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The demand for high quality composite materials is growing and is clearly moving towards to a responsible alternative that will reduce the harmful effects of plastic materials. The present study aims to investigate the structure of the Arboblend V2 Nature bio-based polymer reinforced with silver nanoparticles (AgNP’s) and also of samples, coated with thin ceramic layers. The micro powders used for the ceramic coatings were Amdry 6420 (Cr2O3), Metco 143 (ZrO2 18TiO2 10Y2O3) and Metco 136F (Cr2O3-xSiO2)-yTiO2). The main goal of the silver nanoparticles reinforcement was to demonstrate the antibacterial effect of the uniform distributed nanoparticles (in the sample mass), those facilitating the use of this one in the sanitary field. Regarding the coatings with ceramic layers, it was desired to obtain fine and uniform layers in order to increase the tribological and anticorrosive characteristics so helpful in operation in the automotive industry field but, not only. Take into account the good results obtained can be concluded that the used biodegradable material has superior characteristics that meet the technological demands, being able to gradual substitute the plastic from important activity areas.
Smart manufacturing has become a feasible and high-potential objective for traditional manufacturing companies to enhance their capabilities and strength for business competition. It will not only benefit their production efficiency, but also yield rate, logistic/supply-chain management, and business development. However, due to lack of well-established automatic facility, auto-sensing system, experiences on use of big data analysis, and machine learning, the first difficulty encountered to companies for developing smart manufacturing is usually “where to start” and“how to do”. In this speech, the international trend, the needs of companies, smart manufacturing apps, and implementation examples of smart manufacturing in traditional manufacturing industry will be addressed to give audiences an overall introduction.
The metal industry in Taiwan has been developed for decades, and the Metal Industry Research and Development Centre (MIRDC) in Taiwan has been established for nearly 60 years as well. In the early days, it focused on the research and development of metal materials and related secondary processing technologies. In recent years, in line with the government’s policy of promoting Industry 4.0, we have concentrated on the research and development of key technologies for smart manufacturing, with the aim of helping the metal industries to implement smart manufacturing and to enhance their global competitiveness.
My colleagues and I will take the opportunity to share with you the information about the important history of developing smart manufacturing in Taiwan, and share two successful case studies.
The first case is a case of implementing the smart manufacturing technology into an advanced servo stamping process. A demonstration and trial production line was set up successfully in 2019. This line includes a 400-ton servo press, 6-axis robotic arms, 3D scanning device, feeder, etc. At the same time, we use this line to develop cloud production information system, smart virtual trial run, online die monitoring. We have used these technologies to serve more than 10 stamping part manufacturers.
The second case is: the key technology development and industrial service case of smart manufacturing in the fastener industry. In this case, we have invested about 7 years and a total of 40 masters and doctors are involved in this project. In the third year of this project, we have constructed a demonstration field with the multi-function for research, trial production line and demonstration. The outputs and impacts are quite good. At present, we have served more than 20 fastener manufacturers, and there are more and more local fastener manufacturers coming to inquire for assistance.
Nowadays, bioplastics and biocomposites have been attracting great attention as promising alternatives to oil-based materials in order to reduce carbon footprint. Nevertheless, their low mechanical performance and tricky manufacturing are major obstacles to their widespread use for industrial applications. For example, natural fiber reinforced biopolymers which are investigated to replace glass fiber reinforced polymers, are suffering from their low strength and poor ductility.
In the SEABIOCOMP project (http://www.seabiocomp.eu/), to further enhance the mechanical performance of conventional bio-based composites such as flax fiber reinforced PLA (Polylactide), PLA fibers where polymer molecules are aligned in the fiber direction are added as a secondary reinforcement. Bicomponent PLA filaments composed of PLA with high melting point as core material and coPLA with low melting point as sheath material are developed. These bicomponent PLA filaments are mingled with flax fibers by carded needle punching method to obtain non-woven mats. These non-woven mats can be compression molded to obtain composite parts.
This kind of tertiary composites with two types of reinforcement exhibit excellent mechanical properties, originating from a hybrid effect of stiff and strong flax fibers, and ductile and tough PLA fibers. Owing to the low density of PLA, flax/PLA/coPLA composites have greater specific strength and stiffness than those of SMC (Sheet Molding Compound) which is the most common composite materials in the automotive sector. Hence, this novel 100% bio-based composite material has great potentialities to reduce the structural weight and the environmental footprint. Moreover, the manufacturing conditions such as molding temperature and pressure, are the same as the compression molding of SMC. Therefore, the conventional molding equipment and molds can be used for the manufacturing without modification.
