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The main purpose of this paper was to evaluate the validation process of food safety control measures.

The validation of control measures has been analyzed at 50 food companies in Serbia. The sample included companies that produce food of both plant and animal origin and have certified food safety management systems. A total of 156 control measures that combat physical hazards (41.6%), followed by microbial hazards (34.0%) and chemical hazards (24.4%), have been analyzed. To enable quantification of the validation protocols, each control measure was assigned a score.

The validation scores showed that the highest level of validation was observed in large companies, as opposed to small and medium-sized companies (p < 0.05). The type of food safety hazards and the food sector did not reveal any statistical differences in-between the scores. The main approach to validating control measures was referring to the technical documentation of equipment used (52.6%), followed by scientific and legal requirements (30.7%). Less than 20% of the analyzed control measures were validated with operational data collected on-site. No mathematical modeling was observed for the sampled food companies. Future steps should include the development of validation guides for different types of control measures and training modules.

This study can serve as an improvement guide for food safety consultants, food safety auditors, certification bodies, inspection services, food technologists and food managers.

This study is one of the first to provide an insight into how food companies validate their control measures to combat microbial, chemical and physical food safety hazards.

This paper aims to study the relationship between the ball grid array (BGA) flip-chip underfilling process parameter and its void formation region.

A set of top-down scanning acoustic microscope images of BGA underfill is collected and void labelled. The labelled images are trained with a convolutional neural network model, and the performance is evaluated. The model is tested with new images, and the void area with its region is analysed with its dispensing parameter.

All findings were well-validated with reference to the past experimental results regarding dispensing parameters and their quantitative regional formation. As the BGA is non-uniform, 85% of the test samples have void(s) formed in the emptier region. Furthermore, the highest rating factor, valve dispensing pressure with a Gini index of 0.219 and U-type dispensing pattern set of parameters generally form a lower void percentage within the underfilling, although its consistency is difficult to maintain.

This study enabled manufacturers to forecast the void regional formation from its filling parameters and array pattern. The filling pressure, dispensing pattern and BGA relations could provide qualitative insights to understand the void formation region in a flip-chip, enabling the prompt to formulate countermeasures to optimise voiding in a specific area in the underfill.

The void regional formation in a flip-chip underfilling process can be explained quantitatively with indicative parameters such as valve pressure, dispensing pattern and BGA arrangement.

This paper aims to present a methodology for the detection and categorisation of metal powder particles that are partially attached to additively manufactured lattice structures. It proposes a software algorithm to process micro computed tomography (µCT) image data, thereby providing a systematic and formal basis for the design and certification of powder bed fusion lattice structures, as is required for the certification of medical implants.

This paper details the design and development of a software algorithm for the analysis of µCT image data. The algorithm was designed to allow statistical probability of results based on key independent variables. Three data sets with a single unique parameter were input through the algorithm to allow for characterisation and analysis of like data sets.

This paper demonstrates the application of the proposed algorithm with three data sets, presenting a detailed visual rendering derived from the input image data, with the partially attached particles highlighted. Histograms for various geometric attributes are output, and a continuous trend between the three different data sets is highlighted based on the single unique parameter.

This paper presents a novel methodology for non-destructive algorithmic detection and categorisation of partially attached metal powder particles, of which no formal methods exist. This material is available to download as a part of a provided GitHub repository.

This research aims to comprehensively analyse the Agri-food supply chain, by identifying the barriers, and considering effective pathways towards a green supply chain. To the best of our knowledge, in the research area of supply chain, the majority of the research is oriented towards the evaluation of barriers and pathways or drivers to the implementation of green supply chain management in the manufacturing industry. The field of Agri-food is among the sectors of concern due to the quantum and basic necessity of the industry.

The contribution of this paper is to develop and evaluate a general framework of the most relevant barriers and possible pathways towards the green Agri-food supply chain, with the help of a hybrid approach combining both the ANP and ELECTRE I methods, via a case study of the fish canning industry.

Furthermore, the study findings will help both academicians and practitioners in developing and evaluating green supply chain frameworks in this area of study.

The results of this study show that the cost of greening the supply in terms of implementing sustainability standard, or advanced technology are the most relevant barriers, and that the social and operational pathways family is among the best effectives pathways.

This study aims to investigate the thermal fracture mechanism of moisture-preconditioned SAC305 ball grid array (BGA) solder joints subjected to multiple reflow and thermal cycling.

