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This paper aims to establish a standardized, quick, reliable and cost-efficient method of quality control (QC) in metal powder bed fusion (PBFM) based on process monitoring data.

Based on destructive testing results that emerged from a statistical investigation on powder bed fusion process exceeding reproducibility of mechanical properties, it was investigated if the generated monitoring data from a concept laser machine allows reliable deductions on resulting mechanical properties of the manufactured specimens.

The application of machine learning on generated melt pool images, under-recognition of destructive testing results, enables enhanced pattern recognition. The generated computational model successfully classified 9,280 unseen layer images by 98.9 per cent accuracy. This finding offers an automated approach to quality control within PBFM.

To the authors knowledge, it is the first time that machine learning has been applied for the purpose of QC in additive manufacturing. The ability of deep convolutional neural networks to recognize patterns, which are imperceptible to the human eye, shows high potential to facilitate activities of QC and to minimize QC-related costs and throughput times. The achieved processing speed for image analyses also points a way for future developments of self-corrective PBFM systems.

The purpose of this paper is to determine the suitability of adopting hybridized multicriteria decision-making models as a decision tool in engineering design. This decision tool will assist design engineers and manufacturers to determine a robust design concept before simulation and manufacturing while all the design features and sub features would have been identified during the decision-making process.

Fuzzy analytical hierarchy process (FAHP) and fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) are hybridized and applied to obtain optimal design of a reconfigurable assembly fixture (RAF) from a set of alternative design concepts. Design features and sub features associated with the RAF are identified and compared using fuzzified pairwise comparison matrices to obtain weights of their relative importance in the optimal design. The FAHP obtained the fuzzy synthetic extent (FSE) values of the design features and sub features. The FSE values are used as weights of the design features and sub features in generating the decision matrix. FTOPSIS and FTOPSIS based on left and right scores were adopted to predict effects of the weights. Results were obtained for normalized and unnormalized weights of the design features and its effects on the relative closeness coefficients of the design alternatives.

The improved performance of the FTOPSIS based on left and right scores is due to the involvement of the left and right scores of weights of the design features in the computation of distances from positive and negative ideal solutions. Embedding the weights of the design features in the normalized decision matrix before estimating the distances of the design concepts from ideal solutions reduces the dependency of the closeness coefficients on the weights of the design features. This also decreases the difference in the final values of the design concepts. In essence, the weights of the design features have an impact in the closeness coefficient. There is reduction in the closeness coefficients of the design concepts due to normalization of the weights of the design features. However, normalizing the weights of the design features did not affect the variations in the final values of the design concept. As the final value of the design concepts can be influenced by the normalized weights of the design features, it can be implied that normalization of weights of the sub features will also affect the decision matrix. The study has been able to proof that hybridizing FAHP and FTOPSIS can produce effective results for decisions on optimal design by the application of FTOPSIS based on left and right scores rather than the general FTOPSIS.

This research develops a hybridized multicriteria decision-making model for decision-making in engineering design. It presents a detailed extension of hybridized FAHP and FTOPSIS based on left and right scores as a useful tool for considering the relative importance of design features and sub features in optimal design selection.

Selection of a suitable location for a quarantine infrastructure represents a complex decision problem, which requires a systematic appraisal of myriads of factors. Quarantine facility in this study is a facility that intends to harbour and treat individuals diagnosed with COVID-19 to prevent the widespread of the virus. COVID-19 is a very contagious pandemic disease, hence, the establishment of critical factors that will embrace the selection of a suitable quarantine facility is of high importance. This paper aims to ascertain the vital few factors that must be considered by decision makers in selecting a suitable quarantine facility.

The aim of this study was achieved through the numerical assessment of identified quarantine location selection factors using the analytical hierarchy process (AHP) and Pareto techniques. The factors, which influences the selection of a suitable quarantine facility for COVID-19 patients were first identified from the literature followed by the pairwise comparison of the factors and random consistency analyses, as well as the ranking of the alternatives based on facility location experts’ opinions.

The study revealed that security, skills availability, cost, readiness, proximity to necessary medical facilities and distance to border, with percentage weight scores of 18%, 16.7%, 15.6%, 10.3%, 9.8% and 6.6% were the critical factors that must be considered during the selection of a quarantine facility for COVID-19 patients.