Condition monitoring is one of the key tasks to enhance the quality of product and improve the efficiency of manufacturing process. However, some key issues limited the capability of directly transferring the system developed in the laboratory to the mass production line in industry. In this presentation, the variation of signal features caused by the change of processing parameters, the clamping force, the tool geometry, location of sensor installation, and sensor types is discussed first for metal cutting and laser welding, following by the discussion of the important role of sensor selection and feature extraction for the development of condition monitoring to improve the system robustness. The analyzed signals include vibration, audible sound, and Acoustic Emissions (AE) collected simultaneously from various sensor installation locations will be presented. To determine the best features for developing the monitoring system, a cost function is introduced in this presentation, and the effect of the feature selection on the classification rate is discussed as well. Some evaluation tests are going to be presented to demonstrate that the classification rate of the developed tool wear monitoring system can be improved with the proper selection of signal features from various sensors along with the suitable classifier.
Laser direct micro-fabrication enables high design flexibility, wide range of material selectivity, low thermal impact 3D and eco-friendly precision machining, which makes it a potential game changing technology for next generation 3D electronics. A development of novel microfabrication technologies based on laser-induced photo-thermal-chemical reactions under atmospheric environment will be presented in this talk. Specifically, a novel approach to the rapid and green fabrication of highly conductive patterns on flexible polymer substrate is introduced. Ag and Cu based microstructures are simultaneously synthesized and patterned in a predetermined fashion using an economic continuous wave laser digital manufacturing system. The conductive patterns show great electrical conductivity and mechanical robustness. A comprehensive analysis on the physics of the microscale transport phenomena have also been carried out to investigate the coupled photo-thermal-mass transfer and the effects on the synthesis and deposition of the micro/nanostructures. In particular, a physical modeling technique on the surface tension driven flow at the interfaces of the multiphase and multiple-materials is developed and validated. In addition to assist understandings on the mechanisms and key factors that affect the surface morphology of the fabricated microstructures, this type of physical model can also work along with experimental measurem
Recently, the speed of digital transformation in the manufacturing industry is becoming very fast. Process innovation through smart factory strategies is urgently needed in the flow of digital transformation, not just automation. In addition, as the needs of customers have diversified and the industrial structure has become more complex, the types of products have diversified, and the production quantity per item has been rapidly decreasing. In accordance with these environmental changes in the manufacturing industry, interest in additive manufacturing (3D printing) technology suitable for shortening the development period and producing small/large quantities of multiple varieties is rapidly increasing at the same time.
In this lecture, the pain-point and solution of the manufacturing industry, and the digital transformation and smart factory strategies of manufacturing process are described. Examples of manufacturing process innovation through convergence of sand 3D printing technology are introduced. It outlines how sand 3D printing technology is applied to mass-production processes, how much material quality can be improved compared to existing pattern methods, and how much productivity can be maximized through the technology. Along with examples of innovation application in each field, it also suggests what 3D printing technology means from the perspective of smart factories and how to view and utilize the technology.
Computer-connected devices that mimic human intelligence are referred to as artificial intelligence (AI). Additive manufacturing (AM) applications can currently be found in the food, chemical, aerospace, automotive, and healthcare industries, among other sectors. Perhaps the most significant advantage of 3D printing is that the customer’s specifications can create even complex objects. In the current state of affairs, it is better suited for small-scale production. 3D model preparation, component prototyping, and component production are the stages of additive manufacturing. In the prefabrication stage, the goal is to determine whether or not it is technically possible and feasible to print a given 3D model. It is also known as smart manufacturing when 3D printing with artificial intelligence is used. Productivity would increase as a result of smart manufacturing. It is predicted that the global 3D printing market will grow to $6 billion by 2022, with the biggest growth opportunities for businesses in the home improvement and life sciences industries. Although the additive manufacturing process has made significant strides in recent years, there are still several obstacles to overcome before being widely adopted by the industry. Taking additive manufacturing as an example, there are numerous and complex variables that must be monitored and controlled throughout the process to achieve a reasonable level of print accuracy. Experimenting with different lattice positions or designing appropriate support structures is not a sustainable or time-efficient method of finding the best configuration.
