Showing papers in "The International Journal of Advanced Manufacturing Technology in 2016"

Additive manufacturing methods and modelling approaches: a critical review

Abstract: Additive manufacturing is a technology rapidly expanding on a number of industrial sectors. It provides design freedom and environmental/ecological advantages. It transforms essentially design files to fully functional products. However, it is still hampered by low productivity, poor quality and uncertainty of final part mechanical properties. The root cause of undesired effects lies in the control aspects of the process. Optimization is difficult due to limited modelling approaches. Physical phenomena associated with additive manufacturing processes are complex, including melting/solidification and vaporization, heat and mass transfer etc. The goal of the current study is to map available additive manufacturing methods based on their process mechanisms, review modelling approaches based on modelling methods and identify research gaps. Later sections of the study review implications for closed-loop control of the process.

A study on the residual stress during selective laser melting (SLM) of metallic powder

Abstract: The complex thermal history of the parts manufactured by selective laser melting (SLM) leads to complex residual stress, having a significant impact on the quality of SLM part. The origin of residual stress was investigated in terms of temperature gradient mechanism. Then, stresses along the height and horizontal directions were measured by X-ray diffraction, and effects of processing parameters on the stress distribution were studied. Results showed that residual stress distribution and evolution along the height direction are affected by the subsequent thermal cycling (STC) significantly. In the horizontal direction, higher energy input and longer track length induce larger residual stress. The stress parallel to the scanning direction is much larger than that perpendicular to the scanning direction, and the peak values of residual stress always occurs at the onset of scanning tracks. Based on this study, corresponding measures can be taken to reduce the residual stress or avoid stress concentration, thereby improving the process stability of SLM.

IoT-based real-time production logistics synchronization system under smart cloud manufacturing

Abstract: Cloud manufacturing (CM) and Internet of things (IoT) are interlinked, yet most works only focused on one of them and take the other as a constituent technology unit. This is practically inadequate, especially for a highly service-driven manufacturing execution system which entails systematical CM supports to respond to the real-time dynamics captured from the IoT-enabled execution hierarchy. To deal with the dynamics occurring in production logistics (PL) processes, this paper investigates a dynamic PL synchronization (PLS) of a manufacturer adopting public PL services. Contemporary CM and IoT infrastructures are systematically integrated to enable a smart PLS control mechanism with multi-level dynamic adaptability. The S-CM operation framework, operation logic, and PLS infrastructure are presented with an industrial case, and the effectiveness is also demonstrated and analyzed.

The impact of big data on world-class sustainable manufacturing

Abstract: Big data (BD) has attracted increasing attention from both academics and practitioners. This paper aims at illustrating the role of big data analytics in supporting world-class sustainable manufacturing (WCSM). Using an extensive literature review to identify different factors that enable the achievement of WCSM through BD and 405 usable responses from senior managers gathered through social networking sites (SNS), we propose a conceptual framework using constructs obtained using reduction of gathered data that summarizes this role; test this framework using data which is heterogeneous, diverse, voluminous, and possess high velocity; and highlight the importance for academia and practice. Finally, we conclude our research findings and further outlined future research directions.

Costs, Benefits, and Adoption of Additive Manufacturing: A Supply Chain Perspective.

Abstract: There are three primary aspects to the economics of additive manufacturing: measuring the value of goods produced, measuring the costs and benefits of using the technology, and estimating the adoption and diffusion of the technology. This paper provides an updated estimate of the value of goods produced. It then reviews the literature on additive manufacturing costs and identifies those instances in the literature where this technology is cost-effective. The paper then goes on to propose an approach for examining and understanding the societal costs and benefits of this technology both from a monetary viewpoint and a resource consumption viewpoint. The final section discusses the trends in the adoption of additive manufacturing. Globally, there is an estimated $667 million in value added produced using additive manufacturing, which equates to 0.01 % of total global manufacturing value added. US value added is estimated as $241 million. Current research on additive manufacturing costs reveals that it is cost-effective for manufacturing small batches with continued centralized production; however, with increased automation distributed production may become cost-effective. Due to the complexities of measuring additive manufacturing costs and data limitations, current studies are limited in their scope. Many of the current studies examine the production of single parts and those that examine assemblies tend not to examine supply chain effects such as inventory and transportation costs along with decreased risk to supply disruption. The additive manufacturing system and the material costs constitute a significant portion of an additive manufactured product; however, these costs are declining over time. The current trends in costs and benefits have resulted in this technology representing 0.02 % of the relevant manufacturing industries in the USA; however, as the costs of additive manufacturing systems decrease, this technology may become widely adopted and change the supplier, manufacturer, and consumer interactions. An examination in the adoption of additive manufacturing reveals that for this technology to exceed $4.4 billion in 2020, $16.0 billion in 2025, and $196.8 billion in 2035, it would need to deviate from its current trends of adoption.

