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Literature indicates rapid prototyping (RP) application has become more widespread in design and development of human anatomy models. Practitioners are facing challenges in deployment of RP tools for development of cost-effective medical models, because there are no proven decision support systems in the selection of parameters such as speed, accuracy, materials, and customisation of commercial software. This study aims at alleviating some of these issues by exploring the use of a Genetic Algorithm (GA) approach combined with computer-aided design (CAD) and fused deposition modeling (FDM) techniques. Experiments were conducted using response surface methodology (RSM) to facilitate the optimisation process with build time and model material volume as responses. The validation of the study has been performed with a patella model and the results verified the effectiveness of the proposed RSM-GA approach in the design and development of the anatomical model. The results showed a 27% savings on model material compared to a non-refined model and was deemed satisfactory for practical use as there was a reduction in irregularities from CT data. The study also reveals that the parameter hollow has the largest effect on the responses, followed by the smooth parameter and then the wrap parameter.

A simulation case study was conducted to assess the utility of predictive maintenance and database management for lean maintenance applications within a Petroleum Company. At this company the maintenance of pumps was performed internally where feasible. The study explored a proposed lean strategy on the operations of three departments related to the maintenance of pumping equipment. These departments were, the Pump Shop, Stores Department and Shipping and Receiving. Rockwell Automation’s Arena® simulation software was used to study existing and proposed models of the maintenance system and track the key performance indicators of Flow Time, Waiting Time, and Work-in-Process. Analysis of the performance indicators showed a 76% and a 96% reduction in average Flow Time and Waiting Time, respectively. No difference was determined for Work-in-Process at the 95% confidence interval.

The COVID-19 pandemic has exponentially developed over the past two years into the most prominent crisis that humanity has encountered regarding the stark danger posed to the health and well-being of the everyday citizen. As such, a multitude of safety measures have both been mandated and heavily advised by the governments of the world in a desperate attempt to reduce infection and mortality rates. No method has been more strongly encouraged than the wearing of personal protective equipment (PPE) when coming into human contact, by virtue of the adornment of face masks and face shields. This paper investigates the integration of computer-aided design and engineering (CAD/E) principles for the development of a face shield concept to protect against the COVID-19 virus. The study was orchestrated through the use of the SolidWorks software in generating CAD concepts, employment of the SolidWorks Sustainability tool to evaluate the most viable concept’s lifecycle environmental effects, and the subsequent material redesign of this concept within the sustainability tool to reduce its adverse inflictions on nature. Due to the exceedingly high number of persons using face shields in today’s society to protect against this virus, waste levels upon disposal are equally immense. This paper is to describe the development of the CAD/E concept through the major avenues of sustainability and environmental conservation, and provides an opportunity for sanitation and engineering companies alike to explore a niche in this increasingly expanding market.

PurposeThis paper aims to present a reverse engineering (RE) approach for three-dimensional (3D) model reconstruction and fast prototyping (FP) of broken chess pieces.Design/methodology/approachA case study involving a broken chess piece was selected to demonstrate the effectiveness of the proposed unconventional RE approach. Initially, a laser 3D scanner was used to acquire a (non-uniform rational B-spline) surface model of the object, which was then processed to develop a parametric computer aided design (CAD) model combined with geometric design and tolerancing (GD&T) technique for evaluation and then for FP of the part using a computer numerical controlled (CNC) machine.FindingsThe effectiveness of the proposed approach for reconstruction and FP of rotational parts was ascertained through a sample part. The study demonstrates non-contact data acquisition technologies such as 3D laser scanners together with RE systems can support to capture the entire part geometry that was broken/worn and developed quickly through the application of computer aided manufacturing principles and a CNC machine. The results indicate that design communication, customer involvement and FP can be efficiently accomplished by means of an integrated RE workflow combined with rapid product development tools and techniques.Originality/valueThis research established a RE approach for the acquisition of broken/worn part data and the development of parametric CAD models. Then, the developed 3D CAD model was inspected for accuracy by means of the GD&T approach and rapidly developed using a CNC machine. Further, the proposed RE led FP approach can provide solutions to similar industrial situations wherein agility in the product design and development process is necessary to produce physical samples and functional replacement parts for aging systems in a short turnaround time.

