Welding processes group.
\r\n\tIn sum, the book presents a reflective analysis of the pedagogical hubs for a changing world, considering the most fundamental areas of the current contingencies in education.
",isbn:"978-1-83968-793-8",printIsbn:"978-1-83968-792-1",pdfIsbn:"978-1-83968-794-5",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"b01f9136149277b7e4cbc1e52bce78ec",bookSignature:"Dr. María Jose Hernandez-Serrano",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10229.jpg",keywords:"Teacher Digital Competences, Flipped Learning, Online Resources Design, Neuroscientific Literacy (Myths), Emotions and Learning, Multisensory Stimulation, Citizen Skills, Violence Prevention, Moral Development, Universal Design for Learning, Sensitizing on Diversity, Supportive Strategies",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 14th 2020",dateEndSecondStepPublish:"October 12th 2020",dateEndThirdStepPublish:"December 11th 2020",dateEndFourthStepPublish:"March 1st 2021",dateEndFifthStepPublish:"April 30th 2021",remainingDaysToSecondStep:"3 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Phil. Maria Jose Hernandez Serrano is a tenured lecturer in the Department of Theory and History of Education at the University of Salamanca, where she currently teaches on Teacher Education. She graduated in Social Education (2000) and Psycho-Pedagogy (2003) at the University of Salamanca. Then, she obtained her European Ph.D. in Education and Training in Virtual Environments by research with the University of Manchester, UK (2009).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"187893",title:"Dr.",name:"María Jose",middleName:null,surname:"Hernandez-Serrano",slug:"maria-jose-hernandez-serrano",fullName:"María Jose Hernandez-Serrano",profilePictureURL:"https://mts.intechopen.com/storage/users/187893/images/system/187893.jpg",biography:"DPhil Maria Jose Hernandez Serrano is a tenured Lecturer in the Department of Theory and History of Education at the University of Salamanca (Spain), where she currently teaches on Teacher Education. She graduated in Social Education (2000) and Psycho-Pedagogy (2003) at the University of Salamanca. Then, she obtained her European Ph.D. on Education and Training in Virtual Environments by research with the University of Manchester, UK (2009). She obtained a Visiting Scholar Postdoctoral Grant (of the British Academy, UK) at the Oxford Internet Institute of the University of Oxford (2011) and was granted with a postdoctoral research (in 2021) at London Birbeck University.\n \nShe is author of more than 20 research papers, and more than 35 book chapters (H Index 10). She is interested in the study of the educational process and the analysis of cognitive and affective processes in the context of neuroeducation and neurotechnologies, along with the study of social contingencies affecting the educational institutions and requiring new skills for educators.\n\nHer publications are mainly of the educational process mediated by technologies and digital competences. Currently, her new research interests are: the transdisciplinary application of the brain-based research to the educational context and virtual environments, and the neuropedagogical implications of the technologies on the development of the brain in younger students. Also, she is interested in the promotion of creative and critical uses of digital technologies, the emerging uses of social media and transmedia, and the informal learning through technologies.\n\nShe is a member of several research Networks and Scientific Committees in international journals on Educational Technologies and Educommunication, and collaborates as a reviewer in several prestigious journals (see public profile in Publons).\n\nUntil March 2010 she was in charge of the Adult University of Salamanca, by coordinating teaching activities of more than a thousand adult students. She currently is, since 2014, the Secretary of the Department of Theory and History of Education. Since 2015 she collaborates with the Council Educational Program by training teachers and families in the translation of advances from educational neuroscience.",institutionString:"University of Salamanca",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Salamanca",institutionURL:null,country:{name:"Spain"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"23",title:"Social Sciences",slug:"social-sciences"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"301331",firstName:"Mia",lastName:"Vulovic",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/301331/images/8498_n.jpg",email:"mia.v@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"6942",title:"Global Social Work",subtitle:"Cutting Edge Issues and Critical Reflections",isOpenForSubmission:!1,hash:"222c8a66edfc7a4a6537af7565bcb3de",slug:"global-social-work-cutting-edge-issues-and-critical-reflections",bookSignature:"Bala Raju Nikku",coverURL:"https://cdn.intechopen.com/books/images_new/6942.jpg",editedByType:"Edited by",editors:[{id:"263576",title:"Dr.",name:"Bala",surname:"Nikku",slug:"bala-nikku",fullName:"Bala Nikku"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"49596",title:"Use of Monoclonal Antibodies in Conditioning Regimen in Transplantation",doi:"10.5772/61509",slug:"use-of-monoclonal-antibodies-in-conditioning-regimen-in-transplantation",body:'Monoclonal antibodies (MoAbs) to treat a variety of benign and malignant diseases are used alone or in combination with conventional therapies. The use of MoAbs in autologous and allogeneic hematopoietic stem cell transplant (allo-HSCT) is subject to the following conditions for:
In vivo purging of graft and as a part of the conditioning regimens in the autologous or allogeneic HSCTs, and/or
Prevention or treatment of graft versus host disease (GvHD) developed after allo-HSCT.