3D printing of shape memory polymer for functional part fabrication

Abstract: This article presents a novel method of shape memory polymer (SMP) processing for additive manufacturing, in particular, fused-deposition modeling (FDM). Critical extrusion process parameters have been experimented to determine an appropriate set of parameter values so that good-quality SMP filament could be made for FDM. In the FDM process, effects of different printing parameters such as extruder temperature and scanning speed on object printing quality are also studied. In all the process studies, we aim to achieve good-quality parts by evaluating part density, tensile strength, dimensional accuracy, and surface roughness. Based on these studies, sample SMP models have been successfully built. Due to the thermal sensitive nature of the printed SMP parts, they can potentially be used as fasteners in active assembly/disassembly, smart actuators, deployable structures for aero-space applications, etc.

Visualization of RFID-enabled shopfloor logistics Big Data in Cloud Manufacturing

Abstract: Cloud Manufacturing twining with Internet of Things (IoT) has been waked up to achieve final intelligent manufacturing. With the IoT technologies such as radio frequency identification (RFID) implemented in manufacturing sites, enormous data will be generated. Such data are so complex, abstract, and variable so that it is difficult to make full use of the data which carry great myriad of useful information and knowledge. This paper presents a visualization approach for the RFID-enabled shopfloor logistics Big Data from Cloud Manufacturing. An innovative RFID-Cuboid model is used for reconstructing the RFID raw data given the production logic and time series. Several contributions are highlighted. Firstly, a possible approach to integrate IoT and Cloud Manufacturing is introduced to upgrade and transform the traditional industry for an intelligent future. Secondly, an RFID-Cuboid model is proposed by using the production logic and time stamps to chain the RFID data so that the data could be interpreted. Thirdly, a real-life case is reported to show the feasibility and practicality of the proposed visualization approach to help different end-users to ease their daily operations. Lessons and insights from this case are meaningful for the implementation of IoT-enabled Cloud Manufacturing and Big Data analytics in industry field.

Machining of aluminum alloys: a review

Abstract: The use of aluminum alloys in manufacturing industry has increased significantly in recent years. This is because primarily to their ability to combine lightness and strength in a single material. Concomitant to this growth, the machining of aluminum alloys has enormously increased in volumetric proportions—so that the chip volume represents up to 80 % of the original volume of the machined material in certain segments of the industry, like aerospace. In this context, knowledge of the characteristics of machinability of aluminum alloys is essential to provide industry and researchers with information that allows them to make the right decisions when they come to machining this fantastic material. The purpose of this review is to compile relevant information about the characteristics of machinability of aluminum alloys into a single document.

Performance evaluation of nanofluid MQL with vegetable-based oil and ester oil as base fluids in turning

Abstract: This paper investigated the effect of nanofluid MQL with vegetable-based oil and ester oil as base fluids on cutting force and temperature in cylindrical turning of AISI 1045 medium carbon steel. Comparative experiments were carried out under different cooling/lubrication conditions, i.e., dry cutting, minimal quantity lubrication (MQL) with LB2000 vegetable-based oil, MQL with PriEco6000 unsaturated polyol ester, graphite-LB2000 nanofluid MQL, and graphite-PriEco6000 nanofluid MQL. For this research, graphite-LB2000 and graphite-PriEco6000 nanofluids were prepared by a two-step method, and their thermophysical properties such as viscosity, surface tension, wettability, and thermal conductivity were measured. The experimental results show that application of graphite oil-based nanofluid MQL reduced the cutting force and temperature significantly. Furthermore, graphite-LB2000 nanofluid MQL showed better performance than graphite-PriEco6000 nanofluid MQL in terms of reduction in cutting force and temperature, especially at a high cutting speed. Therefore, compared with PriEco6000 unsaturated polyol ester, LB2000 vegetable-based oil was optimal base oil for graphite oil-based nanofluid MQL machining.