Surface roughness is often used as a measure to identify surface integrity of machined parts. The objective of this study was to optimise part surface roughness by investigating the effects of cutting speed, feed rate, depth of cut and tool nose radius on the surface roughness of Aluminium 6061. A five-level L25 Taguchi orthogonal array was modified to accommodate a four-level process parameter. The optimization was conducted on the prediction model generated by use of Response Surface Methodology (RSM) together with Analysis of Variance (ANOVA), and confirmation test validated the predicted values obtained from the Genetic Algorithm (GA). The best combination of parameters for minimum surface roughness was found to be a cutting speed of 250 m/min, feed rate of 0.03 mm/rev, depth of cut of 0.2 mm and tool nose radius of 0.503 mm. The study proves the efficacy of the GA approach in optimisation of machining parameters for improved surface roughness.

This research study demonstrates the use of machine learning tools for the prediction of dynamic mechanical characteristics of parts produced by the Fused Deposition Modeling (FDM) process. In this regard, I-optimal design of experiments was followed with raster angle, air gap, build orientation and number of contours as independent variables together with natural frequency as the mechanical part characteristic for investigation. Accordingly, a Artificial Neural Network (ANN) model was trained using the Bayesian regularization function. Finally, the trained ANN model was validated by performing multiple confirmation runs which provided predictions generally within 5% accuracy.

In this paper, the flexural strength of 90% Recycled Polyethylene Terephthalate (RPET), Polyethylene Terephthalate Glycol (PETG) and Polycarbonate (PC), are investigated with varying layer thickness, infill percentage and raster angle. Across each material, higher infill percentage resulted in higher flexural strength when loaded, whereas raster angle was the least significant. Using a Genetic Algorithm, the maximum flexural strength of each material was determined to be 48.57 MPa for RPET, 44.22 MPa for PETG and 101.54 MPa for the PC samples. Using these results, practical applications in local industries were investigated. The relatively high flexural strength of the RPET indicates great progress in material science towards creating a strong and sustainable 3D Printing filament. However, a cost-benefit analysis performed on the existing market value indicated that in its current state, the price per strength of RPET is heavily outmatched by PETG as a cheaper option and PC as a stronger option. If the process of creating filament from recycled plastics is refined, RPET filament will become cheaper and more desirable for manufacturing. This can lead to diversifying the local economy as a competitive and eco-friendly option to satisfy consumer needs.

Advances in materials and manufacturing technology and increased competitiveness has led to companies needing to manufacture products more efficiently and rapidly to meet growing market demands. The Additive Manufacturing (AM) process is ideally suited to the fabrication of complex geometries usually impossible with traditional methods furthermore it is capable of fabricating entire assemblies in step without the need for tooling or human involvement. Due to the flexibility and advantages over conventional methods AM has garnered significant attention from the manufacturing sector in meeting market demands. Of the array of available AM processes, Material Extrusion (ME) utilizes a heated thermoplastic filament to construct parts or assemblies via a layer by layer deposition method. This process is not without its own flaws, suffering from accuracy, build time, strength etc., due to the conflicting nature of the process parameters of ME. Therefore, it is critical to understand the shortfalls of ME and classify the factors that directly influence the performance of a part. This paper focuses on the enhancement of the performance measures of the part in terms of build time, material consumption and max torsional stress. This is accomplished by understanding the influence of the process parameters such as raster width, raster angle, part orientation and layer thickness on the performance measures via statistically valid models and optimization methods. This was accomplished using a Box-Behnken design for the experimental design followed by the multi-objective optimization of the empirical models from which the optimum process settings was determined. This study has shown that complex a non-linear relationship exists between the process parameters and performance measures. Results show that the Artificial Neural Network had a better fit when compared to the Response Surface Methodology model and it can be a promising alternative for the prediction and optimization of the ME process.