The BGA package samples are subjected to JEDEC Level 1 accelerated moisture treatment (85 °C/85%RH/168 h) with five times reflow at 270 °C. This is followed by multiple thermal cycling from 0 °C to 100 °C for 40 min per cycle, per IPC-7351B standards. For fracture investigation, the cross-sections of the samples are examined and analysed using the dye-and-pry technique and backscattered scanning electron microscopy. The packages' microstructures are characterized using an energy-dispersive X-ray spectroscopy approach. Also, the package assembly is investigated using the Darveaux numerical simulation method.

The study found that critical strain density is exhibited at the component pad/solder interface of the solder joint located at the most distant point from the axes of symmetry of the package assembly. The fracture mechanism is a crack fracture formed at the solder's exterior edges and grows across the joint's transverse section. It was established that Au content in the formed intermetallic compound greatly impacts fracture growth in the solder joint interface, with a composition above 5 Wt.% Au regarded as an unsafe level for reliability. The elongation of the crack is aided by the brittle nature of the Au-Sn interface through which the crack propagates. It is inferred that refining the solder matrix elemental compound can strengthen and improve the reliability of solder joints.

Inspection lead time and additional manufacturing expenses spent on investigating reliability issues in BGA solder joints can be reduced using the study's findings on understanding the solder joint fracture mechanism.

Limited studies exist on the thermal fracture mechanism of moisture-preconditioned BGA solder joints exposed to both multiple reflow and thermal cycling. This study applied both numerical and experimental techniques to examine the reliability issue.

This paper aims to investigate the high temperature corrosion behaviour of ZrO₂-reinforced Cr₂O₃ matrix-based composite coatings on ASTM-SA213-T-22 steel at 900°C in molten salt environment. The different coatings were deposited by high velocity oxy fuel (HVOF) method.

Hot corrosion studies were conducted in simulated boiler environment in silicon carbide tube furnace at 900°C for 50 cycles on bare and HVOF-coated boiler steel specimens. Each cycle consisted 50 h of heating in the simulated boiler environment followed by 20 min of cooling in air. The weight change measurements were performed after each cycle to establish the kinetics of corrosion using thermogravimetric technique. X-ray diffraction and scanning electron microscopy techniques were used to analyse the corroded specimens.

The addition of 20 Wt.% ZrO₂ in Cr₂O₃ helped reduce corrosion rate by 89.25% as compared to that of uncoated specimen. The phase analysis revealed the presence of Cr₂O₃ and ZrO₂ phases in composite coating matrix, which may have prevented the base metal from interacting with the corrosive elements present in the highly aggressive environment and thus had increased the resistance to hot corrosion.

It should be mentioned here that high temperature corrosion behaviour of thermally sprayed ZrO₂–Cr₂O₃ composite coatings has never been studied, and to the best of the authors’ knowledge, it is not available in the literature. Hence, present investigation can provide valuable information for application of ZrO₂-reinforced coatings in high temperature fuel combustion environments.

Pores and shrink holes are unavoidable defects in the die-casting mass production process which may significantly influence the strength, fatigue and fracture behaviour as well as the life span of structures, especially if they are subjected to high static and dynamic loads. Such defects should be considered during the design process or after production, where the defects could be detected with the help of computed tomography (CT) measurements. However, this is usually not done in today's mass production environments. This paper deals with the stress analysis of die-cast structural parts with pores found from CT measurements or that are artificially placed within a structure.

In this paper the authors illustrate two general methodologies to take into account the porosity of die-cast components in the stress analysis. The detailed geometry of a die-cast part including all discontinuities such as pores and shrink holes can be included via STL data provided by CT measurements. The first approach is a combination of the finite element method (FEM) and the finite cell method (FCM), which extends the FEM if the real geometry cuts finite elements. The FCM is only applied in regions with pores. This procedure has the advantage that all simulations with different pore distributions, real or artificial, can be calculated without changing the base finite element mesh. The second approach includes the pore information as STL data into the original CAD model and creates a new adapted finite element mesh for the simulation. Both methods are compared and evaluated for an industrial problem.

The STL data of defects which the authors received from CT measurements could not be directly applied without repairing them. Therefore, for FEM applications an appropriate repair procedure is proposed. The first approach, which combines the FEM with the FCM, the authors have realized within the commercial software tool Abaqus. This combination performs well, which is demonstrated for test examples, and is also applied for a complex industrial project. The developed in-house code still has some limitations which restrict broader application in industry. The second pure FEM-based approach works well without limitations but requires increasing computational effort if many different pore distributions are to be investigated.