Review on the influence of process parameters in incremental sheet forming

Abstract: Incremental sheet forming (ISF) is a relatively new flexible forming process. ISF has excellent adaptability to conventional milling machines and requires minimum use of complex tooling, dies and forming press, which makes the process cost-effective and easy to automate for various applications. In the past two decades, extensive research on ISF has resulted in significant advances being made in fundamental understanding and development of new processing and tooling solutions. However, ISF has yet to be fully implemented to mainstream high-value manufacturing industries due to a number of technical challenges, all of which are directly related to ISF process parameters. This paper aims to provide a detailed review of the current state-of-the-art of ISF processes in terms of its technological capabilities and specific limitations with discussions on the ISF process parameters and their effects on ISF processes. Particular attention is given to the ISF process parameters on the formability, deformation and failure mechanics, springback and accuracy and surface roughness. This leads to a number of recommendations that are considered essential for future research effort.

A TQCS-based service selection and scheduling strategy in cloud manufacturing

Abstract: Globalization, servitization, and customization in the marketplace are changing the way manufacturing enterprises do their business. Cloud manufacturing (CMfg) offers a possibility to perform large-scale manufacturing collaboration. However, CMfg systems are immature in many aspects. Service selection and scheduling is a key issue for practical implementation of CMfg. In this paper, a service selection and scheduling model is established, with criteria TQCS (time, quality, cost, and service) being considered. A fuzzy decision-making theory is adopted to transform TQCS values into relative superiority degrees. This is different from the traditional linear weighted method in most previous research, which results in large values of non-standardization error. The four relative superiority degrees are then combined linearly into an overall objective, in which the weight coefficients are calculated through analytic hierarchy process (AHP). Afterwards, ant colony optimization (ACO) is repurposed for the established service selection and scheduling model. Meanwhile, a selection mechanism is added to ACO (now ACOS) to enhance its validity. The simulation results demonstrate the practicality of the proposed model and the effectiveness of ACOS compared with other widely used algorithms.

Customized production based on distributed 3D printing services in cloud manufacturing

Abstract: 3D printing features highly digitized interfaces and automated processes for rapid prototyping and product customization. When distributed 3D printing resources are shared, gathered, and applied in a cloud platform, this will be a promising globalized and time-effective environment for customized production. However, how to intelligently and effectively manage and schedule distributed 3D printing services, such as dynamic evaluation, service intelligent matching, planning, and scheduling, in a cloud platform requires further in-depth study. In order to address this issue, this paper proposes a framework for a 3D printing service platform for cloud manufacturing (CMfg). In addition, 3D printing service online integration and 3D model library construction are analyzed. Moreover, some technologies of distributed 3D printing service management are discussed. Finally, some of the application tools and preliminary practices implemented by our team are introduced.

Data mining and statistical inference in selective laser melting

Abstract: Selective laser melting (SLM) is an additive manufacturing process that builds a complex three-dimensional part, layer-by-layer, using a laser beam to fuse fine metal powder together. The design freedom afforded by SLM comes associated with complexity. As the physical phenomena occur over a broad range of length and time scales, the computational cost of modeling the process is high. At the same time, the large number of parameters that control the quality of a part make experiments expensive. In this paper, we describe ways in which we can use data mining and statistical inference techniques to intelligently combine simulations and experiments to build parts with desired properties. We start with a brief summary of prior work in finding process parameters for high-density parts. We then expand on this work to show how we can improve the approach by using feature selection techniques to identify important variables, data-driven surrogate models to reduce computational costs, improved sampling techniques to cover the design space adequately, and uncertainty analysis for statistical inference. Our results indicate that techniques from data mining and statistics can complement those from physical modeling to provide greater insight into complex processes such as selective laser melting.