In the machine tool industry, there is growing interest in the implementation of machines that are capable of learning and adapting to their environment to optimize manufacturing processes. With the advent of the Internet of Things (IoTs), Digital Manufacturing and Industry 4.0, the development of next generation machine tools will be far-reaching in the enhancement of manufacturing processes and economic growth. To achieve, the digital transformation of the physical realm into cyber domain is critically important. In the physical domain, the pervasive sensing of machine tools through wireless communication is needed. The obtained sensor data can be used for automation of processes, compensating errors, saving energy, and preventing failures using both digital twin and artificial intelligence (AI). Digital twin concept can lay out the realization of flexible and collaborative manufacturing environment. Moreover, various AI techniques have been proposed and applied; however, many challenges still exist that inhibit the use of AI for machining tasks. We discuss the challenges and suggest future directions.
Due to inaccuracies in component fabrication and assembly, machine tools and industrial robots have geometric errors (i.e., difference between nominal and actual kinematic motions), which greatly contribute to the errors in parts fabricated on these machines, as well as unduly long process certification times. To compensate for machine tool geometric errors, the standard practice is to directly measure each error individually and, from these measurements, directly populate compensation tables found in the machine tool controller. The drawback to this method is that it is extremely slow due to long instrument set up times and does not capture the complexity (e.g., sagging, twisting) of large machine tools. To compensate for industrial robot geometric errors, circle point analysis is used where the errors of each joint are measured independently. While this method is fast, it still does not capture the complexity of robot kinematic errors. In addition, machine tools and industrial robots suffer from thermal deformations due to changes in ambient temperature and heat sources on the machine, and deflections between the tool and part due to processing forces. These error sources are very difficult to model and, thus, are typically ignored.
This talk will discuss recent work on the volumetric error compensation of large machine tools and industrial robots used for manufacturing tasks A laser tracker is used to measure the machine tool and robot geometric errors over the entire visible joint space. A 6 Degree of Freedom geometric error model is constructed for every joint. Translational and rotational errors for each joint are described by a set of joint-position dependent basis functions and probability-based estimators are employed to identify the geometric error model coefficients. Based on this model, an optimization algorithm is used to populate compensation tables for machine tools, or the inverse Jacobian method is used to modify the joint commands for robots. In this talk we will discuss the details of the new volumetric error compensation methodology and provide several examples of machine tools and robots we have modeled and compensated for a variety of industrial partners. Also, we will discuss our most recent work in on-line compensation of industrial robots where errors are directly measured and compensated for during the operation.
The transition to electric vehicles (EV) has begun in earnest on a global scale. The transition will require massive investments in new product development and new manufacturing systems. In addition to major investments in new electric propulsion systems, significant changes in vehicle structures will be needed to safely accommodate large battery packs while taking full advantage of new design freedoms that are afforded. Vehicle manufacturers must make important judgements on vehicle designs to provide products that consumers want while ensuring profitability. Even with EV technology changing rapidly, it’s not entirely clear what the best EV design will be. Vehicle manufacturers with the most agility to rapidly adapt their vehicle designs are more likely to lead the EV transition. In this talk, I will discuss the important role that reconfigurable assembly systems can play in providing automotive manufacturers with increased agility needed to be leaders in the EV transition.
In view of its low production volume, additive manufacturing (AM) often requires the capability of process prediction and optimization to support short and effective process development cycles in achieving the first-and-every-print-correct goal. Current technology based upon FEM iterations and experimental phenomenology unfortunately suffer from long delivery times and vast uncertainties. At Georgia Tech recent developments have presented a physics-based computational mechanics platform for process prediction and optimization beyond the scopes of experimentation and FEM. This new methodology quantifies the thermodynamics, heat-transfer, and materials thermos-physical behaviors in powder bed and powder feed metal AM for closed-form solutions have been established for temperature distributions. Bounded-medium solutions have been established by folding boundary thermal balance conditions into the traditional semi-infinite medium solutions to compute material responses close to build edges without iterations. Subsequently the corresponding thermal stresses, residual stresses, microstructure, build distortion, porosity, surface roughness,and mechanical properties are expressed as explicit and algebraic functions of process parameters and powder properties, factoring in the effects of scan strategy, and powder packing. Extensive experimental validations are also presented. The solutions deliver more penetrating physics of the metal AM process, showing much higher accuracy, and requiring much less computational efforts, thus promising effective prediction and optimization for first-and-every-print-correct AM.