The results of this paper will help the government and decision makers in locating the quarantine sites for people who tested positive for the COVID-19 virus.

The present study focuses on the application of the decision technique to ascertain critical factors that embrace suitable quarantine facility selection. Combination of AHP and Pareto techniques for prioritization of conflicting factors to be considered in selecting the most suitable location for a quarantine facility has not been reported by existing literature.

In manufacturing, dedicated machine tools and flexible machine tools are failing to satisfy the ever-changing manufacturing demands of short life cycles and dynamic nature of products. These machines are limited when new product designs are introduced. The solution lies in developing responsive machines that can be adjusted or be changed functionally when these change requirements arise. These machines are reconfigurable machines which are becoming the new focus, as they rapidly respond to product variety and volume changes. A sheet metal working machine known as a reconfigurable guillotine shear and bending press machine (RGS&BPM) has been developed. The purpose of this paper is to present a methodology, function-oriented design approach (FODA), which was developed for the design of the RGS&BPM.

The design of the machine is based on the six principles of reconfigurable manufacturing systems (RMSs), namely, modularity, scalability integrability, convertibility, diagnosability and customisability. The methodology seeks to optimise the design process of the RGS&BPM through a design of modules that make up the machine, enable its conversion and reconfiguration. The FODA is focussed on function identification to select the operational function required. Two main functions are recognised for the machine, these being cutting and bending; hence, the design revolves around these two and reconfigurability.

The developed design methodology was tested in the design of a prototype for the reconfigurable guillotine shear and bending press machine. The prototype is currently being manufactured and will be subjected to functional tests once completed. This paper is being presented not only to present the methodology by to show and highlight its practical applicability, as the prototype manufacturers have been enthusiastic about this new approach.

The research was limited to the design methodology for the RGS&BPM, the machine which has been designed to completion using this methodology, with prototype being manufactured.

This study presents critical steps and considerations in the development of reconfigurable machines. The main thrust being to explore the best possibility of developing the machines with dual functionality that will assist in availing the technology to manufacturer. As the machine has been development, the success of the design can be directly attributed to the FODA methodology, among other contributing factors. It also highlights the significance of the principles of RMS in reconfigurable machine design.

The RGS&BM machine is an answer for the small-to-medium enterprises (SMEs), as the machine replaces two machines with one, and the methodology ensures its affordable design. It contributes immensely to the machine availability by eliminating trial and error approaches.

This study presents a new approach to the design of reconfigurable dual machines using principles of RMS. As the targeted market is the SME, it is not limited to that as any entrepreneur may use the machine to their advantage. The design methodology presented contributes to the body of knowledge in dual reconfigurable machine tool design.

The determination of the appropriate site for the location of a research institute represents a multi-criteria problem which requires a scientific approach for decision-making. The research centre in this study is an institute that intends to carry out the state-of-the-art research activities and provide the requisite skills to expedite and optimize the manufacturing of rail cars in South Africa. Hence, the selection of a suitable and conducive location capable of achieving these aforementioned objectives in an effective manner is a problem which requires scientific justification for the allocation of the weights and biases. In light of this, using various decision techniques, this paper aims to establish a suitable framework for the location selection of the research institute which is capable of meeting the short- and long-term objectives of the institute.

This aim was achieved by ascertaining the suitability of potential location alternatives using the factor rating (FR) and centre of gravity (CoG) technique.

The CoG revealed that any location within the longitude of 28.28 and latitude of −25.75 (with a Cartesian coordinate position of 5053.62; 2718.69) is suitable for the research institute, while the result of the FR/weighted score matrix revealed that location J₃ with a weighted score of 72.6% is the most suitable location for the research institute with the longitude of 5053.62 and latitude of 2718.69.

The results of this paper helped decision-makers in locating the given research institute which is currently operational.

The present study is focussed on the application of location decision techniques in the research institute scenario. The combination of FR and CoG techniques for the selection of the most suitable location for a research institute amidst conflicting criteria has not been widely reported by the existing literature.