Tool wear predictability estimation in milling based on multi-sensorial data

Abstract: The safe and reliable operations in industrial manufacturing processes play a crucial role in the economic productivity. Machining process disturbances such as collision, overload, breakdown, and tool wear tend to cause production system failures. The current study aims at investigating the limitations of tool wear prediction on the milling of CGI 450 plates, through the simultaneous detection of acceleration and spindle drive current sensor signals. Tool wear prediction has been accomplished, by utilizing the experimental results that derived from third degree regression models and pattern recognition systems. These results indicate that predictability is affected by the mean signal energy, acquired from the vibration acceleration signals.

Influences of graphite reinforcement on the tribological properties of self-lubricating aluminum matrix composites for green tribology, sustainability, and energy efficiency—a review

Abstract: Aluminum/graphite (Al/Gr) composites have been used as self-lubricating materials due to the superior lubricating effect of graphite during sliding. This paper summarizes various tribological aspects of self-lubricating aluminum composites. The influence of various factors such as (a) material factors, graphite size and volume fraction, and (b) mechanical factors, applied load and sliding speed on the tribological properties of self-lubricating aluminum composites, is discussed. Furthermore, the tribological properties of self-lubricating composites as a function of these parameters and the active wear mechanism involved in various systems are discussed. Bringing self-lubricating composites into different operating systems is a solution to reduce the use of external toxic petroleum-based lubricants in sliding contacts in a way to help the environment and reduce energy dissipation in industrial components for strategies toward sustainability and energy efficiency.

Optimization of the plastic injection molding process using the Taguchi method, RSM, and hybrid GA-PSO

Abstract: This paper proposes a systematic optimization model of process parameters in plastic injection molding (PIM). Firstly, the Taguchi method is employed for experimentation and data analysis, in which the quality characteristics for the plastic injection product are length and warpage. The control factors for the process are melt temperature, injection velocity, packing pressure, packing time, and cooling time. Moreover, the signal-to-noise (S/N) ratio and analysis of variance (ANOVA) are used to obtain a combination of parameter settings. Experimental data are set for the response surface methodology (RSM) in order to analyze and create two quality predictors and two S/N ratio predictors. The two quality predictors are associated with genetic algorithms (GA) to search for an optimal combination of process parameters that meets multiple-objective quality characteristics. Finally, four predictors are combined with the hybrid GA-PSO to find the final optimal combination of process parameters. The confirmation results show that the proposed model not only enhances the stability in the injection molding process, including the quality in length and warpage, but also reduces the costs of and time spent in the PIM process.

Performances of Al2O3/SiC hybrid nanofluids in minimum-quantity lubrication grinding

Abstract: The present research investigated the lubrication performances of Al2O3/SiC nanofluid minimum-quantity lubrication (MQL) grinding in accordance with recent technologies used in conducting minimum-quantity lubrication grinding with nanofluids. The mean grain size of the Al2O3 and SiC nanoparticles (NPs) was set to 50 nm, and the difficult grinding Ni-based alloy was used as the workpiece material in the experiment. Grinding force was measure by using a three-component dynamometer and then used to calculate grinding force ratio (R). Workpiece surface roughness was measured by a roughness tester. Five groups of NPs were mixed with synthetic lipids at a mass fraction of 6 %. The lipids were then used as the grinding fluid for the nanofluid MQL grinding. Results showed that, compared with pure SiC NPs, pure Al2O3 NPs obtained lower R = 0.3, lower specific grinding energy (U = 75.93 J/mm3), and lower surface roughness (Ra = 0.386 μm), indicating better lubrication performance. The mixed NP consisting of Al2O3 and SiC NPs achieved even lower R and surface roughness than pure NPs because of the “physical synergistic effect.” The optimal ratio of the effect of mixed NPs was explored based on this finding. The Al2O3/SiC (2:1) mixed NPs obtained the smallest R = 0.28 and specific grinding energy (U = 60.68 J/mm3), thus indicating the best lubrication performance. Therefore, 2:1 is the optimal ratio for mixed NPs.

