Grading Machine Research Articles

Calibration and Testing of Discrete Element Modeling Parameters for Fresh Goji Berries

This paper aims at the standard grading of fresh goji berries and develops a variable gap-type fresh goji berry grading machine. To establish a complete simulation model, the discrete element parameters of the model were calibrated by a combination of physical experiments and simulation experiments. The outline of the goji berry was extracted by the SFM-CMVS technique, and a goji berry model was obtained using the multi-spherical particle model filling method in the EDEM software. By designing the free-fall, suspension collision, slope slip, and slope rolling experiments, we obtained the discrete element simulation parameters: the inter-particle collision restitution coefficient was 0.158, the collision restitution coefficient of fresh goji berry–silicone rubber material was 0.195, the static friction coefficient of fresh goji berry–silicone rubber material was 0.377, and the rolling friction coefficient of fresh goji berry–silicone rubber material was 0.063. By designing the steepest ascent search and central composite design experiments with the angle of repose (AoR) value obtained from the physical experiment as the target value (31.27°), we determined the inter-particle static friction coefficient to be 0.454 and the inter-particle rolling friction coefficient to be 0.037. Validation tests were conducted on the calibrated discrete element modeling parameters, and the results showed that the established fresh goji berry model and the optimally calibrated parameter combination are effective for discrete element studies on fresh goji berries.

Real-Time Grading of Defect Apples Using Semantic Segmentation Combination with a Pruned YOLO V4 Network.

At present, the apple grading system usually conveys apples by a belt or rollers. This usually leads to low hardness or expensive fruits being bruised, resulting in economic losses. In order to realize real-time detection and classification of high-quality apples, separate fruit trays were designed to convey apples and used to prevent apples from being bruised during image acquisition. A semantic segmentation method based on the BiSeNet V2 deep learning network was proposed to segment the defective parts of defective apples. BiSeNet V2 for apple defect detection obtained a slightly better result in MPA with a value of 99.66%, which was 0.14 and 0.19 percentage points higher than DAnet and Unet, respectively. A model pruning method was used to optimize the structure of the YOLO V4 network. The detection accuracy of defect regions in apple images was further improved by the pruned YOLO V4 network. Then, a surface mapping method between the defect area in apple images and the actual defect area was proposed to accurately calculate the defect area. Finally, apples on separate fruit trays were sorted according to the number and area of defects in the apple images. The experimental results showed that the average accuracy of apple classification was 92.42%, and the F1 score was 94.31. In commercial separate fruit tray grading and sorting machines, it has great application potential.

Automatic Machine System for Traditional Kuih Ros

Mechanization and automation are two technologies that have the potential to propel the country’s agro-food sector forward. The application of the technology will provide a consistent supply of high-quality products while lowering production costs, increasing production rates, and reducing labour costs. In accordance to the current demand, this research aims to develop a conceptual design of automatic machine for traditional malay food called as ‘Kuih Ros’. Manually ‘Kuih Ros’ only can produced one at a time with repeated process of dipping, frying, lifting and dispensing by single operator. The inovation of automatic ‘kuih ros’ revoke the manual production which is less laborious with combined attached mould. While the automatic dispensing system affluence the machine’s user and ensuring consistent quality production of ‘Kuih Ros’. This machine is automatic and only requires single operator to control it. The construction of whole design consists of components such as gears, four DC motors, heating element, aluminium food grade machine body, electrical power supply and arduino as controller. This special focus is given to food industries especially the small-medium enterprises (SMEs) as mechanization will help them for larger scale production, hence opening opportunity for bigger market.

3D printing in fracture treatment : Current practice and best practice consensus.

The use of 3D printing in orthopedic trauma is supported by clinical evidence. Existing computed tomography (CT) data are exploited for better stereotactic identification of morphological features of the fracture and enhanced surgical planning. Due to complex logistic, technical and resource constraints, deployment of 3D printing is not straightforward from the hospital management perspective. As aresult not all trauma surgeons are able to confidently integrate 3D printing into the daily practice. We carried out an expert panel survey on six trauma units which utilized 3D printing routinely. The most frequent indications are acetabular and articular fractures and malalignments. Infrastructure and manpower structure varied between units. The installation of industrial grade machines and dedicated software as well as the use of trained personnel can enhance the capacity and reliability of fracture treatment. Setting up interdisciplinary jointly used 3d printing departments with sound financial and management structures may improve sustainability. The sometimes substantial logistic and technical barriers which impede the rapid delivery of 3D printed models are discussed.