Reconfigurable vibrating screen (RVS) is an innovative beneficiation machine designed at Tshwane University of Technology, Republic of South Africa (RSA); with adjustable screen structure to ensure sorting, sizing and screening of varying mineral particles (sizes and quantities) demanded by the customers in a cost-effective manner through the screen structure geometric transformation. In order to ensure that this machine is optimally maintained and managed when utilized in surface and underground mining industries, there is a need to establish or ascertain the best maintenance practices that would be used in optimally managing the RVS machine using decision making techniques. In view of this, the purpose of this paper is to ascertain the best maintenance practices that would be used to optimally maintain and manage the RVS machine when used in surface and underground mines.

Decision making techniques such as weighted decision matrix (WDM) and analytical hierarchy process (AHP) were used in this research work to establish the best maintenance practice for optimally maintaining and managing the RVS machine using relevant literature survey on maintenance management systems as well as the different maintenance criteria decision indices obtained from different conventional vibrating screen machine manufacturers and maintenance experts.

Based on the results obtained from the WDM analysis, it was anticipated that e-maintenance (e-M) system embedded with diagnosing and prognosing algorithms; with a cumulative weight score of 2.37 is the best maintenance practice for managing the RVS machine when used in surface mines, while AHP with deeper decision making analysis anticipated that the robotic-driven maintenance (RM) system with an important decision criteria; safety, and a cumulative hierarchy score of 28.6 percent, supported by e-M management system with a cumulative hierarchy score of 17.6 percent are the best maintenance mix that could be used in optimally maintaining and managing the RVS machine, when used in a craggy and hazardous underground mining environment.

To this effect, it could be anticipated that e-M management system (endowed with the ability to detect fault on the machine, diagnose and prognose the different subsystems of the RVS machine and ascertain the reconfiguration time and process of the RVS machine in recovering production loss during the maintenance of the machine as well as meeting customers demand, etc.) is the best maintenance practice for optimally maintaining the RVS machine when utilized in surface mines while both e-M management system and RM management system (endowed with the ability to carry out automated maintenance tasks achievement under little or no maintenance manager intervention) are also anticipated as the best customized maintenance practices mix that could be used in optimally maintaining the RVS machine, when used in dangerous and hazardous underground mining environment.

This maintenance management system evaluation and selection for optimal RVS machine functionality will serve as a useful information to different mining machines (and other related machines) maintenance managers, in selecting the best maintenance management system for ensuring optimal functionality, reliability and maintainability of machines used in their industries.

The need to examine the integrity of infrastructure in the rail industry in order to improve its reliability and reduce the chances of breakdown due to defects has brought about development of an inspection and diagnostic robot.

In this study, an inspection robot was designed for detecting crack, corrosion, missing clips and wear on rail track facilities. The robot is designed to use infrared and ultrasonic sensors for obstacles avoidance and crack detection, two 3D-profilometer for wear detection as well as cameras with high resolution to capture real time images and colour sensors for corrosion detection. The robot is also designed with cameras placed in front of it with colour sensors at each side to assist in the detection of corrosion in the rail track. The image processing capability of the robot will permit the analysis of the type and depth of the crack and corrosion captured in the track. The computer aided design and modeling of the robot was carried out using the Solidworks software version 2018 while the simulation of the proposed system was carried out in the MATLAB 2020b environment.

The results obtained present three frameworks for wear, corrosion and missing clips as well as crack detection. In addition, the design data for the development of the integrated robotic system is also presented in the work. The confusion matrix resulting from the simulation of the proposed system indicates significant sensitivity and accuracy of the system to the presence and detection of fault respectively. Hence, the work provides a design framework for detecting and analysing the presence of defects on the rail track.

The development and the implementation of the designed robot will bring about a more proactive way to monitor rail track conditions and detect rail track defects so that effort can be geared towards its restoration before it becomes a major problem thus increasing the rail network capacity and availability.

The novelty of this work is based on the fact that the system is designed to work autonomously to avoid obstacles and check for cracks, missing clips, wear and corrosion in the rail tracks with a system of integrated and coordinated components.

Reconfigurable machines tools (RMTs) are gaining momentum as the new solutions to customised products in the manufacturing world. The driving force, among others, behind these machines is the part envelope and the part family of products that they can produce. The purpose of this paper is to propose a new class of RMT known as a reconfigurable guillotine shear and bending press machine (RGS&BPM). A part family of products that this machine can produce is developed using hierarchical clustering methodologies. The development of these part families is guided by the relationship of the parts in the family in terms of complexity and geometry.