The goals of the use of the MoAbs for in vivo purging of graft and/or as a part of conditioning regimens in autologous or allogeneic HSCTs are to obtain tumor-free stem cells, to reduce the recurrence, and to provide the resulting increase in the efficacy of transplantation on the underlying disease and the cure rate. Additionally, MoAbs in allo-HSCTs prevent the graft rejection and/or reduce the frequency and severity of GvHD. More frequent used MoAbs in the transplantation are: Rituximab, Radioimmunotherapeutics (RITs), Alemtuzumab, and Gemtuzumab Ozogamisin.
Rituximab, the chimeric anti-CD20MoAb is mostly used to treat a broad variety of B-cell non-Hodgkin’s lymphomas (NHL). Rituximab shows direct activity or complement-mediated cytotoxicity and antibody-dependent cytotoxicity. There are numerous studies on the use of conditioning regimens in autologous and allogeneic HSCTs settings.
The first study was reported by Flinn et al. [1] including 25 patients with a variety of NHL (11 follicular lymphoma, 7 mantle cell lymphoma, 5 chronic lymphocytic leukemia or small lymphocytic leukemia, 1 lymphoblastic lymphoma, and 1 marginal zone lymphoma). In this study rituximab was used for in vivo purging during the stem cell mobilization with cyclophosphamide (Cy) and also added in the myeloablative (MA) conditioning regimen including mostly Cy plus total body irradiation (TBI) and a further dose given after the engraftment. As a result, rituximab was well tolerated, engraftment was fast, and temporary neutropenia developed in the mean of 99.5 days in six patients but clinically significant infection was not reported.
Following study on the addition of rituximab for the conditioning regimen in autologous HSCT has been published by Flohr et al. [2]. In this phase II study, 27 patients with a variety of B-cell NHL in both the first day of the stem cell mobilization with chemotherapy and in the conditioning regimen,-10 and -3 days at the dose of 375 mg/square meter (sqm) rituximab have been used. The overall response rate has been reported as 96%. In the median follow-up of 16 months, disease free survival (DFS) and overall survival (OS) have been estimated as 77% and 95%, respectively. Another study of Khouri et al. [3] have evaluated the efficacy of rituximab use in the stem cell mobilization and after the transplantation in a total of 67 patients with diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL). Rituximab (375 mg/sqm) was infused 1 day before chemotherapy and again administered 7 days after chemotherapy at 1000 mg/sqm. In addition, rituximab has been given to the patients on the first and eighth day of 1000 mg/sqm dose following the high-dose BEAM conditioning regimen for autologous HSCT. The results were retrospectively compared with those of a historical control group (n=30 patients) receiving the same preparative regimen without rituximab. Although neutrophil engraftment in the rituximab arm was late in a statistically significant proportion (median 10 days vs.11 days, p <0.05), similar incidence of infection has been shown in the patients who received rituximab compared with the control arm. In the median 20 months of follow-up, they reported that the possibilities of progression-free survival (PFS) and OS significantly prolonged in rituximab arm (PFS: 43% versus 67%, p = 0.004; OS: 53% versus 80%, p = 0.002). In a multicenter study from 10 centers associated with the Italian group, the Gruppo ItalianoTerapie Innovative nei Linfomi, when retrospectively compared the high-dose therapies with and without rituximab in the patients with DLBCL and FL undergoing autologous HSCT, either as salvage or as first-line therapy for high-risk presentation, rituximab was administered in four doses during the high-dose therapy immediately before peripheral blood collection to exploit the drug’s in vivo purging effect, and two additional doses were usually used after autologous-HSCT. They found the similar response rate and early transplant-related mortality between rituximab (+) and (-), but the 5-year projected PFS and OS were better in those with rituximab (+) (p<0.0001) [4].
Hick et al. have also evaluated the use of rituximab in 23 patients with relapsed FL during the mobilization of stem cells for in vivo purging and during the 8th and 24th week after autologous HSCT for a four-week maintenance treatment protocol [5]. This study showed that rituximab provided permanent molecular remission in 77% of the patients associated with significantly prolonged PFS versus those with continued polymerase chain reaction PCR positivity.