Timber tensile strength in mixed stands of European beech (Fagus sylvatica L.)

The conversion to climate-stable, resilient and productive forests has resulted in an increasing share of mixed stands. Different growth conditions and silvicultural treatments lead to an increased scatter in strength compared to what is expected from monoculture experience. The study (i) quantified the magnitude of variation in strength of European beech timber from stands of different composition and (ii) showed the impact of grading on the characteristic strength value of timber coming from those stands. Strength grading models and machine settings for hardwood tensile classes on over 900 European beech (Fagus sylvatica L.) boards were derived. One model used only the dynamic modulus of elasticity (Edyn), and a more complex model used a knot value in addition. Afterwards, 407 boards from pure beech stands as well as mixed stands of beech with Douglas fir (Pseudotsuga menziesii (Mirb.) Franco), Norway spruce (Picea abies (L.) Karst.), sessile oak (Quercus petraea (Matt.) Liebl.), and Scots pine (Pinus sylvestris L.) were graded and analyzed for their material properties from tension tests parallel to grain. Although a variance components analysis attributed only 4.2% of the variation to mixture, the ungraded timber showed significant strength differences between the pure and the beech-pine stands (65.2 versus 46.6 MPa). The yield of the material graded to the highest class in a class combination was higher in pure beech stands. The required characteristic strength values were mostly met for boards from the pure stands; while boards from the beech-pine mixed stands hardly ever reached the required values. To reduce strength variation and guarantee reliable timber products, strength grading should consider the various growth situations in forests when sampling material for the derivation of settings.

Wavelength selection of multispectral imaging for oil palm fresh fruit ripeness classification.

Multispectral imaging has been recently proposed for high-speed sorting and grading machine vision of fruits. It is a prospective method applied in yet traditional sorting and grading of oil palm fresh fruit bunches (FFB). The ripeness of oil palm FFBs determines the quality of crude palm oil (CPO). Implementation of multispectral imaging for the task needs wavelength selection from hyperspectral datasets. This study aimed to obtain the optimum wavelengths and use them for oil palm FFB classification based on three ripeness levels. We have selected eight optimum wavelengths using principal component analysis (PCA) regression which represented the ripeness levels.

Timber strength variation due to establishment density—can grading cope with it?

Abstract High planting densities in coniferous stands usually lead to advantageous wood properties with high yields for strength graded timber in sawmills. In timber design, however, the focus is on mechanical wood properties, as structural engineers rely on the characteristic values for strength, stiffness and wood density defined for the strength classes. Almost 700 Douglas-fir boards from plots with establishment densities of 1000, 2000 and 4000 trees per hectare were graded with different strength grading machines before the boards were finally tensile tested. Three existing models including both the functional relationship and the class limits were applied to the tensile strength prediction: Modelknot based on knot size and wood density, ModelEdyn based on the dynamic modulus of elasticity (Edyn) only and Modelknot,Edyn based on Edyn and knot size were used to calculate the indicating properties (IPs) IPknot, IPEdyn and IPknot,Edyn. The ratio of achieved and required characteristic values for strength, stiffness and wood density was calculated for various T classes according to the European standard EN 338:2016. The focus was on two questions: (1) Do the samples from different densities meet the characteristic values of the respective T classes? (2) How does the complexity of the grading model affect the characteristic values of sawn timber with raw material planted at different initial densities? The results showed that the stiffness and wood density requirements of the T class profile according to EN 338:2016 were met for boards of all initial densities. Boards from higher and highest densities always exceeded the strength requirements, by on average $+$16 per cent and $+$36 per cent, whereas boards from low establishment densities fell short of the strength requirements by on average −15 per cent. Grading of multiple classes in one run also influenced the characteristic strength value. Applying the strength class combinations T15/T9, the negative deviations from the required strength value of the lower class T9 improved for boards coming from plots with an initial stand density of 1000 trees ha−1 from −14 per cent to −13 per cent (Modelknot), from −12 per cent to −2 per cent (ModelEdyn) and from −10 per cent to $+$6 per cent (Modelknot,Edyn). The non-compliance of the characteristic strength values at low initial density was only in a few cases below the limit value required for the test (90 per cent). The relative distance between the characteristic values within the three densities, nevertheless, was high. Complex grading models resulted in both better yields and low deviations from the requirements.

