Abstract

This paper presents the design, implementation, and experimental validation of a novel canned cycle for CNC milling machines, enabling the precise and efficient cutting of threads with fixed or variable pitch and radius. Conventional canned cycles are limited to fixed pitch threads, restricting the versatility of CNC milling machines in thread machining applications.

The development process involves integrating a sophisticated control algorithm into the CNC milling machine’s software, giving the operator remarkable control over the thread cutting process. This algorithm allows the operator to choose between external or internal threads, set both initial and final radii, determine initial and final pitches, specify the number of turns, and select the left or right-hand thread type. Such flexibility enables the creation of threads with diverse geometries. Furthermore, the proposed canned cycle provides the capability to switch between roughing and finishing passes by adjusting the step motion along the prescribed helical curve.

Simulation tests conducted under various threading cases clearly demonstrate the efficiency of the proposed canned cycle. These results showcase its capability to address a wide range of machining scenarios, offering practical solutions applicable across a spectrum of applications.

Introduction

The evolution of Computer Numerical Control (CNC) machining has brought about a revolution in manufacturing processes, not only enhancing precision and efficiency but also addressing the challenges posed by complex geometries and demanding machining applications. This paper deals with the domain of CNC thread milling, introducing an innovative approach through the development and implementation of a comprehensive canned cycle.

A canned cycle is a predefined machining operation that entails a sequence of machine movements to perform various machining tasks like drilling, pocketing, slotting, boring, and tapping. It aims to simplify programming by consolidating multiple commands into a single block, utilizing G-code to define machining operations that would typically necessitate multiple blocks [1, 2]. Continuous efforts are made by CNC manufacturers to incorporate new canned cycles into their controllers, specifically designed to handle intricate and complex geometries [3, 4]. This is easily concluded by comparing the canned cycles available nowadays with the older ones.

Centered on programming and facilitating the precise and efficient cutting of threads within a user-friendly environment, incorporating both fixed and variable pitch and radius, the presented canned cycle addresses limitations posed by conventional canned cycles, which primarily target fixed pitch threads. In the precise domains of engineering and manufacturing, the use of variable pitch threads is necessitated by diverse potential applications. These applications encompass manufacturing components for automotive vehicle engines, fasteners for aerospace systems, industrial devices, robots manufacturing parts [5,6,7,8] and the creation of medical implants [9,10,11,12]. The mentioned references constitute a sample of recent related research work.

Conclusions

The work presented in this paper built upon the foundations laid in previous mentioned research. As predicted in that earlier work, the present study advanced and extended the capabilities of the thread canned cycle to encompass threads with predefined start and ending radii, start and ending pitches, and the desired number of turns, applicable to both left- or right-hand, internal or external threads. Moreover, the proposed canned cycle seamlessly transitions between roughing and finishing passes, allowing precise regulation of step motion along the prescribed helical curve. The introduced canned cycle surpasses the limitations of conventional cycles, which are confined to fixed pitch threads, by providing a comprehensive solution for diverse threading requirements.

Simulation tests confirm the adaptability of the proposed canned cycle across diverse threading conditions. The graphical outputs, combined with the mathematical formulation of helix equations and CNC milling machine simulations across various threading cases, collectively verify the efficiency, and validation of the proposed canned cycle.

Abstract

Pure waterjet surface treatment without abrasive particles has a promising application in the biomedical field, because it induces compressive residual stresses on a metal surface and never leaves the tiny hard particles. In this work, the influence of operation pressure, standoff distance and the number of paths of the waterjet on the surface topography as well as the hardness was studied using the Taguchi method. The results showed that the most essential parameter is the operation pressure. By tuning the operation pressure from 100 to 300 MPa, the surface of Ti6Al4V specimens can be smoothed, roughened or damaged; when the surface layer is eroded, the new-born surface exhibits a clear stochastic nature accompanied by microvoids. The standoff distance benefits finer controlling the height parameters, whilst the number of paths affects the waviness. The hardening effect generated by the waterjet impingement extends to a few hundred-micron depth of the specimens, and the peak value of microhardness was found at a depth of 70 μm, which is an increase by greater than 20 %. The roughness parameters of Arithmetical mean height (Sa), Skewness (Ssk), Auto-correlation length (Sal), and Developed interfacial area ratio (Sdr) as a set are recommended to characterize the biomaterial’s surface. The present research results promote the application of waterjet treatment in the field of fine-tuning biomaterial surface morphology.

