding robot are studied by powerful dynamic simulation functions of Adams. The maximum stress position of the spot-welding robot working under load is also studied, and the maximum stress curve is obtained.

RetinaNet With Difference Channel Attention and Adaptively Spatial Feature Fusion for Steel Surface Defect Detection

Surface defect detection of products is an important process to guarantee the quality of industrial production. A defect detection task aims to identify the specific category and precise position of defect in an image. It is hard to take into account the accuracy of both, which makes it be challenging in practice. In this study, a new deep neural network (DNN), RetinaNet with difference channel attention and adaptively spatial feature fusion (DEA_RetinaNet), is proposed for steel surface defect detection. First, a differential evolution search-based anchor optimization is performed to improve the detection accuracy of DEA_RetinaNet. Second, a novel channel attention mechanism is embedded in DEA_RetinaNet to reduce information loss. Finally, the adaptive spatial feature fusion (ASFF) module is used for an effective fusion of shallow and deep features extracted by convolutional kernels. The experimental results on a steel surface defect data set (NEU-DET) show that DEA_RetinaNet achieved 78.25 mAP and improved by 2.92% over RetinaNet. It has better recognition performance compared with other famous DNN-based detectors.

Reliability Estimation for Zero-Failure Data Based on Confidence Limit Analysis Method

Due to the improvement of the quality of industrial products, zero-failure data often occurs during the reliability life test or in the service environment, and such problems cannot be handled using traditional reliability estimation methods. Regarding the processing and analysis of zero-failure data, the confidence limit assessment methods were proposed by some researchers. Based on the existing research, a confidence limit method set (CLMS) is established in the Weibull distribution for reliability estimation of zero-failure data. The method set includes the unilateral confidence limit method and optimal confidence limit method, so that almost all existing grouping types of zero-failure data can be quickly evaluated, and multiple methods can be used in parallel to deal with the same problem. The effectiveness and high efficiency of the CLMS combined with numerical simulation examples have been verified, and the possibility of analyzing multiple groups of zero-failure data with a confidence limit method suitable for processing single group of zero-failure data is expanded. Finally, the actual effect of the method set is verified by the single group of zero-failure data of rolling bearings and the multiple groups of zero-failure data of torque motors. The results of the example evaluation show that the CLMS has obvious advantages in practical engineering applications.

Sampling method for evaluation and control of industrial product quality

The article develops a sampling method to control and evaluate the quality of industrial products. The method allows for a comprehensive quality evaluation at designing and controlling industrial product manufacture. The method is based on a set of mathematical and statistical models of forming a sampling frame of industrial product parameters. It takes into account the factors of the company’s external and internal environment, control over decision making process (DMP), quality evaluation by a sum of partial indicators of ten groups. The method developed is automated with the help of the software MathCad Prime. The industrial approbation of the method showed its efficiency for the solution of production tasks related to the evaluation and control of industrial product quality at various stages of product and manufacturing life cycles.

Discrimination of tobacco cultivars using SCAR and RAPD markers

Tobacco genetic purity is crucial to maintain the quality of cigarette products in the tobacco industry. To reduce the difficulties in the discrimination of large number of tobacco cultivars in production practice, we developed a two-step identification strategy by using SCAR and RAPD markers. A total of 53 tobacco cultivars were examined in the study. Initially, all the selected cultivars were divided into four groups, each group consisted of seven to seventeen tobacco cultivars based on difference in phenotypes identified by the SCAR markers S4 and S8. Later, in each group, each tobacco cultivar was identified using RAPD fingerprinting by using one to four polymorphic primers, which were selected from 200 random primers. The results showed that all 53 tobacco cultivars could be effectively distinguished by using only two SCAR and seven RAPD markers. The two-step fingerprinting strategy could be used as a convenient and cost-effective tool to discriminate large numbers of tobacco cultivars for production planning in the tobacco industry.

