Review of Intelligence for Additive and Subtractive Manufacturing: Current Status and Future Prospects

Additive manufacturing (AM), an enabler of Industry 4.0, recently opened limitless possibilities in various sectors covering personal, industrial, medical, aviation and even extra-terrestrial applications. Although significant research thrust is prevalent on this topic, a detailed review covering the impact, status, and prospects of artificial intelligence (AI) in the manufacturing sector has been ignored in the literature. Therefore, this review provides comprehensive information on smart mechanisms and systems emphasizing additive, subtractive and/or hybrid manufacturing processes in a collaborative, predictive, decisive, and intelligent environment. Relevant electronic databases were searched, and 248 articles were selected for qualitative synthesis. Our review suggests that significant improvements are required in connectivity, data sensing, and collection to enhance both subtractive and additive technologies, though the pervasive use of AI by machines and software helps to automate processes. An intelligent system is highly recommended in both conventional and non-conventional subtractive manufacturing (SM) methods to monitor and inspect the workpiece conditions for defect detection and to control the machining strategies in response to instantaneous output. Similarly, AM product quality can be improved through the online monitoring of melt pool and defect formation using suitable sensing devices followed by process control using machine learning (ML) algorithms. Challenges in implementing intelligent additive and subtractive manufacturing systems are also discussed in the article. The challenges comprise difficulty in self-optimizing CNC systems considering real-time material property and tool condition, defect detections by in-situ AM process monitoring, issues of overfitting and underfitting data in ML models and expensive and complicated set-ups in hybrid manufacturing processes.

pH-Sensitive nanocarrier assisted delivery of adenosine to treat osteoporotic bone loss

Bone tissue undergoes continuous remodeling via osteoclast-mediated bone resorption and osteoblast-mediated bone formation. An imbalance in this process with enhanced osteoclastic activity can lead to excessive bone resorption, resulting in bone thinning. Once activated, osteoclasts bind to the bone surface and acidify the local niche. This acidic environment could serve as a potential trigger for the delivery of therapeutic agents into the osteoporotic bone tissue. To this end, we developed a pH-responsive nanocarrier-based drug delivery system that binds to the bone tissue and delivers an osteoanabolic molecule, adenosine. Adenosine is incorporated into a hyaluronic acid (HA)-based nanocarrier through a pH-sensitive ketal group. The HA-nanocarrier is further functionalized with alendronate moieties to improve binding to the bone tissues. Systemic administration of the nanocarrier containing adenosine attenuated bone loss in ovariectomized mice and showed comparable bone qualities to that of healthy mice. Delivery of osteoanabolic small molecules that can contribute to bone formation and inhibit excessive osteoclast activity by leveraging the tissue-specific milieu could serve as viable therapeutics for osteoporosis.

The Efficacy of Machine-Learning-Supported Smart System for Heart Disease Prediction

The disease may be an explicit status that negatively affects human health. Cardiopathy is one of the common deadly diseases that is attributed to unhealthy human habits compared to alternative diseases. With the help of machine learning (ML) algorithms, heart disease can be noticed in a short time as well as at a low cost. This study adopted four machine learning models, such as random forest (RF), decision tree (DT), AdaBoost (AB), and K-nearest neighbor (KNN), to detect heart disease. A generalized algorithm was constructed to analyze the strength of the relevant factors that contribute to heart disease prediction. The models were evaluated using the datasets Cleveland, Hungary, Switzerland, and Long Beach (CHSLB), and all were collected from Kaggle. Based on the CHSLB dataset, RF, DT, AB, and KNN models predicted an accuracy of 99.03%, 96.10%, 100%, and 100%, respectively. In the case of a single (Cleveland) dataset, only two models, namely RF and KNN, show good accuracy of 93.437% and 97.83%, respectively. Finally, the study used Streamlit, an internet-based cloud hosting platform, to develop a computer-aided smart system for disease prediction. It is expected that the proposed tool together with the ML algorithm will play a key role in diagnosing heart diseases in a very convenient manner. Above all, the study has made a substantial contribution to the computation of strength scores with significant predictors in the prognosis of heart disease.

An Artificial Intelligence Approach Toward Food Spoilage Detection and Analysis

Aiming to increase the shelf life of food, researchers are moving toward new methodologies to maintain the quality of food as food grains are susceptible to spoilage due to precipitation, humidity, temperature, and a variety of other influences. As a result, efficient food spoilage tracking schemes are required to sustain food quality levels. We have designed a prototype to track food quality and to manage storage systems at home. Initially, we have employed a Convolutional Neural Network (CNN) model to detect the type of fruit and veggies. Then the proposed system monitors the gas emission level, humidity level, and temperature of fruits and veggies by using sensors and actuators to check the food spoilage level. This would additionally control the environment and avoid food spoilage wherever possible. Additionally, the food spoilage level is informed to the customer by an alert message sent to their registered mobile numbers based on the freshness and condition of the food. The model employed proved to have an accuracy rate of 95%. Finally, the experiment is successful in increasing the shelf life of some categories of food by 2 days.