Introduction

The surface topography of the implant significantly influences the bone response [1,2]. There are two stages (short/long) of biological response after implantation [3]. One is the short-term response that a fibrous soft tissue is formed and capsules around the implant, and another response directly relates to bone-implant contact without the connective tissue layer, known as osseointegration [4]. The surface topography plays an important role in the biomechanical fixation between the implant and the soft fibrous tissue at the early stage of implantation. Meanwhile, the rate and quality of osseointegration that determine the long-term success of the clinic implantation are also affected by the surface topography [5]. The demand for specific surface finishes of biomedical devices [6] drives researchers to develop efficient surface modification methods.

Waterjet technology that places a sample under the waterjet itself and subjects it to the water pressure in the air or underwater is one of the promising methods [7,8]. As one of the fastest-growing technologies, the waterjet can machine (cut, drill and mill, clean, remove) almost any material [9]. Waterjet is used in a variety of fields from automotive and aerospace to medical and the food industries [10]. Particularly, pure waterjet induces compressive residual stress to enhance fatigue strength [11], and never leaves tiny residues (hard particles) on a metal surface compared to the abrasive waterjet, it holds tremendous promise in the medical industry [7,11].

Conclusion

Using the Taguchi method, nine waterjet experimental cases were designed and conducted. The influence of the operation pressure, standoff distance, and the number of paths on the surface topography and the areal roughness parameters as well as the hardness were analyzed and discussed. The main conclusion is drawn as follows:
• Among the three process parameters of waterjet treatment, the most critical one is the operation pressure. The standoff distance is beneficial for finer control of the height parameters, whilst the number of paths affects the waviness.

• By increasing the operation pressure from 100 to 300 MPa, the surface of Ti6Al4V specimens can be smoothed, roughened, or damaged; when the surface layer is eroded, the new-born surface exhibits a clear stochastic nature accompanied by microvoids.

• The hardening effect generated by the impingement of waterjet spans over a few hundred microns width, with the peak microhardness being found at a depth of 70 μm, which increases by over 20 %.

• After a comprehensive analysis of the 5 categories of 14 areal roughness parameters, the parameters of Sa, Ssk, Sal, and Sdr as a set are recommended to characterize the biomaterial’s surface.

Abstract

Climate change requires transforming the production processes of high-emission industries toward low-carbon technologies. One of the main emitters of greenhouse gases is the steel industry. Therefore, steel manufacturers are planning to substitute the blast furnace-basic oxygen furnace route with hydrogen-based direct reduction and electric arc furnaces. Thus, direct greenhouse gas emissions can be avoided almost entirely. This involves changes in the materials and energies used. Besides hydrogen, natural gas is becoming more important for the direct reduction of iron ores. Therefore, future greenhouse gas emissions associated with low-carbon steelmaking are deeply intertwined with the developments of the system environment. Upon potential transformation pathways of the system environment, future greenhouse gas emissions from natural gas and hydrogen-based direct reduction coupled with electric arc furnaces are investigated. To this end, a prospective cradle-to-gate life cycle assessment approach is used. The results indicate that greenhouse gas emissions highly depend on the electricity mix if hydrogen is produced by electrolysis. Using natural gas for direct reduction is a viable short-term option until the decarbonization of the energy sector is further advanced.

Introduction

Due to climate change, it is required to decarbonize highemission industries such as the steel industry. Approximately 7% of global greenhouse gas (GHG) emissions result from steelmaking processes, especially from primary steelmaking [1]. In most cases, the coal-based blast furnace-basic oxygen furnace (BF-BOF) route is used for the primary production of crude steel [2]. However, alternative technologies for reducing direct GHG emissions of steelmaking already exist. As of today, most steel manufacturers are planning to substitute BFBOF production facilities with direct reduction plants (DRP) and electric arc furnaces (EAF) from 2025 onwards [3–5]. In this process design, sponge iron is produced within the DRP. Afterward, the sponge iron is further processed within the EAF for crude steel production. Existing DRPs are currently solely operated with natural gas (NG/DR) for direct reduction [1]. The NG/DR-EAF process serves as a transitional technology in the transformation process of integrated steel mills toward lowcarbon steelmaking. In the long term, NG/DR can be substituted by hydrogen-based direct reduction (H/DR) of iron ore. If hydrogen is used, direct GHG emissions can be reduced by up to 97% [6]. A framework for designing economically advantageous transformation pathways is developed within [7].

