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Foundry produces cast metal components and parts for various industries and drives manufacturing excellence all over the world. Assuring quality of these components and parts is vital for the end product quality. The complexity in foundry operations increases with the complexity in designs, patterns and geometry and the quality parameters of the casting processes need to be monitored, evaluated and controlled to achieve expected quality levels.

The literature addresses quality improvement in foundry industry primarily focusing on surface roughness, mechanical properties, dimensional accuracy and defects in the cast parts and components which are often affected by numerous process variables. Primary data are collected from the experts working in sand and investment casting processes. The authors perform machine learning analysis of the data to model the quality parameters with appropriate process variables. Further, cluster analysis using k-means clustering method is performed to develop clusters of correlated process variables for sand and investment casting processes.

The authors identified primary process variables determining each quality parameter using machine learning approach. Quality parameters such as surface roughness, defects, mechanical properties and dimensional accuracy are represented by the identified sand-casting process variables accurately up to 83%, 83%, 100% and 83% and are represented by the identified investment-casting process variables accurately up to 100%, 67%, 67% and 100% respectively. Moreover, the prioritization of process variables in influencing the quality parameters is established which further helps the practitioners to monitor and control them within acceptable levels. Further the clusters of process variables help in analyzing their combined effect on quality parameters of casting products.

This study identified potential process variables and collected data from experts, researchers and practitioners on the effect of these on the quality aspects of cast products. While most of the previous studies focus on a very limited process variables for enhancing the quality characteristics of cast parts and components, this study represents each quality parameter as the function of influencing process variables which will enable the quality managers in Indian foundries to maintain capability and stability of casting processes. The models hence developed for both sand and investment casting for each quality parameter are validated with real life applications. Such studies are scarcely reported in the literature.

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.

Microwave curing (MC) can facilitate rapid concrete repair in cold climates without using conventional accelerated curing technologies which are environmentally unsustainable. Accelerated curing of concrete under MC can contribute to the decarbonisation of the environment and provide economies in construction in several ways such as reducing construction time, energy efficiency, lower cement content, lower carbonation risk and reducing emissions from equipment.

The paper investigates moisture loss and pore properties of six cement-based proprietary concrete repair materials subjected to MC. The impact of MC on these properties is critically important for its successful implementation in practice and current literature lacks this information. Specimens were microwave cured for 40–45 min to surface temperatures between 39.9 and 44.1 °C. The fast-setting repair material was microwave cured for 15 min to 40.7 °C. MC causes a higher water loss which shows the importance of preventing drying during MC and the following 24 h.

Portland cement-based normal density repair mortars, including materials incorporating pfa and polymer latex, benefit from the thermal effect of MC on hydration, resulting in up to 24% reduction in porosity relative to normal curing. Low density and flowing repair materials suffer an increase in porosity up to 16% due to MC. The moisture loss at the end of MC and after 24h is related to the mix water content and porosity, respectively.

The research on the application of MC for rapid repair of concrete is original. The research was funded by the European commission following a very rigorous and competitive review process which ensured its originality. Original data on the parameters of porosity and moisture loss under MC are provided for different generic cementitious repair materials which have not been studied before. Application of MC to concrete construction especially in cold climates will provide environmental, economic and energy benefits.

This study aims to develop geopolymer concrete (GPC) using manufactured sand (M-sand) and recycled concrete aggregate (RCA) under different curing conditions. GPC is a sustainable construction material developed with industrial waste products such as fly ash to eliminate the use of cement in the production of concrete. GPC requires heat curing for the attainment of early age strength. The development of GPC under heat curing conditions is a hard process in practice. To overcome such circumstances, an attempt was made to develop the GPC under different curing conditions with the aid of coarse aggregate (CA) and RCA. The influence of different curing conditions on strength gain and microstructural characteristics of GPC is investigated. Mechanical properties of GPC such as compressive strength, tensile strength, flexural strength and elastic modulus are reported and discussed.