Part families cannot be developed in isolation, but that process has to incorporate the machine modules used in the reconfiguration process for producing the parts. Literature was reviewed, and group technology principles explored, to develop a concept that can be used to develop the part families. Matrices were manipulated to generate part families, and this resulted in the development of a dendrogram of six possible part families. A software with a graphic user interface for manipulation was also developed to help generate part families and machine modules. The developed concept will assist in the development of a machine by first developing the part family of products and machine modules required in the variable production process.

The developed concepts assist in the development of a machine by first developing the part family of products and machine modules required in the variable production process. The development of part families for the RGS&BPM is key to developing the machine work envelope and modules to carry out the work. This work has been presented to demonstrate the importance of machine development in conjunction with a part family of products that the machine will produce. The paper develops an approach to manufacturing where part families of products are developed prior to developing the machine. The families of products are then used to develop modules that enable the manufacture of the parts and subsequently the size of the machine.

The research was limited to the development of part families for a new RGS&BPM, which is still under development.

The study reflects the development of reconfigurable machines as a solution to manufacturing challenges in terms of group technology approaches adopted in the design phase. It also highlights the significance of the concepts in the reconfigurable machine tool design. The part families define the machine work envelop and its reconfiguration capability.

The success of the research will usher an alternative to smaller players in sheet metal work. It will contribute to the easy development of the machine that will bridge the high cost of machine tools.

The study contributes to the new approach in sheet metal manufacturing where dedicated machines may be substituted by a highly flexible reconfigurable machine that has a dual operation, making the investment for small to medium enterprises affordable. It also contributes to the body of knowledge in reconfigurable machine development and the framework for such activities, especially in developing countries.

The purpose of this study is to develop a new mathematical model and an exact solution method for an assembly line rebalancing problem. When an existing assembly line has to be adapted to a new production context, the line balancing, resources allocation and component management solutions have to be revised. The objective is to minimize the number of modifications to be done in the initial line in order to reduce the time and investment needed to meet new production requirements. The proposed model is evaluated via a computational experiment. The obtained results the efficacy of the proposed method.

This paper develops a new mathematical model and an exact solution method for an assembly line rebalancing problem with the objective to minimize the number of modifications to be done in the initial line to reduce the time and investments needed to meet new production requirements.

The computational experiments show the efficacy of the proposed method.

These reconfiguration costs were analysed for different part-feeding policies that can be adopted in an assembly line.

The purpose of this paper is to present a method to extract corner features for map building purposes in man-made structured underwater environments using the sliding-window technique.

The sliding-window technique is used to extract corner features, and Mechanically Scanned Imaging Sonar (MSIS) is used to scan the environment for map building purposes. The tests were performed with real data collected in a swimming pool.

The change in application environment and the use of MSIS present some important differences, which must be taken into account when dealing with acoustic data. These include motion-induced distortions, continuous data flow, low scan frequency and high noise levels. Only part of the data stored in each scan sector is important for feature extraction; therefore, a segmentation process is necessary to extract more significant information. To deal with continuous flow of data, data must be separated into 360° scan sectors. Although the vehicle is assumed to be static, there is a drift in both its rotational and translational motions because of currents in the water; these drifts induce distortions in acoustic images. Therefore, the bearing information and the current vehicle pose corresponding to the selected scan-lines must be stored and used to compensate for motion-induced distortions in the acoustic images. As the data received is very noisy, an averaging filter should be applied to achieve an even distribution of data points, although this is partly achieved through the segmentation process. On the selected sliding window, all the point pairs must pass the distance and angle tests before a corner can be initialised. This minimises mapping of outlier data points but can make the algorithm computationally expensive if the selected window is too wide. The results show the viability of this procedure under very noisy data. The technique has been applied to 50 data sets/scans sectors with a success rate of 83 per cent.

MSIS gives very noisy data. There are limited sensorial modes for underwater applications.

The extraction of corner features in structured man-made underwater environments opens the door for SLAM systems to a wide range of applications and environments.

A method to extract corner features for map building purposes in man-made structured underwater environments is presented using the sliding-window technique.