Conclusion

Most European steel manufacturers are planning to transform their integrated steel mills’ production infrastructure toward DR-EAF production to significantly reduce GHG emissions from primary steelmaking. This article consists of a prospective cradle-to-gate LCA approach for DR-EAF steelmaking. For environmental impact assessment, it is differentiated between NG/DR, H/DR with hydrogen from electrolysis using the German electricity mix, and H/DR with hydrogen from electrolysis using electricity from onshore wind turbines. The developed assessment provides an understanding of the environmental impacts of the DR-EAF route considering different future developments of the system environment.

This study finds that direct GHG emissions can be reduced by around 96% if H/DR-EAF production is applied to substitute the BF-BOF route. However, indirect GHG emissions are highly intertwined with future developments of the system environment. Thus, fast decarbonization of the energy sector is highly important to heavily reduce overall GHG emissions. Also, GHG emissions from raw material extraction, processing, and transportation need to be reduced to aim for a low GHG emission steel industry. Process-related, direct GHG emissions are higher when NG/DR is applied. Indirect emissions, however, are less affected by system environment developments. Thus, natural gas provides a viable short-term option for the DR process. Besides decarbonization of the system environment which needs to be driven by policymakers, steel manufacturers need to focus on measures to increase their shares of low-carbon hydrogen in the early phase of the transformation process. To this end, electrolysis capacities need to be installed if sufficient external hydrogen sources for H/DR are not available. Also, increasing the share of low-carbon electricity to operate electrolysis facilities is of high importance.

Abstract

     Management 4.0 assists businesses in evolving, surviving and performing in the competitive and dynamic world. This fourth revolution uses advanced technologies like Artificial Intelligence (AI), Virtual Reality (VR), Internet of Things (IoT), Robotics, Holography, Additive Manufacturing etc., for the proper management systems. These technologies facilitate working personnel and make it more appealing to complete their duties efficiently and accurately. The main aim of this paper is to understand the concept of Management 4.0, its technologies and applications for proper management systems. As Management 4.0 enhances process control, the chance of human error is reduced, leading to increased efficiency. It enables rapid and intelligent decision-making, reduces costs, accelerates growth, and raises profitability. Management 4.0 technologies and advanced data analytics are helpful to make smart supply chain management well suited to fulfil industry 4.0. Thus, to overcome various obstacles and effectively deploy Management 4.0 technologies in manufacturing industries, top management must establish a clear asset performance management plan with the help of process engineers familiar with industrial system failure occurrences and what operators need to improve. Management 4.0 involves advanced technologies, system connectivity, data collection & analysis at the organisation level. Management 4.0 is expected to be a critical component in the long-term survival of any business, either manufacturing or service-providing organisations.

Introduction

     Implementing Management 4.0 technologies provides firms with increased efficiency by connecting automation with data collecting and exchange protocols. Streamlining the procedures and boosting access to valuable data can optimise production with optimal resource utilisation. Advanced technologies are integral to Management 4.0, and their effective use could lead to better production and management. For instance, robots are an essential part of flexible automation and have many services in industrial manufacturing, including welding, assembling, plasma cutting, and finishing [1], [2], [3]. Manufacturers of all sizes may profit from robots in a variety of ways. Proper integration of Robots contributes to improved quality control, a safer work environment, decreased bottlenecks, higher output, enhanced worker satisfaction, and many more. Like this, a new factory is planned, and another powerful digital twin technology could be utilised for new products or processes. It allows for faster and more reliable innovation development with fewer physical prototypes. When a product is manufactured, or a factory is implemented, more data is generated to improve the Management 4.0 environment [4,5].