This study focuses on the assessment of mechanical and microstructure characterization of eco-efficient GPC developed with natural CA and RCAs. The required optimum quantity of binder, alkali activator, alkaline liquid to binder ratio and aggregates was determined by appropriate trials. Three types of curing methods, namely, ambient, oven and water, were used for the development of GPC mixes. Following the properties of RCA, it is realistic to substitute up to 40% of coarser aggregates as the resulting aggregate mix falls within the requirements of the analyzed mix.

Special attention is required for the mix with RCA because the mix’s consistency is affected by the high water absorption of the RCA mix. GPC specimens cured at ambient and water conditions exhibited marginal variation in the compressive strength for both CA and RCA. The compressive strength of GPC mixes prepared with RCA was marginally higher than that of the GPC made with CA under different curing regimes. RCA can be used as a sustainable material in lieu of CA in GPC.

The main significance of this research work is to develop the optimal mix design with appropriate mix proportion. The present study proposes a satisfactory methodology that enhances the mechanical strength of GPC as the guidelines are not available in the standards to address this problem. Effective use of waste materials such as fly ash and recycled aggregate for the development of GPC is another major research focus in the proposed investigation.

As an advanced manufacturing method, additive manufacturing (AM) technology provides new possibilities for efficient production and design of parts. However, with the continuous expansion of the application of AM materials, subtractive processing has become one of the necessary steps to improve the accuracy and performance of parts. In this paper, the processing process of AM materials is discussed in depth, and the surface integrity problem caused by it is discussed.

Firstly, we listed and analyzed the characterization parameters of metal surface integrity and its influence on the performance of parts and then introduced the application of integrated processing of metal adding and subtracting materials and the influence of different processing forms on the surface integrity of parts. The surface of the trial-cut material is detected and analyzed, and the surface of the integrated processing of adding and subtracting materials is compared with that of the pure processing of reducing materials, so that the corresponding conclusions are obtained.

In this process, we also found some surface integrity problems, such as knife marks, residual stress and thermal effects. These problems may have a potential negative impact on the performance of the final parts. In processing, we can try to use other integrated processing technologies of adding and subtracting materials, try to combine various integrated processing technologies of adding and subtracting materials, or consider exploring more efficient AM technology to improve processing efficiency. We can also consider adopting production process optimization measures to reduce the processing cost of adding and subtracting materials.

With the gradual improvement of the requirements for the surface quality of parts in the production process and the in-depth implementation of sustainable manufacturing, the demand for integrated processing of metal addition and subtraction materials is likely to continue to grow in the future. By deeply understanding and studying the problems of material reduction and surface integrity of AM materials, we can better meet the challenges in the manufacturing process and improve the quality and performance of parts. This research is very important for promoting the development of manufacturing technology and achieving success in practical application.

The influence of using magnetic water instead of tap water in the mechanical properties of the concrete exposed to elevated temperatures was investigated. Two concrete mixes were used and cast with the same ingredients. Tap water was used in the first mix and magnetic water was used in the second mix. A total of 48 specimens were cast and divided as follows: 16 cylinders for the concrete compressive strength test (8 samples for each mix), 16 cylinders for the splitting tensile strength (8 specimens for each mix) and 16 beams to test the influences of magnetized water on the flexural strength of concrete (8 specimens for each mixture). Specimens were exposed to temperatures of (25 °C, 200 °C, 400 °C and 600 °C). The experimental results showed that magnetic water highly affected the mechanical properties of concrete. Specimens cast and curried out with magnetic water show higher compressive strength, splitting tensile strength and flexural strength compared to normal water specimens at all temperatures. The relative strength range between the two types of water used was 110–123% for compressive strength and 110–133% for splitting strength. For the center point loading test, the relative flexural strength range was 118–140%. The use of magnetic water in mixing concrete contribute to a more complete hydration process.