Many single-arm phase II studies including small number of patients have been reported that the addition of rituximab especially reduced intensity conditioning (RIC) regimens in allogeneic HSCT settings reduced the incidence of acute or chronic GvHD and non-relapse mortality (NRM) [7-11]. In these studies, rituximab has led to an increase of serious infections due to long-term cytopenias and prolonged hypogammaglobulinemia.
Ultimately, there is no consensus regarding the dosage and scheme of rituximab use as a part of conditioning regimen for autologous and allogeneic HSCT in these studies. In addition, it is also not sufficient in randomized controlled trials demonstrating the superiority of adding rituximab. Therefore, prospective multicenter randomized trials aiming to determinate the exact role of rituximab for in vivo purging and/or as a part of conditioning regimens should be made in lymphoma patients.
Radioimmunotherapeutics (RITs) uses monoclonal antibodies directed against specific tumor antigens labeled with a particle emitting radioisotope to deliver radiation directly to the tumor. This type of treatment gives a high dose of radiation to tumor tissue and protects uninvolved tissues and organs [12-13]. Labeled antibodies to the antigen over-expression in the target tissue with radioactive substances specifically bind. For this purpose beta- particles are the most frequently used: Radioactive particles connected the MoAb slowly give out its radiation and kill the other nearby cells. This is called as cross fire. They give high tissue distribution with high nucleotides in the target tissue and homogeneous energy and provide the myeloablation or affect the large tumor mass. To achieve a favorable biodistribution of a radiolabeled monoclonal antibody, an ideal antigen would be expressed homogeneously on the tumor cell surface and would lack expression on normal cells. Lacking such an antigen, methods to target lineage-specific hematopoietic antigens, such as CD20, CD33, and CD45, have been successfully developed in the autologous and allogeneic-HSCT setting.
Currently there are two RITs in clinical use for indolent NHL [14]: Yttrium-90 ibritumomab tiuxetan (90Y-IT) (Zevalin) and iodine-131 tositumomab (131I-T) (Bexxar). There are studies on the use of high or standard doses as a part of the conditioning regimen for the transplantation.
Studies are generally on the use of single or combined with chemotherapy in the conditioning regimen for autologous-HSCT. In the first study, Press et al. conducted the phase I study in 43 relapsed B-cell lymphoma patients, and the administration of anti-CD20 and anti-CD37 antibodies labeled 131I-T alone in the conditioning regimen was to evaluate the toxicity and efficacy (15). The maximum tolerated dose was 27.25 Gy. However, researches have shown that patients administered more than this dose had cardio-toxic effects. In addition, the biodistribution of the antibodies in the patients with the large spleen size and a large tumor mass were emphasized not to be good in the study. The overall response rate of 95% with high-dose RIT (84% complete response and 11% partial response) and tumor response were calculated as the median of 11 months. Subsequently, the same researchers have made a phase II study with anti-CD20 labeled 131I-T in 25 patients with relapse NHL [16]. In this study, they have reported that PFS was 62% and 93% of OS with the median 2-year follow-up. Similarly, Liu et al. found a median PFS of 42% and 68% of OS in median 42-month follow-up [17]. This was followed by similar studies regarding the use of the high-dose RITs. However, due to the gamma radiation emitted by 131I-T most subsequent studies had been conducted with 90Y-IT, a pure beta emitter [18-21]. There was no need to prolong strict patient isolation and contact alert in 90Y-IT in contrast to131I-T. Besides, disease statuses prior to the transplantations in those studies were also variable. Although the use of high-dose RIT was planned for the patients unable to tolerate high-dose treatment, the majority of patients in the studies was under 60 years of age and had chemosensitive relapses. Additionally, there are no prospective studies to prove RITs’ superiority to chemotherapy and/or radiotherapy. Furthermore, this treatment should be administered in specialized centers.
To overcome the problems related to the safe yield of high-dose RITs, the efficacies of standard-dose RITs combined with chemotherapy in preparative regimens of the transplantation have been assessed in the following studies. The results in several single-arm studies not including control group were impressive. In a randomized trial, the Blood and Marrow Transplant Clinical Trials Clinical Trials Network (BMT-CTN) 0401 in which 131I-T-BEAM or Rituximab-BEAM were given to the patients with chemosensitive relapse of DLBCL [22], disease control and survival effects of RIT could not have been shown. A randomized study compared 90Y-IT-BEAM with BEAM alone in recurrent B cell NHL was closed early by reason of the slow patient recruitment. As their evaluation with a small number of cases, it was the first randomized study that proved that higher DFS and OS were in the RIT arm. Nevertheless, the published studies do not support the routine use of standard-dose RITs in DLBCL.