Conclusion

     Management 4.0 technologies help connect machines to the cloud and construct customised versions of their industrial IoT. These technologies are laying the groundwork for future development and scalability. The ultimate objective of Management 4.0 is to enhance and advance industrial processes. In recent years, the manufacturing business has undergone many changes. Management 4.0 is a comprehensive and unified set of technologies that can transform the entire management system. It has also become a reality in which factory automation is commonplace, and factories are more intelligent than ever before. Technologies aim to collect massive volumes of data from various sources. These provide intelligent production scheduling software, the foundation for smart autonomous decision-making. In this Management 4.0 scenario, supply chain analytics will reach new heights. Manufacturing as a service allows firms to bid on manufacturing a product or a single production step. This specialisation can improve manufacturing efficiency while lowering costs and fine-tuning the supply chain. Production and logistics operations are smartly connected, resulting in a more efficient and adaptable real-time lean manufacturing environment. This will make it easier to build smart value-creation chains that span the whole product life cycle, from conception to manufacture, usage, and maintenance to recycling. These characteristics of the Management 4.0 facilitates the managers to make data-driven decisions and set their strategic goal in that direction. Management 4.0 enabled culture to help the employee develop their own skill set that might help make the process and operations more efficient. The primary role of Management 4.0 is to motivate the managers to develop the Management 4.0 culture. In addition, Management 4.0 technologies will become extremely useful in the manufacturing process and assist with various logistics, including manufacturing, production, and shipping. This study might be helpful for proper management systems in the industries and provide proper information systems.

Abstract

     Artificial Intelligence (AI) and Machine learning (ML) represents an important evolution in computer science and data processing systems which can be used in order to enhance almost every technology-enabled service, products, and industrial applications. A subfield of artificial intelligence and computer science is named machine learning which focuses on using data and algorithms to simulate learning process of machines and enhance the accuracy of the systems. Machine learning systems can be applied to the cutting forces and cutting tool wear prediction in CNC machine tools in order to increase cutting tool life during machining operations. Optimized machining parameters of CNC machining operations can be obtained by using the advanced machine learning systems in order to increase efficiency during part manufacturing processes. Moreover, surface quality of machined components can be predicted and improved using advanced machine learning systems to improve the quality of machined parts. In order to analyze and minimize power usage during CNC machining operations, machine learning is applied to prediction techniques of energy consumption of CNC machine tools. In this paper, applications of machine learning and artificial intelligence systems in CNC machine tools is reviewed and future research works are also recommended to present an overview of current research on machine learning and artificial intelligence approaches in CNC machining processes. As a result, the research filed can be moved forward by reviewing and analysing recent achievements in published papers to offer innovative concepts and approaches in applications of artificial Intelligence and machine learning in CNC machine tools.

Introduction

     CNC machining operation is one of the most important part-production methodologies, and it is often referred to as the engine of modern manufacturing processes. The automotive and medical sectors, aerospace, gas and oil, and warehousing services, are using the CNC machining operations to create parts in different applications [1]. CNC machining is generally used in manufacture of every machine, molded part, or finished product as one of the most important manufacturing processes. CNC machinery has paved the way in manufacturing and machining, allowing businesses to achieve their goals and targets in a variety of ways. However, because manufacturing methodologies is always evolving and new technologies are being introduced, it is critical to consider future of CNC machining operations [2, 3]. Machine learning (ML) is the study of computer algorithms that gives computers the capacity to automatically learn from data and prior experiences in order to find patterns and make predictions without human involvement. The ML and applications in different areas of study are considered to be a component of artificial intelligence [4], [5], [6].

     Machine learning and artificial intelligence in particular raise plenty of concerns about the future of CNC machining operations and how these concepts will evolve future works of manufacturing companies [7]. The way a machine learns, adapts, and optimizes output can also be influenced by real-time data, analytics, and deep learning. Data sets are essential for operators to understand how a machine works and, eventually, how a whole floor of machines works together [8, 9]. Due to the development of affordable, reliable, and resilient sensors and acquisition and communication systems, novel implementations of machine learning approaches for tool condition monitoring can be presented [10]. Machine learning systems are capable of completely examine data and identify various types of areas which should be modified. Machine tools are increasingly being equipped with edge computing options to record internal drive signals at high frequency in order to supply the necessary vast quantity of data for the use of machine learning techniques in manufacturing [11]. Productivity and efficiency are two areas where artificial intelligence can modify CNC machine tools operations in order to enhance accuracy of CNC machining operations[12]. Machines can generate and analyze production data and provide real-time findings to human operators are effective devices for increasing productivity in part production processes. As a result, shop owners can quickly adjust the way a machine operates using the modified data generated by advanced machine learning algorithms in order to enhance productivity of part manufacturing [13]. Having more knowledge and making better decisions in process planning strategies means less downtime on the work floor during process of part production. Production and maintenance process of part manufacturing using CNC machine tools can be developed using the machine learning and the artificial intelligence in order to enhance efficiency in part manufacturing operations [14].