Cryogenic cooling-induced process performance and surface integrity in drilling CFRP composite material

Abstract: There has been a substantial growth in using carbon fiber-reinforced plastic (CFRP) composite materials in aerospace and automotive industries due to their superior properties. This experimental study presents results from a comprehensive and systematic study investigating the effects of cryogenic cooling on drilling performance and surface integrity characteristics of CFRP composite material. Experimental data on cutting edge radius of drill bit, outer corner wear of drill bit, trust force, torque, delamination factor, and surface integrity characteristics, including borehole subsurface damage and diameter error of drilled hole, are presented and analyzed comparing dry drilling with cryogenic cooling of CFRP composite material. The findings demonstrate that cryogenic cooling has a profound effect on reducing the cutting edge rounding of drill bit and outer corner wear; it also helps enhancing the surface integrity characteristics of produced hole. However, cryogenic cooling generates larger thrust force, torque, and thus larger delamination factor.

Laser welding dissimilar materials of aluminum to steel: an overview

Abstract: Joining aluminum to steel can lighten the weight of components in the automobile and other industries, which can reduce fuel consumption and harmful gas emissions to protect the environment. However, the differences of thermal, physical, and chemical properties between aluminum and steel bring a series of problems in laser welding. The main problems are how to control the thickness of the intermetallic compound layer and reduce or avoid the generation of pores, cracks, and thermal stresses which severely limit the mechanical properties of welded joints. Laser fusion-brazing technology utilizes the precise control of heat input with or without filler to partially melt the low melting temperature aluminum base material and promote wetting on the high melting temperature steel base material in order to achieve sound metallurgical by combining the advantages of fusion welding and brazing. Different forms of laser beam welding including single beam laser welding, dual-beam laser welding, and laser arc hybrid fusion-brazing welding are reviewed.

A genetic algorithm for energy-efficiency in job-shop scheduling

Abstract: Many real-world scheduling problems are solved to obtain optimal solutions in term of processing time, cost, and quality as optimization objectives. Currently, energy-efficiency is also taken into consideration in these problems. However, this problem is NP-hard, so many search techniques are not able to obtain a solution in a reasonable time. In this paper, a genetic algorithm is developed to solve an extended version of the Job-shop Scheduling Problem in which machines can consume different amounts of energy to process tasks at different rates (speed scaling). This problem represents an extension of the classical job-shop scheduling problem, where each operation has to be executed by one machine and this machine can work at different speeds. The evaluation section shows that a powerful commercial tool for solving scheduling problems was not able to solve large instances in a reasonable time, meanwhile our genetic algorithm was able to solve all instances with a good solution quality.

Mixed CUSUM-EWMA chart for monitoring process dispersion

Abstract: Every industrial process has to encounter two types of variation in product characteristic(s) that can be classified as common and special cause variations. These variations can exist in any parameters (like location, dispersion, shape, etc.) of the distribution of process characteristic. To handle the special cause variations, statistical tools are generally used to handle these special cause variations; statistical control chart is one of them. The most famous control charts are Shewhart, exponentially weighted moving average and cumulative sum charts, and their substantial modifications are available in the literature. In this article, we have proposed a new control chart named as mixed CUSUM-EWMA (called MCE) control chart for the efficient monitoring of process dispersion. The proposed MCE chart is compared with other existing control charts and some of their modifications. Average run length, extra quadratic loss, relative average run length, and performance comparison index are the measures that are used to judge the performance of charts. For practical considerations, an illustrative example with real data is also provided.

Dynamic characterization of machining robot and stability analysis

Abstract: Machining robots have major advantages over cartesian machine tools because of their flexibility, their ability to reach inaccessible areas on a complex part, and their important workspace. However, their lack of rigidity and precision is still a limit for precision tasks. Innovations and design optimization of robotic structure, links, and power transmission allow robot manufacturers to propose business solutions for machining applications. Beyond accuracy problems, it is also necessary to quantify the vibration phenomena that may affect, as in machine tools, the quality of machined parts and the tools and spindle lifespan. These vibrations occurred at specific machining conditions depending on robot and spindle dynamic properties. The robot’s posture evolved significantly in its workspace and induces dynamic’s changes observed at the tool tip that in turn impact the stability of the machining process. The objective of this paper is to quantify the dynamic behavior’s variation of an ABB IRB 6660 robot equipped with a high-speed machining (HSM) spindle in its workspace and analyze the consequences in terms of machining stability. Through an experimental modal characterization, significant variability of modal parameters is observed at the tool tip and impacts the stability of machining. The results show that an adjustment of the cutting conditions must accompany the change of robot posture during machining to ensure stability.