3D printing in fracture treatment : Current practice and best practice consensus

The use of 3D printing in orthopedic trauma is supported by clinical evidence. Existing computed tomography (CT) data are exploited for better stereotactic identification of morphological features of the fracture and enhanced surgical planning. Due to complex logistic, technical and resource constraints, deployment of 3D printing is not straightforward from the hospital management perspective. As aresult not all trauma surgeons are able to confidently integrate 3D printing into the daily practice. We carried out an expert panel survey on six trauma units which utilized 3D printing routinely. The most frequent indications are acetabular and articular fractures and malalignments. Infrastructure and manpower structure varied between units. The installation of industrial grade machines and dedicated software as well as the use of trained personnel can enhance the capacity and reliability of fracture treatment. Setting up interdisciplinary jointly used 3d printing departments with sound financial and management structures may improve sustainability. The sometimes substantial logistic and technical barriers which impede the rapid delivery of 3D printed models are discussed.

Strength grading of timber in the UK and Ireland in 2021

ABSTRACT This paper summarises the state of the art for strength grading of construction timber grown in the United Kingdom and the Republic of Ireland. It includes the latest approvals based on recent research on spruce, larch and Douglas-fir. It lists the following information along with the primary references: visual grading grades and strength class assignments; grading machines with approved settings for machine control grading; the species, size ranges and strength class combinations covered; and grade determining properties of specific strength classes for the UK and Irish markets. This paper is useful for those grading timber, and those specifying UK and Irish grown timber.

Parametric study of electric discharge machining of titanium grade 2 alloy in distilled water

The current article provides a detailed discussion on the machining of titanium grade 2 alloy in an electric discharge machine (EDM). Copper is taken as a tool electrode, and distilled water is taken as dielectric for machining purpose. Machining with hydrocarbon oil in EDM causes environmental pollution and severe threats to the operator's health. Machining is carried out in distilled water to replace the hazardous hydrocarbon oil. The full factorial experimental design method is adopted to realize the outcome of input parameters on the output parameters. The consequence of current (Ip) and pulse-on time (Ton) are analyzed on response measures viz. material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR). The cause of change in MRR, TWR, and SR trends is discussed. All the response measures show a rising trend with a rise in the value of process parameters. Analysis of Variance is implemented to notice the contribution fraction of each control variable on the response measures. Optical microscopic images are analyzed at lower and higher energy settings to observe the difference in surface morphology. MRR varies from 2.142 to 10.116 mg/min, TWR from 0.251 to 1.356 mg/min, and SR from 4.1 to 9.6 µm, respectively. Overall evaluation criteria are used to obtain input parameters to optimize the performance parameters.

Analysis and optimization of cutting parameters under high-speed machining of Ti-6Al-4V alloy

The objective of the work done is to investigate the effect of machining parameters on the quality of MRR and SR in high-speed machining of low weight titanium grade 5 alloys using statistical method. In the last few years, there has been an increasing trend in the industry towards high-speed machining, to achieve higher productivity without compromising on product quality. In this way, it becomes fundamental to decide the ideal response of machining and cutting conditions that can give predominant outcomes as for as tool execution and surface characteristics. To find out the optimal responses for the Ti grade -5 alloy experiments were done applying L9 orthogonal array by different variables like cutting speed, feed rate, depth of cut, stepover. The ultimate objective is to achieve good surface finish and better metal removal rate by machining at high speed. The results obtained after machining it can be concluded that the cutting speed, stepover have a substantial effect on the machining outcome. The results obtained have been confirmed by confirmatory tests with a value of less than 5%. This shows that Taguchi methodology is reliable and a good specific model for solving the problem of high-speed machining parameters for titanium grade 5 alloys.

Evaluation of a size grading machine for onion

Evaluation of a size grading machine for onion

Development and performance evaluation of dehusked coconut grading machine

Abstract The objective of this research is to develop a dehusked coconut grading machine for coconut cultivators and entrepreneurs. The basis of design is characterized by a tapered belt conveyor and sizing board with openings of increasing aperture which run along the edge of the belt conveyor. Dehusked coconuts are fed onto the tapered belt conveyor where the gravitational force pushes the nut towards the edge until it comes in contact with sizing board. Due to the tangential force, the nut reels along the sizing board, where dehusked coconuts are graded and allowed to fall through aperture according to their sizes. Performance tests indicated that the inclination angle and velocity of the belt significantly affected contamination ratio, grading efficiency and capacity at 5% significance level. The most efficient configuration for fully husked coconut and semi husked was a belt speed of 1.3 m/s with the inclination angle of 15° and belt speed of 1.5 m/s with the inclination angle of 15° respectively. The machine is acceptable by coconut growers and entrepreneurs.

