INFLUENCE OF MACHINE LEARNING IN ADDITIVE MANUFACTURING PROCESSS

The absence of the right tool to produce products makes traditional manufacturing processes time-consuming and uneconomical for several industries. In recent years, machine learning (ML) algorithms have become more prevalent in manufacturing, reducing labor cost, time, and effort in developing items and products. The availability of massive data and advancements in digitalization and manufacturing methods have further emphasized the importance of integrating ML and optimization techniques to enhance product quality. By integrating ML into manufacturing methods, new approaches are accepted, time, energy, and resources are saved, and waste is minimized. ML integrated assembly processes contribute to smart manufacturing, where technology automatically adjusts errors in real-time to prevent spillage. The term Industry 4.0 collectively refers to a wide range of current concepts, whose clear classification concerning a discipline as well as their precise distinction is not possible in individual cases. In the following fundamental concepts are listed:

• Smart Factory: Manufacturing will completely be equipped with sensors, actors, and autonomous systems. By using “smart technology” related to holistically digitalized models of products and factories (digital factory) and an application of various technologies of Ubiquitous Computing, so-called “Smart Factories” develop which are autonomously controlled.
• Cyber-physical Systems: The physical and the digital level merge. If this covers the level of production as well as that of the products, systems emerge whose physical and digital representation cannot be differentiated in a reasonable way anymore. An example can be observed in the area of preventive maintenance: Process parameters (stress, productive time etc.) of mechanical components underlying a (physical) wear and tear are recorded digitally. The real condition of the system results from the physical object and its digital process parameters.
• Self-organization: Existing manufacturing systems are becoming increasingly decentralized. This comes along with a decomposition of classic production hierarchy and a change towards decentralized self-organization.
• New systems in distribution and procurement: Distribution and procurement will increasingly be individualized. Connected processes will be handled by using various different channels.
• New systems in the development of products and services: Product and service development will be individualized. In this context, approaches of open innovation and product intelligence as well as product memory are of outstanding importance.
• Adaptation to human needs: New manufacturing systems should be designed to follow human needs instead of the reverse.
• Corporate Social Responsibility: Sustainability and resource-efficiency are increasingly in the focus of the design of industrial manufacturing processes. These factors are fundamental framework conditions for succeeding products.

“Industry 4.0” describes different – primarily IT driven – changes in manufacturing systems. These developments do not only have technological but furthermore versatile organizational implications. As a result, a change from product- to service-orientation even in traditional industries, is expected. Second, an appearance of new types of enterprises can be anticipated which adopt new specific roles within the manufacturing process resp. the value-creation networks. For instance, it is possible that, comparable to brokers and clearing-points in the branch of financial services, analogy types of enterprises will also appear within the industry.

With the planning, analysis, modelling, design, implementation and maintenance (in short: the development) of such highly complex, dynamic, and integrated information systems, an attractive and at the same time challenging task for the academic discipline of business and information systems engineering BISE arises, which can secure and further develop the competitiveness of industrial enterprises.

                   (a)                                                                                  ( b)                                                                                        (c)                                 

Fig. 1 : Flow chart of the machine learning approaches

 Fig 2. (a)Nine technologies transforming the industrial product design. b Breakeven comparison Additive manufacturing Vs Conventional manufacturing . c Major challenges faced by the manufacturing sector

Uncertainty in Additive Manufacturing (AM) techniques stems from the variation in the quality of manufactured parts and serves as a major barrier to realizing their full potential. This challenge can be addressed through uncertainty quantification (UQ) and uncertainty management (UM) by modeling and simulating the AM process. Various stages of AM, such as powder bed forming, melting, and solidification, involve sources of uncertainty that lead to variability in the quality of components produced. This variability poses a challenge to consistent manufacturing of high-quality products, especially in the production of metal components. To ensure quality control in the AM process, a comprehensive understanding of uncertainty sources at each stage and their impact on product quality is essential. Uncertainty quantification (UQ) involves investigating these sources to achieve improved quality in additive manufacturing.

Conclusion 

Many studies have shown that the world is currently experiencing a flood of data that is generating large amounts of data every day from different sources. Examples of these sources include urban planning statistics, sensor readings, environmental parameters, financial records, AM/3D printer data, medical information, data on mobility, and more. It is crucial to gather relevant insights and information from the diverse information available on many networks. ML algorithms can be utilized to examine new research areas by representing and extracting insights from heterogeneous data using available data. Intelligent Manufacturing, there will be AM and ML as important innovations for Industry 4.0, also known as the 4th industrial revolution.

• Researchers can utilize advanced machine learning techniques for time series analysis, conceptual advancements, simulation, model-based fabrication, production data processing and retrieval, analytical thinking, medical and biological records, data-driven clustering algorithms, fault and outlier identification, and in-situ and performance tracking.
• Overall, ML has increased the likelihood of companies implementing AM and has enhanced their perception of its value. However, most ML solutions for AM have not undergone sufficient testing to be applied to real-world problems. To address this issue, current studies should focus on making these tools more applicable to actual industrial challenges and provide practical examples from industry to increase confidence in their effectiveness.
• Another potential study issue could be equipment simplicity and the utilization of higher raw element limitations. ML-based optimization strategies may cope with greater requirements of the processing phases, providing consistent quality while lowering machinery and raw material costs.
• ML techniques and their applications in various key AM processes are thoroughly examined in this study. Researchers have found ML to be effective in improving the quality of 3D builds. In AM, integration of ML is used to enhance tool productivity, research novel materials, and discover property–structure relationships. The majority of existing ML applications in manufacturing fields heavily focus on processing-related procedures such as process parameter optimization. These applications can adjust operation parameters for one or more performance standards. Despite the lack of work aiming to develop better approaches for these optimization algorithms, they are machine-oriented. Regardless of whether the final optimization technique is conventional or ML-based, a significant number of tests will be required without such advancements. Therefore, ongoing ML research is expected to soon concentrate on novel materials, reasonable production plans, and computerized in-process analysis.

 Avishek Samanta

Asst.Professor, Institute of Science & Technology

Dept. of Mechanical Engg, 

GOPSAI AVINANDAN SANGHA

Editor