Emerging manufacturing paradigm shifts for the incoming industrial revolution

Abstract: Industrial revolutions have transformed manufacturing to mass production. However, there is a controversy about what production paradigm is emerging along with the incoming new industrial revolution. Most focus is either on smart manufacturing or 3D printing (additive manufacturing). This study converges smart manufacturing and additive manufacturing to form the long tail of making things as a paradigm shift for the new industrial revolution, which results in an ideal production system with the advantages of both economies of scale and scope. With the introduction of social aspects, socio-cyber-physical system (SCPS)-based manufacturing is proposed from the organizational semiotic perspective and exemplified by the four pillars of Future Internet. Furthermore, an extended version of the long tail is formed by the two modes of SCPS-based manufacturing: wisdom manufacturing and social manufacturing, which reviewed as the logical extension of smart manufacturing and additive manufacturing, respectively. Finally, a general interoperability model for SCPS-based manufacturing is suggested, which can also be applied to other integrated manufacturing systems.

Real-time control of microstructure in laser additive manufacturing

Abstract: A novel closed-loop process is demonstrated to control deposition microstructure during laser additive manufacturing (LAM) in real-time. An infrared imaging system is developed to monitor surface temperatures during the process as feedback signals. Cooling rates and melt pool temperatures are recorded in real-time to provide adequate information regarding thermal gradients, and thus control the deposition microstructure affected by cooling rates during LAM. Using correlations between the cooling rate, traveling speed, and the clad microstructure, a novel feedback PID controller is established to control the cooling rate. The controller is designed to maintain the cooling rate around a desired point by tuning the traveling speed. The performance of the controller is examined on several single-track and multi-track closed-loop claddings in order to achieve desired microstructures with specific properties. Results indicate that the closed-loop controller is capable of generating a consistent controlled microstructure during the LAM process in real-time.

Assessment of operational feasibility of waste vegetable oil based bio-dielectric fluid for sustainable electric discharge machining (EDM)

Abstract: Since the first application of electric sparks for the material removal was demonstrated, the electric discharge machining (EDM) process has gone through considerable changes in terms of technology and application. The process has surpassed the technological barriers by overcoming its then thought limitations like processing speed, material conductivity, dimensional and geometrical accuracies, and surface finish. However, environmental impact due to release of toxic emission products, operator health concerns due to release of toxic fumes, vapours and aerosols during the process, poor operational safety due to fire hazards and electromagnetic radiation, and toxic and non-biodegradable dielectric waste generated are some of the concerns still prevailing in EDM process. Authors, in this paper, have assessed the operational feasibility of waste vegetable oil (WVO) as possible alternative dielectric fluid and compared the response patterns of WVO with hydrocarbon oil, kerosene. Experiments were performed using spark current, gap voltage, pulse on time (pulse duration) and pulse off time (pulse interval) as control parameters to study the response behaviour for material removal rate (MRR), electrode wear rate (EWR) and tool wear ratio (TWR). The results obtained reveal that WVO-based bio-dielectric fluid can be used as an alternate to hydrocarbon-, water- and synthetic-based dielectric fluids for EDM. Besides the successful trials for operational feasibility assessment, application of bio-fluids offers a cleaner, greener and safer solution for dielectric to improve sustainability of EDM process by improving environmental friendliness, operational safety and personnel health issues of the process. Based on the experimental results and observations, the authors have suggested further scope of works to improve sustainability of the EDM process.