Conclusion

     Machine learning and artificial intelligence are applied to various industrial applications in order to improve performances of industrial process. To increase accuracy as well as efficiency during CNC machining operations, different applications of the machine learning and artificial intelligence systems are studied in different research works. Reducing machine downtime, optimization of CNC machine tools, cutting tool wear prediction, cutting force model, CNC machine tool maintenance, monitoring of machining operations, surface quality prediction and energy prediction systems are some examples of machine leniting applications in the development of CNC machining operations. ML techniques are recently applied to energy consumption prediction models in order to decrease the energy consumption during CNC machining operations. Accuracy and radiality of energy consumption models are significantly enhanced using the ML methodologies in comparison to the tradition’s methods of energy usage predictions during CNC machining operations.

     The applications of machine learning and artificial intelligence systems in CNC machine tools are examined in this study by analyzing recent achievements from published papers. The main aim of this study is to provide an overview of current studies on machine learning and artificial intelligence techniques in CNC machining operations in order to provide a useful study for the researchers in the interesting field. Network of sensors and cloud data sources can connect CNC machines together can be employed to provide smart CNC machine tools. The machining industry’s efficiency can be increased as it transitions to smart machining techniques, allowing it to achieve self-optimization and adaption to uncontrolled circumstances. However, developing the applications of advanced machine learning systems in CNC machining operations as the combination of physical, computers, and networking process created challenges and difficulties regarding the safety and security of the web of data. In order to provide secure and advanced connections between the different CNC machine tool, the security of networks should be enhanced.

Abstract

Reducing manufacturing cost is the main goal of machine tool precision allocation under the condition of meeting precision design requirements. The previous studies generally took “ensuring machining errors completely within the allowable range of design precision” as constraint. Due to the strict constraint, the cost reduction effect is limited. This paper proposes a new method of precision allocation based on precision reliability. The probability that the machining errors are within the design precision allowable range is taken as the measurement index of precision reliability, and the optimization model constraint is relaxed to “that the machining errors are within the allowable range of design precision with predefined precision reliability”, so as to obtain lower manufacturing cost under tolerable precision loss risk. The case study of a large-scale gear hobbing machine shows that this method can effectively reduce the manufacturing cost, and the precision allocation is more economical and reasonable.

Introduction

Due to the fierce competition in the machinery industry, higher requirements are put forward for the design of machine tools. The traditional empirical design method cannot meet the design requirements of balancing the precision, cost and reliability of machine tools. Optimization design is a kind of modern design method, which is to optimize the design parameters under various specified constraints to obtain the optimal value of one or more design indicators. It aims at improving product quality and reducing product cost.

The machine tool design mainly includes the structural design, electrical design, precision design, and thermal design. Precision design, also known as precision allocation, is to reasonably allocate the precision of each part of the machine tool according to the machining performance requirements of the machine tool. Due to the complex impact on product quality and cost, the precision allocation is a highly responsible task [1]. Precision allocation generally includes three steps: precision prediction, precision allocation optimization modeling and model solving.

Conclusions

Precision allocation is an important work in machine tool design, which is of great significance to ensure the machining precision, improve product quality and reduce manufacturing cost. The precision allocation method mainly includes the basic principle method, the method with the optimization objective of minimizing manufacturing cost, and the method with the optimization objective of minimizing quality loss. The precision allocation of motion parts is mostly based on the sensitivity (or contribution) of their errors to the manufacturing cost.

According to the modern mechanical precision design theory, the overall precision of machine tool transmission chain is a random variable with normal distribution. Taking the normal distribution random variables properties into consideration, the probability is very low with variable reaching the maximum. Obviously, it is too harsh for the overall precision to fully meet the requirements of design precision, and the precision allocation scheme based on this constraint is not economical and reasonable. In the design of precision allocation, it does not seek meeting 100 % design requirements. Based on the characteristics of normal distribution of the comprehensive machining precision, it takes the probability of the machining precision meeting the design requirements to reach a certain threshold as the constraint condition for precision allocation, which can ensure the machining precision of the CNC-LGHM and effectively reduce the manufacturing cost.