Manufacturing has long served as the main engine that drives a country’s economic growth and development. Manufacturing engineering (MfgE) education should thus support and help drive advances in the manufacturing sector. In Trinidad and Tobago, there has been much talk about diversifying the economy by strengthening sectors such as manufacturing. However, the bolstering of MfgE education to drive this diversification has largely been ignored. This paper provides an overview of the present structure of manufacturing engineering education in Trinidad and Tobago with an aim of briefly benchmarking its current state. A new concept of science, technology, engineering, mathematics, manufacturing and entrepreneurship (STEMME) education is introduced to draw as much focus on manufacturing and entrepreneurial education as the traditional science, technology, engineering, mathematics (STEM) subjects. A research agenda is then outlined which proposes the future research direction of the authors towards the development of MfgE education to support the sustainable diversification of the economy, specifically the manufacturing sector.

Design complexity in additive manufacturing (AM) is a current issue in the research community, fueled by the well-known phrase “complexity for free”. This statement has promoted the assumption that complex geometries may be achieved without any increase in the cost of production. However, recent research has indicated that increasing shape complexity produces an increase in production costs for the material extrusion process. This challenges the mainstream assumption that AM technologies provide ‘complexity for free’. The AM community requires further investigation of design complexity and its impact on sustainable production when used as a Design for Manufacturing (DfM) tool. This paper proposes a data-driven method which uses design complexity as an AM performance indicator for the material extrusion process. The manufacturing responses included build time (BT), dimensional accuracy (DA) and complexity index (CI). Design space exploration of an automotive air filter model was achieved by varying five critical design features which impact complexity. The study utilized a Face Centered Central Composite Design (FCCCD) of three levels for the design features, comprising 32 experimental models. The optimal model was manufactured based on multi-objective optimization using the MINITAB© response optimizer. This method exploits the design features to achieve target performance and manufacturability. The viability of design complexity as an AM performance indicator was discussed leading to three major improvements to the Product Design and Development (PDD) process for AM. The proposed improvements have the potential to reduce process times and minimize resources, providing a sustainable AM approach for developing regions.

This study highlights the cause and effects of part defects in ABS-Based samples using an additive manufacturing process. The parameters that were investigated include build orientation, infill pattern, number of contours, airgap, road width and annealing as a post-processing parameter. Samples were made, and their compressive strength was tested. Additionally, the tested samples were investigated using optical microscopy and the classification of their defects was done. This study is unique in investigating the effect of stress relief annealing along with build process parameters. Furthermore, the various defects associated with compressive failure in additively manufactured artefacts were categorized and a cause and effect diagram was derived which would enable designers to predict the areas of failure of a part.

Parking challenges and problems are prominent worldwide as well as in Trinidad and Tobago. The capital city of Trinidad, Port Of Spain, is a major area where these challenges are experienced as there are approximately 30% of the population who venture into the city on a daily basis during the week. Worldwide trends involve the utilization of Internet of Things (IoT) platforms expected to alleviate parking challenges by implementation of contemporary parking models such as a smart parking system (SPS). This study investigated feasibility for implementation of a SPS in Port Of Spain. In this study, a Discrete Event Simulation (DES) modelling approach was used to simulate real-life scenarios of the standard parking and a proposed SPS model for a facility chosen in Port Of Spain. These simulation models were executed effectively and results were validated. Verifications were done by comparing how the models worked, to the existing parking facility. Models were then validated using the Chi-squared statistic to determine if there were any statistical difference between developed models and actual statistic. The proposed SPS will reduce driver frustration in finding a vacant parking spot, fuel usage and also reduce environmental degradation.

Local manufacturing small and medium enterprises (SME) have faced continuous challenges in competing with manufacturing firms of scale on the global market. Factors such as the presence of a traditional organisational structure and inefficient utilisation of resources have contributed to the challenges faced by local SMEs in keeping up with regional and international competitors within the manufacturing sector. To investigate the aforementioned challenges, a case study was conducted on a local bottle manufacturing SME which sought to improve their system performance across their Plastic Injection Melding (PIM) operation. Using strategies in the form of lean manufacturing as well as ARENA® simulation software, the challenges identified within their operation were observed and mitigated using key performance indicators. Lean strategies such as Heijunka, Kanban and Six Sigma were utilised as possible methods of reducing waste within the existing simulation model. When comparing the key performance indicators from the simulation, findings highlighted improvements in the Work in Process (WIP) and Waiting Time (WT) by 84.78% and 98.03% for the entire operation. A cost-benefit analysis was carried out to identify the most financially feasible strategy in purchasing the resources that were required for the strategy’s integration into the actual system.