An experimental study on abrasive waterjet cutting of CFRP/Ti6Al4V stacks for drilling operations

Abstract: In the present study, CFRP/Ti6Al4V stacks were machined with abrasive water jet using different process parameters in order evaluate the viability of AWJ industrial application as a substitute of conventional drilling. The effect of the stack configuration, the traverse feed rate, the cutting tool (combination of orifice and focusing tube diameter and abrasive mass flow rate), and the pressure over the kerf profile, taper angle, and surface roughness has been analyzed through an ANOVA analysis and related to the physical parameters of the AWJ process. As a result, a positive taper angle is observed in Ti6Al4V while a negative is observed in CFRP in almost all cutting conditions. This leads to obtain an X-type or barrel-type kerf profile depending on the stack configuration. In addition, the surface roughness can be as low as 6.5 μm in both CFRP and Ti6Al4V materials at 95 mm/min when CFRP/Ti6Al4V configuration is used.

Evaluation of cutting force and cutting temperature in milling carbon fiber-reinforced polymer composites

Abstract: The cutting temperature and cutting force are some of the main factors that influence the surface quality of carbon fiber-reinforced polymer (CFRP). However, few investigations have been done on cutting temperature because it is difficult to capture the dynamic response of the temperature measurement system. Degradation of resin will occur within the machined surface or surface layer as the temperature exceeds the glass-transition temperature of the resin matrix. In this research, the relationship between cutting parameters and cutting temperature, cutting force were developed by response surface methodology (RSM). The experiments were designed using the tool-workpiece thermocouple technique. Taking into consideration the effect of the glass-transition temperature, the influence of cutting force and cutting temperature on surface quality of CFRP was analyzed. Analysis results showed that Spindle speed is the key parameter which influenced the cutting temperature while feed rate is the key parameter which influenced the cutting force in milling of CFRP. When the cutting temperature exceeds the glass-transition temperature (T g), the matrix cannot provide enough support to the fibers, and the machining quality of composite material is poor.

Clinching process for joining dissimilar materials: state of the art

Abstract: Clinching is a method for mechanically joining sheet metal of different thickness and properties in which the two plates to be joined undergo plastic deformation. The clinching process is established by connection or joining using simple tools: a punch and a die. This method has different characteristics compared to thermal joining methods, such as spot welding, including low purchase and operating costs, little preparatory work, safe and environmentally friendly, interesting mechanical properties, reproducibility, and durability. In this article, a brief review of traditional joining methods for dissimilar materials and the clinching process are illustrated in greater detail. In addition, the article looks to guide researchers for future work by identifying weaknesses of the current processes as well as potential for valuable contributions in the field of clinching.

Improving robotic machining accuracy through experimental error investigation and modular compensation

Abstract: Machining using industrial robots is currently limited to applications with low geometrical accuracies and soft materials. This paper analyzes the sources of errors in robotic machining and characterizes them in amplitude and frequency. Experiments under different conditions represent a typical set of industrial applications and allow a qualified evaluation. Based on this analysis, a modular approach is proposed to overcome these obstacles, applied both during program generation (offline) and execution (online). Predictive offline compensation of machining errors is achieved by means of an innovative programming system, based on kinematic and dynamic robot models. Real-time adaptive machining error compensation is also provided by sensing the real robot positions with an innovative tracking system and corrective feedback to both the robot and an additional high-dynamic compensation mechanism on piezo-actuator basis.

The effects of cryogenic-treated carbide tools on tool wear and surface roughness of turning of Hastelloy C22 based on Taguchi method

Abstract: In this study, Taguchi method has been applied to evaluate the effect of cryogenically treated tools in turning of Hastelloy C22 super alloy on surface roughness. The optimum parameters (cryogenic treatment, cutting speed, and feed rate) of turning were determined by using the Taguchi experimental design method. In Taguchi method, L9 orthogonal array has been used to determine the signal noise (S/N) ratio. Analysis of ANOVA was carried out to identify the significant factors affecting surface roughness. The statistical analysis indicated that feed rate, with a contribution percentage as high as 87.64 %, had the most dominant effect on machining performance, followed by the cryo-treated tools treatment and cutting speed, respectively. The confirmation tests indicated that it is possible to improve surface roughness significantly by using the Taguchi method. Surface roughness was improved by 28.3 and 72.3 % by shallow (CT1) cryogenic treatment and deep cryogenic treatment (CT2) applied on cementite carbide tools (UT). It found that wear resistance of tungsten carbide insert was increased by shallow and deep cryogenic treatments.