Experimental study was carried out on two concrete mixes to investigate the effect of magnetic water. Mix#1 used normal water as the mixing water, and Mix#2 used magnetic water instead of normal water. After 28 days, all the samples were taken out of the tank and left to dry for seven days, then they were divided into different groups. Each group was exposed to a different temperature where it was placed in a large oven for two hours. Three different tests were carried out on the samples, these tests were concrete compressive strength, flexural strength and splitting tensile strength.

Exposure of concrete to high temperatures had a significant influence on concrete mechanical properties. Specimens prepared using magnetic water showed higher compressive strength at all temperature levels. The use of magnetic water in casting and curing concrete can increase the compressive strength by 23%. Specimens prepared using magnetic water show higher splitting tensile strength at all temperatures up to 33%. The use of magnetic water in casting and curing can strengthen and increase concrete resistance to high temperatures, a significant enhancement in flexural strength at all temperatures was found with a value up to 40%.

Previous research proved the advantages of using magnetic water for improving the mechanical properties of concrete under normal conditions. The potential of using magnetic water in the concrete industry in the future requires conducting extensive research to study the behavior of magnetized concrete under severe conditions to which concrete structures may be subjected to. These days, there are attempts to obtain stronger concrete with high resistance to harsh environmental conditions without adding new costly ingredients to its main mixture. No research has been carried out to investigate the effect of magnetic water on the mechanical properties of concrete exposed to elevated temperature. The main objective of this study is to evaluate the effect of using magnetic water on the mechanical properties of hardened concrete subjected to elevated temperature.

The purpose of this study is to prepare a robust superhydrophobic coating on concrete substrate with remarkable chemical and mechanical durability through “all-covalent” strategy.

Amino-modified silica nano/micro-particles were prepared through two synthetic steps. “All-covalent” strategy was introduced to prepare a robust superhydrophobic coating on concrete surface via a “all-in-one” dispersion and a simple spraying method. The successful construction of the products was confirmed by Fourier transform infrared spectroscopy, water contact angles (WCA), X-ray photoelectron spectroscopy (XPS) and scanning electron microscope (SEM). The concrete protective properties were verified by solution immersion test, pull-off test and rapid chloride migration coefficient test. The mechanical durability was tested by falling sand impact.

Hierarchical structures combined with the low-surface-energy segments lead to typically superhydrophobic coating with a WCA of 156° and a sliding angle of 1.3°. The superhydrophobic coating prepared through “all-covalent” strategy not only improves chemical and mechanical durability but also achieves higher corrosion and wear resistance than the comparison sample prepared by physically blending strategy. More importantly, the robust superhydrophobic coating showed excellent adhesion and protective performance of concrete engineerings.

This new “all-covalent” superhydrophobic coating could be applied as a concrete protective layer with properties of self-cleaning, anti-graffiti, etc.

Introduction of both silica nanoparticles and silica microparticles to prepare a robust superhydrophobic coating on concrete surface through “all-covalent” strategy has not been systematically studied previously.

Damage to reinforced concrete (RC) structural elements is inevitable. Such damage can be the result of several factors, including aggressive environmental conditions, overloading, inadequate design, poor work execution, fire, storm, earthquakes etc. Therefore, repairing and strengthening is one way to improve damaged structures, so that they can be reutilized. In this research, the use of an ultra high-performance fibre-reinforced concrete (UHPFRC) layer is proposed as a strengthening material to rehabilitate damaged-RC beams. Different strengthening schemes pertaining to the structural performance of the retrofitted RC beams due to the flexural load were investigated.

A total of 13 normal RC beams were prepared. All the beams were subjected to a four-point flexural test. One beam was selected as the control beam and tested to failure, whereas the remaining beams were tested under a load of up to 50% of the ultimate load capacity of the control beam. The damaged beams were then strengthened using a UHPFRC layer with two different schemes; strip-shape and U-shape schemes, before all the beams were tested to failure.