A new simulation approach which combines the FEM with the FCM has been developed and implemented into the commercial Abaqus FEM software. This approach the authors have applied to simulate a real engineering die-cast structure with pores. This approach could become a preferred way to consider pores in practical applications, where the porosity can be derived either from CT measurements or are artificially adopted for design purposes. The authors have also shown how pores can be considered in the standard FEM analysis as well.

To avoid the high cost of poor quality (COPQ), there is a constant need for minimizing the formation of defects during manufacturing through defect detection and process parameters optimization. This research aims to develop, design and test a smart system that detects defects, categorizes them and uses this knowledge to enhance the quality of subsequent parts.

The proposed system integrates data collected from the deep learning module with the machine learning module to develop and improve two regression models. One determines if set process parameters would yield a defective product while the second model optimizes them. The deep learning model utilizes final product images to categorize the part as defective or not and determines the type of defect based on image analysis. The developed framework of the system was applied to the forging process to determine its feasibility during actual manufacturing.

Results reveal that implementation of such a smart process would lead to significant contributions in enhancing manufacturing processes through higher production rates of acceptable products and lower scrap rates or rework. The role of machine learning is evident due to numerous benefits which include improving the accuracy of the regression model prediction. This artificial intelligent system enhances itself by learning which process parameters could lead to a defective product and uses this knowledge to adjust the process parameters accordingly overriding any manual setting.

The proposed system was applied only to the forging process but could be extended to other manufacturing processes.

This paper studies how an artificial intelligent (AI) system can be developed and used to enhance the yield of good products.

This study aims to evaluate the state of preservation of one of the most famous manuscripts dated back to the 15th century using some analytical techniques to identify the manuscript components, explain its deterioration mechanisms and produce some solutions for conservation processes in future studies.

The analytical techniques used were visual assessment, digital microscope, scanning electron microscope (SEM) with EDX, pH measurement, attenuated total reflection – Fourier transform infrared spectroscopy (ATR/FTIR) and cellulose crystallinity.

Stains, missed parts and scratching were the most common aspects of deterioration. Some insects were observed by digital microscope. The SEM showed that linen fibers and goat skin were used to manufacture paper sheets and leather binding. Energy dispersive X-ray analysis proved that niobium and tantalum were added during the manufacture of paper sheets. Carbon black ink was the main writing material. The other pigments used were cinnabar in red ink, gold color from brass and blue color from lapis lazuli. FTIR analysis proved that some chemical changes were noticed. Low crystallinity of the historical paper was obtained. There was a reduction in the pH value of the historical bookbinding.

The importance of the analytical techniques used to detect the main components, forms and mechanism of deterioration of the studied manuscript. The elements of niobium and tantalum were added to paper sheets, which protected them from deterioration. The insects such as house flies and Sitophilus granarius were found in the manuscripts.

Porosity is a commonly analyzed defect in the laser-based additive manufacturing processes owing to the enormous thermal gradient caused by repeated melting and solidification. Currently, the porosity estimation is limited to powder bed fusion. The porosity estimation needs to be explored in the laser melting deposition (LMD) process, particularly analytical models that provide cost- and time-effective solutions compared to finite element analysis. For this purpose, this study aims to formulate two mathematical models for deposited layer dimensions and corresponding porosity in the LMD process.

In this study, analytical models have been proposed. Initially, deposited layer dimensions, including layer height, width and depth, were calculated based on the operating parameters. These outputs were introduced in the second model to estimate the part porosity. The models were validated with experimental data for Ti6Al4V depositions on Ti6Al4V substrate. A calibration curve (CC) was also developed for Ti6Al4V material and characterized using X-ray computed tomography. The models were also validated with the experimental results adopted from literature. The validated models were linked with the deep neural network (DNN) for its training and testing using a total of 6,703 computations with 1,500 iterations. Here, laser power, laser scanning speed and powder feeding rate were selected inputs, whereas porosity was set as an output.

The computations indicate that owing to the simultaneous inclusion of powder particulates, the powder elements use a substantial percentage of the laser beam energy for their melting, resulting in laser beam energy attenuation and reducing thermal value at the substrate. The primary operating parameters are directly correlated with the number of layers and total height in CC. Through X-ray computed tomography analyses, the number of layers showed a straightforward correlation with mean sphericity, while a converse relation was identified with the number, mean volume and mean diameter of pores. DNN and analytical models showed 2%–3% and 7%–9% mean absolute deviations, respectively, compared to the experimental results.

This research provides a unique solution for LMD porosity estimation by linking the developed analytical computational models with artificial neural networking. The presented framework predicts the porosity in the LMD-ed parts efficiently.