Some studies in chemorefractory DLBCL patients who underwent autologous-HSCT conditioned by standard-dose RIT have been reported as two or three year PFS and OS 39%–63% and 55%–67%, respectively [23-26]. In the European Mantle Cell Network MCL-3 study 90Y-IT was given to patients younger than 66 years one week prior to BEAM or BEAC (Carmustine, Etoposide, Cytarabine and Cyclophosphamide) conditioning regimen [27]. When compared with the results of the MCL-2 study, they concluded that there was no benefit in the patients undergoing autologous-HSCTas a first-line intensification treatment.
In allogeneic HSCT, RIT has generally been added to the RIC regimens in the refractory NHL patients. One of the first studies where Shimoni et al. gave 90Y-IT with fludarabine-based conditioning regimens to 12 patients with chemorefractory CD20+ NHL demonstrated the safety of RIT [28]. Subsequently, several studies related to adding RITs to the conditioning regimen have been published [29,30]. Although allogeneic HSCTs made by adding RITs to RIC regimes have reliability in these studies, it has not been shown to be superior to the transplantations with RIC regimens not including RIT yet.
There are some studies related to the adding of RITs to the preparative regimens in acute leukemia and myelodysplastic syndrome (MDS) as well. Initially, 131I-labeled M195, the mouse Moabs of lintuzumab (reactive with CD33) was used in 10 patients with relapsed or refractory acute myeloid leukemia in a phase I study from the Memorial Sloan Kettering Cancer Center [31]. Subsequently, 131I-labeled anti-CD33 MoAbs were added to standard myeloablative regimen in 31 patients with refractory or relapsed AML (n=16), accelerated or blastic phase chronic myeloid leukemia (CML) (n=14) or advanced myelodysplastic syndrome (MDS) (n=1) underwent allogeneic HSCT from their related donor [32]. The median survival was calculated as 4.9 months (range 0.3–90+ months). Three patients with relapsed AML remain in complete remission more than 5 years.
Based on the feasibility of MoAbs, investigators have focused on the CD45, the other antigen. The CD45 antigen, common leukocyte antigen, is expressed on the surface of virtually all hematopoietic cells, except mature red cells and platelets. In addition, CD45 expression has been detected in 85% to 90% of AML and ALL, and the antigen does not internalize after the antibody binding. In a phase I study conducted by the Fred Hutchinson Cancer Center, RIT with 131I-anti-CD45 has been implanted one week prior to the conditioning regimen including Cy-TBI in AML beyond the first complete remission (CR), acute lymphoblastic leukemia, and MDS-excess blast [33]. The patients of this study have undergone allogeneic HSCT from their human leukocyte antigen (HLA) -identical relative donors or autologous HSCT. This first study has shown that the radiation with acceptable toxicity should be given to the bone marrow and spleen. Subsequently RIT with 131I-anti-CD45 has been added to the conditioning regimen with busulfan (Bu) plus Cy (BuCy) in the patients with the first CR AML [34]. Three-year non-relapse mortality and disease-free survival in this study was calculated at 21% and 61%, respectively. They have reported that the results were comparable by the International Bone Marrow Transplantation Registry (IBMTR) data using only BuCy in allogeneic HSCT. Similarly Pagel et al. added the RIT to the RIC regimens in elderly patients with advanced stage or high risk AML and showed the reliability of RIT in a phase I study [35]. Same researches used the 131I-anti CD45 targeted radiotherapy in combination with Fludarabine plus 2 Gy TBI in younger patients (age 16 to 50 years) with advanced AML or high-risk MDS who underwent allogeneic HSCT [36]. They aimed to define the maximum tolerated dose (MTD) of radiolabeled anti-CD45 antibody in addition to non-myeloablative conditioning regimes and to create better antitumor control with minimal toxicity in comparison with standard myeloablative regimens. Their study suggested that a maximum dose of 28 Gy could be delivered to the liver and the arbitrary limit of 43 Gy to the marrow might be unnecessarily conservative. Conjugation of anti-CD45 antibody with alternative radioisotopes including 90Y is currently explored in clinical trials.