Based on the test results, the control beam and all strengthened beams failed in the flexural mode. Compared to the control beam, the damaged-RC beams strengthened using the strip-shape scheme provided an increase in the ultimate load capacity ranging from 14.50% to 43.48% (or an increase of 1.1450 to 1.4348 times), whereas for the U-shape scheme beams ranged from 48.70% to 149.37% (or an increase of 1.4870–2.4937 times). The U-shape scheme was more effective in rehabilitating the damaged-RC beams. The UHPFRC mixtures are workable, as well easy to place and cast into the formworks. Furthermore, the damaged-RC beams strengthened using strip-shape scheme and U-shape scheme generated ductility factors of greater than 4 and 3, respectively. According to Eurocode8, these values are suitable for seismically active regions. Therefore, the strengthened damaged-RC beams under this study can quite feasibly be used in such regions.

Observations of crack patterns were not accompanied by measurements of crack widths due to the unavailability of a microcrack meter in the laboratory. The cost of the strengthening system application were not evaluated in this study, so the users should consider wisely related to the application of this method on the constructions.

Rehabilitation of the damaged-RC beams exhibited an adequate structural performance, where all strengthened RC beams fail in the flexural mode, as well as having increment in the failure load capacity and ductility. So, the used strengthening system in this study can be applied for the building construction in the seismic regions.

Aside from equipment, application of this strengthening system need also the labours.

The use of sand blasting on the surfaces of the damaged-RC beams, as well as the application of UHPFRC layers of different thicknesses and shapes to strengthen the damaged-RC beams, provides a novel innovation in the strengthening of damaged-RC beams, which can be applicable to either bridge or building constructions.

This chapter argues that the concept of ‘mass supervision’, and indeed the concept of ‘mass incarceration’ from which it derives, is both quantitatively and qualitatively indeterminate when applied outside of the context of the US. However, the qualitative indeterminacy of mass supervision only holds so long as one treats the word ‘mass’ as being an analogy to mass consumption. This chapter therefore considers an alternative construction of ‘mass’ punishment in terms of mass production. Comparing the philosophies of production associated with Henry Ford and William Morris with the scholarship of Michel Foucault and Fergus McNeill reveals that mass supervision can authentically claim to be qualitatively ‘massive’, given the bespoke and one-on-one nature of traditional supervision. It is thus possible to speak coherently of ‘mass supervision’ in an international context, although this negative conception of a problem invites questions about the best solution that it generally leaves open.

The artificial intelligence (AI) based total productive maintenance (TPM) condition based maintenance (CBM) approach through Industry 4.0 transformation can well predict the breakdown in advance to eliminate breakdown.

Meeting the customer requirement as per the delivery schedule with the existing resources are always a big challenge in industries. Any catastrophic breakdown in the equipment leads to increase in production loss, damage to machines, repair cost, time and affects delivery. If these breakdowns are predicted in advance, the breakdown can be addressed before its occurrence and the demand supply chain can be met. TPM is one of the essential operational excellence tool used in industries to utilize the existing resources of a plant in a optimal way. The conventional time based maintenance (TBM) and CBM approach of TPM in Industry 3.0 is time consuming and not accurate enough to achieve zero down time.

The proposed AI and IIoT based TPM is achieved in a digitalized data oriented platform to monitor and control the health status of the machine which may reduce the catastrophic breakdown by 95% and also improves the quality rate and machine performance rate. Based on the identified key signature parameters related to major breakdown are measured using the sensors, digitalised by programmable logic controller (PLC) and monitored by supervisory control and data acquisition (SCADA) and predicted in server or cloud.

Long short term memory based deep learning network was developed as a regression forecasting model to predict the remaining useful life RUL of the part or assembly and based on the predictions, corrective action has been implemented before the occurrence of breakdown. The reliability and consistency of the proposed approach are validated and horizontally deployed in similar machines to achieve zero downtime.