Classification of industrial surface defects based on neural architecture search

Surface defect classification (SDC) is the visual inspection of the surface of an object to identify appearance defects. Efficient and accurate SDC is i mportant for improving the quality of industrial products. A manually designed convolutional neural network (CNN) is traditionally used for SDC. In this study, a simpler SDC scheme with a higher classification accuracy, named NAS-SDC, is developed based on the neural architecture search (NAS) technique. A max-pooling cell based on NASNet is introduced to reduce the search space and the number of network parameters, thus simplifying the candidate operators for the search. Two network architectures are proposed to stack the search candidates or the best cells. The proposed method can be used to automatically design an efficient CNN model for SDC on a specific dataset. Experimental results show that the proposed method can find the best cells in ~11 h using a single graphics processing unit (GPU) and achieves higher classification accuracies (99.98%, 99.8% and 99.26%) than state-of-the-art methods on the Northeastern University (NEU-CLS), DAGM, and bridge defect datasets. The number of network parameters used in the proposed method is only 0.35 M, and the average test time per sample is approximately 61 ms, thus achieving a balance between performance and speed.

A Computer Vision System Applied to The Recognition of Different Capacitors

In this paper, we establish an automation system based on computer vision for the identification of capacitors with different capacitances. The images of capacitors on the industrial assembly line are obtained in real time by using a CCD industrial camera. Then, the obtained images based on RGB model are transformed into HSI color format to avoid the effect of environmental brightness. According to the calculated characteristic hue and saturation values, the established automation system could accurately and effectively identify the capacitances of the capacitors by colors. The identification results would provide accurate information for subsequent robot arm recognition, location, collection, and other operations. By applying to the industrial assembly line, this computer vision system could greatly improve the automation level and product quality of industrial production.

Geometric Positioning and Color Recognition of Greenhouse Electric Work Robot Based on Visual Processing

With the continuous development of science and technology, industrial production technology is also constantly developing, and production efficiency is also constantly improving. Greenhouse electric working robots are industrial production tools with automatic control technology as the core, which affects the quality of industrial products and thus affects the profitability of the factory. According to the set programming work, the greenhouse electric working robot can realize the reproduction production and reduce the workload of the workers. In today’s era, the industrial production steps are more complicated, the production process is more flexible, and the robot’s unchanging posture and movement cannot meet the needs of modern industry, which restricts the development of the factory. In order to better complete the work of industrial robots, it is necessary to study the geometric positioning and color recognition of industrial robots based on machine vision to improve the working efficiency of industrial robots. This paper established an active positioning machine vision system for precise positioning of robot parts greenhouse electric working stations. The matching method using image processing and feature recognition area based on the shape of the binding phase combines the threshold shape criterion to identify object features. The experiments prove that the method can quickly and accurately obtain the object boundaries and centroid calculations and identification data, the robot kinematics combined with real-time motion control of the robot in order to eliminate this error, meet the requirements of the industrial robot self-aligned.

Tool condition monitoring in milling process using multifractal detrended fluctuation analysis and support vector machine

Tool wear will lead to the reduction of surface quality and machining accuracy. Therefore, tool condition monitoring is vital to the improvement of industrial production efficiency and quality. In this paper, first of all, the internal mechanism of the milling process and the performance characteristics of the milling signals were analyzed. It is found that there are different trends between the large and small fluctuations of milling signals in the process of tool wear increasing. This property can be characterized by various parameters of multifractal spectrum to establish the relationship between tool wear and multifractal parameters. By analyzing the changes of multifractal spectrum parameters, the tool wear monitoring can be realized. Then, the multifractal detrended fluctuation analysis (MFDFA) method is used to calculate the mean square error, generalized Hurst exponent, and multifractal spectrum parameters, which are the eigenvectors, and establish its relationship with tool wear. Finally, the tool condition diagnosis is conducted by a support vector machine (SVM). The results show that the tool condition monitoring method of MFDFA combined with SVM is proved to be effective and the multifractal parameters of MFDFA are very sensitive to tool wear.