Another attempt in the studies was the use of anti-CD66 moAbs in leukemic patients. But leukemic blasts do not express CD66. Therefore, the anti-leukemic effect of CD66 RIT depends on “crossfire” from the beta-particles emitted by 188-Rhenium (Re). 188Re-labeled anti-CD66 moAbs were used as a part of the standard myeloablative conditioning regimen including total body irradiation (12 Gy) (n = 30) or busulfan (n = 27) and high-dose cyclophosphamide +/- thiotepa prior to allogeneic or autologous HSCT in 57 patients with high-risk AML or MDS [37]. In median 26 months follow-ups, disease-free survival were 64% for 44 patients in the first or second CR or in very good partial remission (less than 15% blasts in the marrow at transplantation) and only 8% for those with more than 15% blasts in the marrow at transplantation. Likewise, targeted marrow irradiation with 188Re-anti-CD66 moAbs were used in 20 patients with Philadelphia chromosome-positive acute lymphoblastic leukemia or advanced CML prior to allogeneic HSCT [38]. With a median follow-up of 54 months (range 23–81) overall and disease-free survival were 29% (95%-CI 14–58) and 25% (95%-CI 12–53), respectively. Subsequently, conjugation of anti-CD66 with 188Re or 90Y were added to a reduced intensity conditioning regimen in 20 patients with a median age of 63 years (range: 55–65 years) suffering from acute leukemia (n=17) or MDS (n=3) [39]. The probability of survival was estimated as 70% at 1 year and 52% at 2 years post-transplant. They concluded that 90Y-anti-CD66 moAbs were more feasible and less nephrotoxic than 188Re.
Briefly, the use of RIT is an attractive approach to increase conditioning prior to HSCT. The randomized studies in refractory aggressive or indolent NHL show the superiority of adding RIT. Nevertheless, the addition of standard dose RIT to the conditioning regimen in autologous transplantation is a valuable research topic. In allogeneic transplantation, until displaying the superiority of RIT-based conditioning regimen in controlled randomized studies, this approach should only be considered within clinical trials.
Alemtuzumab is a human originated MoAbs to CD52 that normally expresses on B and T lymphocytes, macrophages, monocytes, natural killer cells, and some dendritic cells. While alemtuzumab efficiently reduces both T and B cells from the circulating blood, it has minimal or no effect on hematopoietic progenitor cells [40].
Anti-CD52 is often used in the treatment of chronic lymphocytic leukemia. But adding CD52 to the conditioning regimens in allogeneic HSCTs in many malign hematological diseases has reduced the frequency and severity of GvHD as well as decreased the risk of graft rejection [41-43]. Also alemtuzumab in combination with fludarabine and Cy in allogeneic HSCT for acquired aplastic anemia was associated with a very low incidence of chronic GvHD and excellent survival [44-49]. However, the studies have reported that alemtuzumab led to increase the frequency of opportunistic infection, in particular Cytomegalovirus, Epstein–Barr virus, and Adenovirus, and the risk for the recurrence of the underlying disease due to the reduction of graft versus tumor effects.
Gemtuzumab ozogamisin (GO) is a moAbs to CD33 conjugated with human calicheamicins. It has been withdrawn from the market by the US Food and Drug Administration in 2010 because of the increasing risk of liver sinusoidal obstruction, and a lack of data for the efficacy and safety. Recently many studies have been published about the use of GO in the treatment of CD33+ AML patients as a part of induction therapy or consolidation [50-51]. Furthermore, phase I/II studies have reported that the use of GO as a part of MA or RI conditioning regimens in allo-HSCT setting could be safe and efficient in poor-risk AML patients [52-54]. In addition, a pilot study has been recently published about the administration of GO combined with azacytidine as the maintenance treatment of post-transplant relapses in AML [55].
Although many studies have been published for the additions of MoAbs to the conditioning regimens for HSCTs, there are no sufficient data to determine the optimal dose and administration schedule of the MoAbs until now. However, rituximab recently has been widely used in many single-arm studies in NHL patients who underwent allogeneic HSCT. Another controversial issue is about the use of RITs. Randomized data do not support incorporating RITs into the conditioning regimens for either autologous or allogeneic SCT settings. The high-dose RITs should be used in refractory or advanced malign hematological disease in specialized centers though lacking randomized data. The standard-dose RIT is also a good research topic for lymphoid malignancies planning high dose therapy with autologous rescue or allogeneic SCT.
Moreover, given the reduced risk of graft failure and GvHD with alemtuzumab but increased risk of disease relapse and the incidence of opportunistic infections, the use of alemtuzumab in the allogeneic SCT should be considered in patients with matched unrelated or mismatched related donors. Owing to the shortage of studies on GO, another MoAbs, with the reuse, GO should be used in clinical trials.
In conclusion, it is anticipated that additional MoAbs to the conditioning regimens will be routinely used in the next door following by the proven clinical efficacy and safety.
