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This paper aims to present a reverse engineering (RE) approach for three-dimensional (3D) model reconstruction and fast prototyping (FP) of broken chess pieces.

A 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.

The 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.

This 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.

This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.

I-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.

This study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.

The fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.

This study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.

The proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.

The purpose of this paper is to present an integrated design approach for rapid product development (RpD) of a broken product.

Reverse engineering (RE), re‐engineering (ReE) and RpD systems have been incorporated to infuse agile characteristics in the proposed design and development process. A case study involving a broken clutch shoe was selected to demonstrate the efficacy of the proposed integrated approach.

Integration of RE, ReE and RpD presented an unconventional approach towards achieving reduced lead times for design and development of products. Agile characteristics have been manifested for the broken clutch shoe by retrieval of a digitized parametric computer aided design (CAD) model. Moreover, development and selection of an enhanced feasible design (M₃) as well as delivery of the corresponding prototype was accomplished just in one week.

The proposed integrated approach for RpD can provide solutions to similar industrial situations wherein agility in the product design and development process can be infused so that the developed part can be delivered quickly to the customer at the reduced time and costs.

Traditional machining centre selection methods may not guarantee a cost effective solution. Properly trained back‐propagation artificial neural network (BPANN) tend to select reasonable machining centres when presented with machining parameters that they have never seen before. The aim of this paper is to demonstrate the applicability of artificial neural networks (ANNs) to machine centre selection problems.

A three‐layer feedforward back‐propagation supervised training approach is selected to address the machining centre selection problem and demonstrated its potential through an example. This is intended to help readers understand implications on manufacturing system design and future research.

Very limited studies attempted the machining centre selection problem. Feedforward ANN approach has been applied to a wide variety of manufacturing problems. Neural networks have training capability to solve problems that are difficult for conventional computers or human beings. The developed BPANN model has potential to solve the machine centre selection problem with notable consistency and reasonable accuracy.

The BPANN model is an innovative approach fundamentally based on artificial intelligence, which is not directly visible to the user, but is able to solve through a simpler and supervised feedforward back‐propagation training process. The model consists of an input layer, a hidden layer and an output layer. The 18 neurons fixed in the input layer are same as the set of machining centre parameters which are taken directly from the machine tool manufacturer's catalogues. Evidently the proposed three‐layer ANN model has the capability of solving the machine centre selection problem with three hidden neurons for threshold level of 0.9, noise level of 0.05 and tolerance of 0.01.

The work size, weight, travel range, spindle speed range, horse power, feed, accuracy, tool magazine and price are used as machining centre selection parameters. Machining centres' information in the form of 24 patterns along with the desired machining centres' were used to train and test the network.

The purpose of this paper is to provide a comprehensive work flow for the improvement of the Reverse Engineering (RE) process in producing non‐uniform rational B‐splines (NURBS) models from scanned point cloud data. This should become a reliable guide in the creation of desired 3D‐CAD models in order to improve efficiency of downstream operations and further to make decisions regarding quality control.

The paper deals with a detailed investigation of operations in achieving an object's accuracy in the data editing phase and data fitting phase that employs the use of a 3D scanner. A case example involving the ShapeGrabber® AI310 laser scanner was used in digitizing the physical object. Operations considered for investigation at the data editing phase include relaxation, decimation of triangles and sharpening of edges. Contour detection, construct patches, target patch count, grid construction and grid resolution are selected as the operations for investigation in the data fitting phase. Evaluation of the generated digitized models was carried out by performing tests which include 3D Comparisons and Geometric Dimensioning and Tolerance (GD & T) testing.

The process of data editing is considered to be extremely time consuming which requires a high degree of skill in order to carry out the data manipulation steps. For the purpose of investigation, an electrical socket cover was considered as the object for digitization. The study found some contributors to enhance the quality of the digital model that can be used in the first piece inspection. The results indicate that although the operations associated with the data fitting phase affect the overall quality of the digitized model; they are however, limited by whatever the quality achieved at the data editing phase.

The RE work flow described in this research will assist designers and practitioners in improving both the efficiency and effectiveness of design and manufacturing functions.