Biodiversity and dairy traits of lactic acid bacteria from foliage of aromatic plants before and after dehydration process monitored by a smart sensors system

The main hypothesis of this work was to evaluate the presence of lactic acid bacteria (LAB) intrinsically resistant to plant essential oils in sage (Salvia officinalis L.) and laurel (Laurus nobilis), for future applications in functional cheese production by addition of aromatic herbs. The effect of the drying process on the viability of LAB was evaluated with three biomass densities (3, 4 and 5 kg/m2). The drying densities did not affect weight loss, but influenced the levels of LAB of sage and laurel. A total of 10 different strains of Enterococcus faecium, Enterococcus mundtii, Enterococcus raffinosus and Leuconostoc mesenteroides were identified from laurel, while sage did not host any LAB species. In particular, L. mesenteroides was the only species sensitive to the heat treatment. Only five strains, all enterococci, were resistant to at least one antibiotic, even though no strain showed gelatinase or haemolytic activity. The investigation on the technological traits useful in cheese making demonstrated that all LAB can be considered non starter LAB, because they were characterized by a slow acidification capacity (the pH was still above 6.00 after 3 d) and a very limited autolysis (the maximum decrease of the optical density at 599 nm was barely 0.2).

Smart Anomaly Detection and Prediction for Assembly Process Maintenance in Compliance with Industry 4.0

One of the big problems of today's manufacturing companies is the risks of the assembly line unexpected cessation. Although planned and well-performed maintenance will significantly reduce many of these risks, there are still anomalies that cannot be resolved within standard maintenance approaches. In our paper, we aim to solve the problem of accidental carrier bearings damage on an assembly conveyor. Sometimes the bearing of one of the carrier wheels is seized, causing the conveyor, and of course the whole assembly process, to halt. Applying standard approaches in this case does not bring any visible improvement. Therefore, it is necessary to propose and implement a unique approach that incorporates Industrial Internet of Things (IIoT) devices, neural networks, and sound analysis, for the purpose of predicting anomalies. This proposal uses the mentioned approaches in such a way that the gradual integration eliminates the disadvantages of individual approaches while highlighting and preserving the benefits of our solution. As a result, we have created and deployed a smart system that is able to detect and predict arising anomalies and achieve significant reduction in unexpected production cessation.

INSPEX: Optimize Range Sensors for Environment Perception as a Portable System

Environment perception is crucial for the safe navigation of vehicles and robots to detect obstacles in their surroundings. It is also of paramount interest for navigation of human beings in reduced visibility conditions. Obstacle avoidance systems typically combine multiple sensing technologies (i.e., LiDAR, radar, ultrasound and visual) to detect various types of obstacles under different lighting and weather conditions, with the drawbacks of a given technology being offset by others. These systems require powerful computational capability to fuse the mass of data, which limits their use to high-end vehicles and robots. INSPEX delivers a low-power, small-size and lightweight environment perception system that is compatible with portable and/or wearable applications. This requires miniaturizing and optimizing existing range sensors of different technologies to meet the user’s requirements in terms of obstacle detection capabilities. These sensors consist of a LiDAR, a time-of-flight sensor, an ultrasound and an ultra-wideband radar with measurement ranges respectively of 10 m, 4 m, 2 m and 10 m. Integration of a data fusion technique is also required to build a model of the user’s surroundings and provide feedback about the localization of harmful obstacles. As primary demonstrator, the INSPEX device will be fixed on a white cane.

Wearable Smart System for Visually Impaired People

In this paper, we present a wearable smart system to help visually impaired persons (VIPs) walk by themselves through the streets, navigate in public places, and seek assistance. The main components of the system are a microcontroller board, various sensors, cellular communication and GPS modules, and a solar panel. The system employs a set of sensors to track the path and alert the user of obstacles in front of them. The user is alerted by a sound emitted through a buzzer and by vibrations on the wrist, which is helpful when the user has hearing loss or is in a noisy environment. In addition, the system alerts people in the surroundings when the user stumbles over or requires assistance, and the alert, along with the system location, is sent as a phone message to registered mobile phones of family members and caregivers. In addition, the registered phones can be used to retrieve the system location whenever required and activate real-time tracking of the VIP. We tested the system prototype and verified its functionality and effectiveness. The proposed system has more features than other similar systems. We expect it to be a useful tool to improve the quality of life of VIPs.

Deployment of a Smart Structural Health Monitoring System for Long-Span Arch Bridges: A Review and a Case Study

Structural health monitoring (SHM) technology for surveillance and evaluation of existing and newly built long-span bridges has been widely developed, and the significance of the technique has been recognized by many administrative authorities. The paper reviews the recent progress of the SHM technology that has been applied to long-span bridges. The deployment of a SHM system is introduced. Subsequently, the data analysis and condition assessment including techniques on modal identification, methods on signal processing, and damage identification were reviewed and summarized. A case study about a SHM system of a long-span arch bridge (the Jiubao bridge in China) was systematically incorporated in each part to advance our understanding of deployment and investigation of a SHM system for long-span arch bridges. The applications of SHM systems of long-span arch bridge were also introduced. From the illustrations, the challenges and future trends for development a SHM system were concluded.