One of the most important processes of joining metals is welding process, like the one that appears in [1]. It is used in simple structures fabrication, nuclear and petroleum industries, as well as chemical components.
In a typical fusion welding process of metals, such as resistance welding, arc welding, electron beam welding, laser welding, a heat source is applied locally to the interfaces of the two metals to be joined. The interface can be metals’ surfaces, where faces of each other are joined by a nugget (e.g., spot or resistance welding). In arc welding, the interface will be the weld seam. However, complex physical phenomena and processes occur due to the heating/melting and cooling/solidifying. This may produce adverse effects on weld properties and base metal properties [2]. In order to reduce adverse effects and obtain desired results, many studies have been developed to monitor, predict, or control welding processes. All these studies are based on the optimal welding parameters’ adjustment, but all of these are adjustable.
All adjustable welding parameters, such as current or current waveform, heat input, wire feed speed, travel speed, and arc voltage, may be used as system inputs and be designed to assure the required outputs. For that reason and the interrelations-parameters complexity, welding process can be analyzed like a stochastic system, which has input and output parameters and several disturbances [2]. Chen’s article [3] was related to the need to improve the information acquired from these welding parameters and identify characteristics in order to improve and control the welding process results. Chen defined new objectives of modern welding manufacturing technology to show the way for better welding processes. It exposes some problems of the intelligentized welding manufacturing technology (IWMT), which are shown in Figure 1.
Some technical problems in IWMT [3].
Other science areas present the potential to solve these problems. Computer science areas have had great results with new technique applications of data analysis, learning models, and intelligent control. Data analysis objective indicates nontrivial features on a large amount of data. Due to the increase and complexity of data, more efficient data analysis techniques have been developed. Welding process can be analyzed with this point of view. So, the welding process analysis with new techniques is nothing more than a continuity in the development of welding analysis processes. This interdisciplinarity is one of the necessary contributions proclaimed by the so-called Industry 4.0, like the one shown in [4, 5, 6].
The fourth industrial revolution refers to the next manufacturing generation, where automation technology will be improved by self-optimization and intelligent feedback [7]. For this reason, the application of the most recent data analysis techniques and processes can contribute to a better control and monitoring of welding processes. These techniques can be joined in machine learning techniques [8, 9, 10, 11, 12], data mining process [13, 14, 15, 16, 17], and control process [18, 19, 20]. The interrelation of these areas and their origins are presented in Figure 2. Machine learning is a growing area in computer science, with far-reaching applications, for data analysis [21]. Machine learning uses computer theory and statistics for building mathematical models with the goal of making inference from a sample [22]. One branch in machine learning with fast growing is deep learning. These methods are an essential part of the research on speech recognition in the state of the art [23], image recognition [24, 25, 26], object detection [27, 28], videos [29, 30], and sound [31, 32] analysis. Interesting patterns come out from such a machine learning techniques. One important process is data mining. Data mining puts strong emphasis on different aspects, like efficiency, effectiveness, and validity of process [33]. Data mining processes define several stages and methodologies to achieve these objectives, as exposed by Marbán in [34]. An important objective of data analysis is to reveal and indicate diverse, nontrivial features in data. For this reason, welding process can be analyzed with this point of view.
Origin diagram of the new data analysis techniques.
A search conducted in the Web of Science from 2011 to October 3, 2018, shows the growing trend of these new data analysis techniques and processes in welding process researches Figure 3, but when comparing with the investigations on models welding process, growth is almost imperceptible, as appearing in Figure 4.
Cited per year on welding (Web of Science [35]).
Cited per year on welding (Web of Science [35]).
These demonstrate the need for this review to show these techniques, the advantages in their applications, and the increasing trend of their utilization. This review can be resumed in following stages:
Welding process—understanding of welding processes being analyzed.
Sensors—analysis of some principal sensors in welding process.
Data processing—analysis of technique to transform sensors information to welding process dataset.
Modeling welding process—analysis of some modeling techniques in welding process.
Intelligent control of welding process—analysis of some intelligent control techniques in welding process.
These stages has a close relationship with data mining processes as a sample [34].
American Welding Society (AWS) definition for a welding process is:
“a materials joining process which produces coalescence of materials by heating them to suitable temperatures with or without the application of pressure or by the application of pressure alone and with or without the use of filler material” [36].
AWS defines groups of welding techniques depending on the energy transfer mode. The processes analyzed in this chapter are grouped as shown in Table 1.