The data editing and fitting processes are time consuming due to various adjustments necessary in obtaining a NURBS model from the digitized data. Thus, the proposed RE work flow identified the steps to realize the desired CAD models from the point cloud data. Moreover, from this study, practitioners will get a concise overall understanding about which geometrical features need to be adjusted so that the required model can be achieved; instead of the need to develop this procedure by themselves through the process of trial and error.

The main objective of this paper is to introduce the development of a novel feature recognition system for escape route planning. The genetic algorithm (GA) can be a useful tool in solving route planning problems.

A computer‐aided design (CAD) system is proposed to help to predict the best escape route. The system is based on a feature recognition process, which will scan a CAD drawing to determine the centroid points of corridors, and determine the best escape route with the help of GA.

The feature recognition system provides a quick method of determining the available pathways for use in escape route planning.

Factors such as multiple story buildings, number of occupants, or fire spread are not considered. Thus the system will be useful in the initial stages of escape route planning.

The system is limited to perpendicular walls, which should be of the same thickness. Doorways should also be a constant size. Thus the system will be useful in determining where escape routes exist, and attempt to determine the best route based on that information.

This paper shows how legacy drawings from various CAD systems may be reused to plan escape routes, rather than creating new drawings for the same purpose. This is useful to those in escape route planning, since the redrawing of plans using custom software is inefficient.

The current good manufacturing practices (cGMP) are followed in the pharmaceutical sector to ensure that products are consistently produced and controlled to the appropriate quality standards as required by the marketing agencies. The purpose of this paper is to share improvement experiences due to implementation of lean principles combined with cGMP in a pharmaceutical company.

To conduct the study, an intensive review of literature has been conducted. For analysis of the problems existing in the selected production line, the 5‐whys methodology was followed. The current state value stream map (VSM) has been developed after making several company visits and necessary computations. For improvement of the system, various lean strategies have been proposed and the future state VSM developed.

The methodology assisted this company in reducing lead times, cycle times and WIP inventory in the manufacturing process. Furthermore, the storage area was reduced by 38 per cent and production staff was reduced by 50 per cent.

The prime purpose of a lean strategy is to eliminate wastes and to search for continuous improvement in processes. The proposed lean strategies indicated scope for improvement in production efficiency and effectiveness of manufacturing operations.

The guiding principle of cGMP is that quality is built into a product, and not just tested into a finished product. This paper makes several contributions to the pharmaceutical manufacturing company. The proposed lean strategies are considered to be highly valuable for implementation in similar manufacturing industries.

The purpose of this paper is to investigate the effects of liquid carbon dioxide (LCO₂) on grinding of stainless steel.

A factorial experimental approach was used to compare the LCO₂'s performance against grinding under dry air and emulsion coolants.

The experimental results have a great use to practitioners. It was found that under special conditions, LCO₂ proves to be an alternative coolant for grinding of temperature sensitive materials. Furthermore, grinding under LCO₂ conditions produced the lowest tangential force, while the normal forces were close to the values found under emulsion fluid environment. When compared to grinding under dry conditions, LCO₂ coolant was successful in reducing the work piece temperatures. LCO₂ and emulsion conditions inhibit work hardening by reducing material deformation at the grinding zone.

The paper shows that sub‐zero temperature coolants have the ability to bring about lower grinding temperatures than what is typically achieved under conventional fluids.

This paper aims to present a methodology for combining lean manufacturing with current good manufacturing processes (cGMP) in a pharmaceutical company.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Do you believe that cutting back costs leads to inferior product quality? This paper demonstrates that both aims can be achieved by taking lean manufacturing techniques and adding cGMP to build quality into the product as it is produced. The authors demonstrate this by modeling the production line for packaging products in a Trinidadian pharmaceuticals factory. An extensive literature review shows which techniques would be suitable, and how cGMP can be linked to lean manufacturing. The real‐life case study shows how the current‐state value stream map (VSM) can be changed radically into the future‐state VSM in remodeling the cellular production line. So quality can be built into the product while shrinking the space used, the number of staff employed on the production line and the production time. It can be applied in practice throughout the factory and in similar industries.

Pharmaceutical products can be packaged and dispatched much faster, using smaller premises and fewer staff, with a combination of lean manufacturing and cGMP.

High productivity levels will result from low‐cost quality production in a developing country, raising the return on capital and thus increasing social well‐being.

This review introduces a combination of lean manufacturing and cGMP that has not hitherto been applied to pharmaceutical production.