Abstract

The importance of cutting hardened materials without influencing their structural properties makes abrasive waterjet machining a favorable choice in certain applications in comparison with other manufacturing technologies. Using this technology, faulty surface structures and errors caused by high thermal loads such as burrs, burns and cracks, can be avoided. In this paper, the results of applying the abrasive waterjet technology for cutting of hardened steel and its advantageous properties in comparison with cut-off grinding will be presented. The influence of process parameters on surface properties and burr formation has been determined by experimental analysis.

Introduction

Separating bars with small diameters made of hardened steel is usually conducted by applying the cut-off grinding technology [1]. This approach results in high temperatures within the cutting process, which leads to thermally influenced structural and surface errors on the workpiece, such as grinding burns, burrs and residual stress within the material [2]. These negative effects can be lowered by optimizing the machining strategy and process parameters in addition to using a CBN grinding wheel, which exhibits a high thermal conductivity and reduces the temperature transferred in the workpiece [3]. However, such an approach increases tooling costs and the overall processing time per one workpiece, which makes the application of these strategies applicable only when these factors are not of high importance. In this paper, a different approach for separating bars made of hardened steel using the abrasive waterjet technology is investigated.

Conclusion

In this research work, the influence of common abrasive waterjet process parameters and different cutting strategies on the surface quality and kerf properties were presented. Based on the study of the results gathered within the conducted experimental analysis of the abrasive waterjet cutting of the hardened high-strength steel 1.7102, following statements can be concluded:

Abstract

Keywords

1. Introduction

2. Literature review

3. Research methodology

4. Case study

5. Results and discussions

6. Conclusions

7. Compliance with ethical standards

Declaration of Competing Interest

References

Abstract

Lean manufacturing is a profound system designed to enhance every manufacturing industry’s efficiency by reducing waste through internationally recognized tools and techniques. Manufacturing industries strive to adopt lean concepts to maximize their resources like staff, facilities, materials, and schedules to be economically effective. However, managers face difficulty selecting the appropriate lean tools out of the many available LM tools for successful lean implementation. This study suggests an innovative approach to choose suitable lean tools to maximize these essential resources. Herein the Value Stream Mapping and plant layout are considered for waste identification. Fuzzy QFD and FMEA prioritize the crucial resources concerning the defined wastes and determine the risk associated with each failure mode’s sub-element for lean application. It saves time by analyzing only the most critical resources for a successful lean implementation since its focus only on the most important resources. The applicability of the proposed approach is demonstrated through a case study of an Ethiopian shoe manufacturing firm. With the aid of future state plant layout and value stream map, total cycle time is reduced by 56.3%, lead-time is reduced by 69.7%, materials transportation distance and transportation activities are reduced by more than 75%, and workers required are reduced from 202 to 200.

1. Introduction

Companies adopt lean manufacturing (LM) principles and tools for various reasons, including global competition, an uncertain market environment, and rising customers’ expectations. The concepts of LM can make it possible to use their resources effectively and increase their competitiveness. According to Deif and ElMaraghy (2014) and Goshime, Kitaw, and Jilcha (2018), LM is characterized by doing more with less. It focuses on reducing/eliminating waste in order to increase productivity and maximize customer values (Belekoukias et al., 2014).

With the origin of the Toyota Production System, several LM techniques and tools were developed and used to achieve lean. Some of them are TPM, JIT, TQM, kaizen, kanban, production smoothing, cellular manufacturing, one-piece flow, Value Stream Mapping (VSM), and standardized work. Most of them are adopted in discrete manufacturers (Kumar & Parameshwaran, 2018). LM techniques and tools where effectively implemented in Automotive (Vamsi Krishna Jasti & Sharma, 2014), chemical (Jilcha & Kitaw, 2015), textile industry (Hodge, Goforth Ross, Joines, & Thoney, 2011), construction (Ko, 2010; Aka, Danladi Isah, Eze, & Timileyin, 2020), and healthcare industries (Barberato, Freitas, Godinho, & Francisco, 2016).