Application of the six sigma DMAIC in quality control of potato chips to reduce production defects

To produce competitive products, Small Medium Enterprises (SMEs), one of which is Agronas as a potato chip producer in Batu City, must continue to improve the quality of their products by minimizing production defects. Quality defects on potato chips occurred in color, crispness, wholeness, and size consistency indicators. This study aimed to identify and analyze the factors causing defects in potato chips and to design alternative improvement strategies. The research method used is Six Sigma Define, Measure, Analyze, Improve dan Control (DMAIC), with a sample size of 15 packs for each variable. The results showed that the main priority of the defined stage for quality improvement was crispness and size consistency with a Critical To Quality (CTQ) value of 80.2%. In the measuring stage, based on the process capability value, the final result is 82.3% which has a value above the industry standard in Indonesia of 69.2%. The result of the analysis of the DPMO value of 177,425 is equivalent to 2.43 sigma. The priority of improvement is the two highest defect levels of crispness and size consistency. Factors causing defects in potato chip crispness include limited manpower, lack of supervision and training, different levels of quality, manual packaging, and less than optimal frying. Alternative improvement strategies are labor controlling from the manager, providing training and process SOPs, supervising the selection of raw materials, and checking packaging equipment regularly. Factors causing defects in potato chips’ size consistency include limited manpower, lack of supervision and training, and the absence of a chip rating machine. Alternative improvements include training and process SOPs, supervision in grading chips, and procurement of grading machines.

Efficiency of Visual and Machine Strength Grading of Sawn Timber with Respect to Log Type

A batch of pine timber sawn from butt, middle and top logs was strength graded with the visual method (classification into grading classes KW—best quality, KS—medium quality, KG—inferior quality and Reject) and with the machine strength grading method—performed with the use of a mobile timber grader (classification into C strength classes). We compared the efficiency of grading classes and strength classes, depending on the type of log from which the material was obtained (butt, middle, top). Next, a strength grading machine was used to measure the modulus of elasticity in bending (MOE) and static bending strength (MOR). The ANOVA confirmed the influence of both the log type (butt, middle, top), the C strength class, and the visual strength grading class on the values of density (DEN) and MOR. Timber density and MOR decreased from the butt log section to the top log section. The ANOVA confirmed the influence of log type on MOE values, but only to a limited extent.

Image-based lime size grading using the comparison ratio of the pixel radius and the actual size of lime fruit

Lime is a commercially important fruit in Thailand whose sale price depends on the fruit’s size; hence, farmers must grade limes by size before distribution. However, as lime grading machines are very expensive and each province has different size grading limits, grading is often performed manually, which is time-consuming and error-prone. Agricultural production systems for automatic selection and grading use image processing techniques for extracting key features. Therefore, this study proposes techniques to extract features of limes and to develop analytical methods for grading them. This method can reduce time and cost, and increase accuracy and flexibility for selecting different lime sizes according to each province’s size criteria. To verify our method, we classified limes according to criteria from four Thailand provinces as sample data in an experiment. The focal image feature was the radius or diameter of the lime and the grading conditions were defined by the maximum comparison ratio of the fruit’s radius in pixels to the measured radius of the actual lime in centimeters. The average grading accuracy was 99.59%, which outperformed that of mechanical grading. The processing time was 1.70 seconds per individual fruit.

Efficient Fruit Grading System Using Spectrophotometry and Machine Learning Approaches

Physical Classification of ripe fruits is an expensive affair in the agriculture industry and human error can lead to inaccurate results. This paper introduces the concept of an intelligent AI-based system using spectrophotometry and computer vision for automated fruit segregation based on their grade. When the fruit is fed into the proposed system, the fruit is identified with 95% accuracy, using a cloud-computing platform provided by Microsoft Azure. After that, using spectroscopy and ensemble machine learning approaches, fruit grade is predicted. This ensemble model is trained using 1366 apple readings taken from Unitec's Apple Sorting and Grading Machine from an industrial plant. With the help of H2O's Driverless.AI, the proposed ensemble provides an overall approximate validation accuracy of 82%. The model is also tested on an unseen test dataset containing real-life spectral values and the accuracy of fruit segregation into different classes peaked at 72%.