Group | Welding process |
---|---|
Arc welding | Gas metal arc welding (GMAW) |
Gas tungsten arc welding (GTAW) | |
Plasma arc welding (PAW) | |
Shielded metal arc welding (SMAW) | |
Submerged arc welding (SAW) | |
Variable polarity plasma arc welding (VPPAW) | |
Rotating arc narrow gap MAG welding (RANGMW) | |
Girth welds | |
Resistance welding | Resistance spot welding (RSW) |
Large scale RSW (LSRSW) | |
Other welding processes | Laser beam welding (LBW) |
Welding processes group.
These groups present different parameters and characteristics that were analyzed in the articles presented in this chapter.
The group arc welding is characterized with electric arc. The electric arc is the heat source most commonly used in fusion welding of metallic materials. The welding arc comprises a relatively small region of space characterized by high temperatures (similar to or even higher than the sun’s surface), strong generation of light and ultraviolet radiation, intense flow of matter, and large gradients of physical properties. It has an adequate concentration of energy for localized base metal fusion, ease of control, low relative cost of equipment, and an acceptable level of health risks to its operators. The study of the arc is of special interest in areas such as astrophysics and the electrical and nuclear industries [37]. The electric arc generates a complex interrelation of thermal, electrical, and magnetic parameters. These are hampering much of their studies based on definite theoretical formulations. Despite many studies, the electric arc is quite complex and the knowledge so far allows a partial understanding of the phenomenon [1].
Resistance welding is the joining of metals by applying pressure and passing current for a length of time through the metal area that is to be joined. Its principal advantage is no other materials are needed to create the bond; this reason makes this process extremely cost effective. Resistance welding is applied in a wide range of automotive, aerospace, and industrial applications. Among the main parameters are welding time, welding force, contact resistance, materials properties [1]. Resistance spot welding, like all resistance welding processes, creates welds using heat generated by resistance to the flow of welding current between the faying surfaces, as well as force to push the workpieces together, applied over a defined period of time. Resistance spot welding uses the electrode face geometries to focus the welding current at the desired location, and apply force to the workpieces. Once sufficient resistance is generated, the materials set down and combine, and a weld nugget is formed [36]. The process is fast and effective, and it is also complicated due to complex interactions between electrical, mechanical, thermal, and metallurgical processes. The heat generation in RSW is due to the resistance of the parts being welded to the flow of a localized electric current, based on Joule’s law. The quality of the joint in RSW is influenced by the welding parameters. These parameters mainly include welding current, welding time, electrode force, and electrode geometry [38]. Large scale resistance spot welding (LSRSW), as mentioned in Table 1, is generally adopted in the automotive industry. It is an automotive structure that includes thousands of spot welds. It presents the same parameters and complexity as RSW; only the parameters related and influenced by its scalability are increased [39].
In this group, AWS presents various welding processes. Laser welding is the only one belonging to this group, which is found in the analyzed articles.
Laser beam welding is one of the most technically advanced welding processes. Laser welding is in general a keyhole fusion welding technique which is achieved with the very high power density obtained by focusing a beam of laser light to a very fine spot [40]. This light ray heats metals up quickly so that the two pieces fuse together into one unit. The light beam is very small and focused, so the metal weld also cools very quickly. Laser welding operates in two fundamentally different modes: conduction limited welding and keyhole welding. The mode in which the laser beam will interact with the material is welding; it will depend on the power density of the focused laser spot on the work piece [41].
Other parameters that are present in these processes are those of final welding geometry, which behave differently in different processes and under different conditions. The parameters of the respective sources generate their influence on the final result of each welding process.
Welding is a complex process, so it requires more intelligent techniques in its analysis, monitoring, and production quality improvement. The use of sensors allows the acquisition of process parameters. The new artificial intelligence techniques will allow a better study, modeling, and control of these processes.
Several sensors have been applied in the welding process for monitoring. The weld bead and the weld-pool indirect sensing technologies can be classified like exposed in [42] and in Figure 5.
Some indirect monitoring technologies in welding process [42].
Infrared vision techniques have been widely applied in the welding process [43, 44, 45, 46, 47, 48, 49, 50]. One of the problems of this technique is that the environment where it is applied can interfere in the precision of the data obtained from a process. This may be due to the own heat emission of the technologies utilized.
Sound may indicate conditions that generate weld defects. Acoustic information plays a relevant role for expert welders, as described in [51]. Sound signature produced by GMAW contains information about arc column behavior, the molten metal, and the metal transfer mode. High-speed data acquisition and computer-aided analysis of sound signature may indicate conditions that generate weld defects [52, 53]. Di Wu, in 2016 [54], tried to monitor penetration and keyhole with acoustic signals and image analysis. Lv et al. [55], proposed a recognition model to analyze the relationship between penetration state and arc sound. In 2017, Lv et al. [56] again presented a welding quality control in pulse gas tungsten arc welding (P-GTAW). The welding acoustic signal was used to analyze the design of an automated welding penetration control system.