FEM SIMULATION ASSESSMENT OF MACHINING TITANIUM GRADE 2 USING TEXTURED TOOL

This work focuses on the 3D numerical investigation of machinability criteria using textured tools for the dry machining of titanium grade 2. For the development of the 3D finite element model, DEFORM-3D (version 10.1) software was used. Dimple patterns were created nearer to the cutting edge region on the rake face of carbide inserts so as to predict their effect and investigate the machinability criteria. Comparison of dimple-textured tool was being made with a conventional tool based on the cutting forces ([Formula: see text], [Formula: see text] and [Formula: see text], cutting temperature, tool wear, effective stress and effective strain. The numerical study revealed that the dimple-patterned tool improved the machinability of titanium grade 2. The cutting forces (tangential cutting force, feed force and radial force) reduced while machining with a dimple-patterned tool as compared to the conventional nontextured tool. There was an enhancement of tool life and a decrease in effective stress and effective strain while dry turning grade-2 titanium alloy using the textured tool.

Estimation of genetic parameters for body weight in a multistage within-family selection process in Pacific white shrimp (Litopenaeus vannamei)

The objective of this study was to estimate the genetic parameters of a Pacific white shrimp population (Litopenaeus vannamei), which used within-family selection (WFS) at different stages in commercial production. The characteristics analyzed were body weight at 12 (W12), 14 (W14), and 21 (W21) weeks in family cages within tanks in a genetic nucleus and weight at harvest at 19 weeks. The WFS was carried out employing a size grading machine. Three linear mixed multivariate models were used for data analysis. The base model (Model I) considered as fixed effects for W12, W14, and W21 the tank and the population density inside the cage; and for the weight at harvest the tank and the sex, and the age covariate, as random effects, the additive and common genetic effects of siblings in all the models were considered. To correct for the effects of WFS, Model II included the initial weight at W14 and W21, while Model III included the selection intensity, respectively. The variance components estimated with Models I and III are essentially the same, while with Model II, there is a decrease in the additive variances for weight at 14 and 21 weeks. The heritabilities are between 0.22 and 0.61, while the significant genetic correlations range from 0.44 to 0.51. On the one hand, the results indicate that the multistage within-family selection processes for body weight can interfere in the estimation of the components of (co) variance. Moreover, on the other hand, the (co)variance components estimated for all weights evaluated in this study allow considering selection responses correlated to harvest weight with different degrees of efficiency; however, it is necessary to carry out studies that value responses from a cost-benefit approach in selection programs of this type in Litopenaeus vannamei.

The Design of the Divider Door for the Orange Grading Machine Based on the Diameter and Color Uses Image Processing

Post-harvest handling will affect the quality of agricultural commodities. One of the main post-harvest activities of orange fruit is grading. Grading is an activity to separate or classify orange fruit based on certain criteria such as size, weight, and color according to the applicable quality standard. This grading activity is important because it will determine the next handling process. Generally, orange grading activities are done manually, this was chosen because the process can be faster, but the manual grading has weakness e.g less efficient and requires more man power. Using the man power in determining product quality are based on experience, guesswork and tend to be subjective because it is in accordance with the perceptions of human being. A grading system and machines that use controls were developed to obtain a more uniform product based on color and size is an alternative to overcome this problem, . Nowadays, the grading process has been developed with an automation system using electronic and mechanical devices so that the results are more effective and accurate. The grading process of orange fruit with a divider door that uses image processing application and a microcontroller will have more precise and objective capability. It will be more profitable than manual grading in the future life. The aim of this research is to design a machine grading system based on the Arduino microcontroller. The grading machine uses an image processing application that reads the diameter value and skin color of the orange, Arduino microcontroller is used to separate oranges based on its quality class using a divider door connected to a stepper motor. The design of the orange grading machine has a size of 1.35 cm × 74 cm and has 4 grading classes, i.e class A, class B, class C and class D. The design of the divider door for the orange grading machine has 2 trapezoid-shaped doors, the length of the first door is 30 cm, the width of the top is 26 cm and the width of the bottom is 10 cm. The length of the second door is 30 cm, the width of the top is 22 cm and the width of the bottom is 10 cm. The percentage of suitability of the results of the grading machine with a dividing door compared to manual grading of oranges is 97%. Image processing error percentage is 14%. The capacity of the orange grading machine model with a dividing doors is 300 items / hour and the efficiency value of the orange grading machine model is 55.66%.