In welding, it is easy to capture sound, but it is very difficult to analyze the noises and differences of intensities that are sometimes generated. This is not a problem to sound deep learning technique like present [32, 57]. To understand the welding sound analysis with deep learning techniques, it is necessary make an image arc correlation to know what happens in welding arc.
Vision sensor is largely utilized in welding process to analyze weld-pool process [58, 59], arc-welding process [60, 61], and weld bead geometry [62, 63]. The more light generated by arc can be difficult for the image obtention. Some techniques are utilized. One of them was utilized by Chen in 2010 [64].
He made monitoring and control of the hybrid laser-gas metal arc welding process with an economical sensor system, and a coaxial vision system, which was integrated from a relatively inexpensive industrial vision system and a personal computer (PC). Another visualization technique is Shadowgraphy, applied in Esdras Ramos investigation, in 2013 [65, 66]. This is based on process shadow arc with laser source.
In [60], a laser illumination was utilized. To reduce the arc light, a narrow band interference filter was applied. For precise measurements, an image-analysis technique was used. This technique can be used to obtain high quality images but only it can be used in processes without material transfer.
Chen et al. [67] utilized a visual double-sided sensing system. In one frame, the weld-pool geometry parameters in GTAW process were determined.
With high speed illumination laser in [68], great quality images are obtained. This technique is more recent one but it needs a laser with more potentiality than Shadowgraphy technique. This technique is more expensive too.
Some papers define their own image processing technologies, like Hong Yue in 2009 [69], where the weld image processing adopts the classic techniques such as Laplacian, Gaussian, neighborhood mean filters, and threshold segmentation. Yanling Xu, in 2014 [70], proposed the Canny edge detection algorithm for detecting edges and extracting pool and seam characteristic parameters. Qian-Qian Wu in [71] researched to find out the optimal algorithm to filter. He made a comparison of Wiener filter, Gaussian filter, and Median filter on welding seam image. In the classic image processing, it is very difficult to generalize a filter or algorithm, because it depends on the conditions and characteristics of camera parameters and light.
Another problem with these algorithms mentioned above is that the real-time analysis has an insufficient response time to be utilized in a process control despite recent developments in computational resources.
Deep learning techniques have efficient result in real-time executions [28] and classifications [24, 25] despite classifications on new images. One example applied in welding process is [62, 63]. It utilizes autoencoder deep learning technique to extract features of images process in laser welding. Another example of recent application of deep learning technique is [72]. It presents a method based on deep learning aims to extract information from photographs on spot welding. This monitoring system on the spot welding productive line shown better performance than the previous images analysis.
Not focused on welding arc analysis, but with good results, the work [73] proposed an automatic detection for weld defects in X-ray images. A classification model on deep neural network was developed. The accuracy rate of the proposed model was 91.84%. This was one more example of the potential of these techniques in welding area on images processing.
Today’s manufacturing environments has a rapid advancement on demand for quality products. Many techniques and methods are applied to correlate between process parameters and bead geometry. One of them is response surface methodology (RSM). It was applied by Sen in 2015 [74]. He made to evaluate the correlations between process parameters and weld bead geometry in double-pulsed gas metal arc welding (DP-GMAW). Santhana Babu [75] with the same technique got good results for predicting and controlling the weld bead quality in GTAW process. The problem of this method is that the researcher can find the equation, called response surface, by test and error. This can be very difficult. Many theoretical models have been defined to determine the process that occurs in the welding arc, including [76]. The main problems of these models were that they lose precision because it was very difficult to obtain a formula that contains all the complexity of these processes, as well as affirmed by Hang Dong in [77]. Mathematical models, based on machine learning techniques, have better results in problems as complex as this one. In the same paper, Hang Dong expressed the potential of these models.
One of the well-known and utilized regression algorithms is the least squares method. It was utilized in [78] to predict the seam position under strong arc light influence. Other work is [79] a LR model that is utilized to analyze the pool image centroid deviation and weld based on visual weld deviation measurement in GTAW process. The other technique is Gaussian process (GP) regression (GP), which was utilized in [77] to predict better performance in arc welding process of GTAW process.
An interesting method, utilized in [80], was Mahalanobis Distance Measurement (MDM). It was employed to determine welding faults occurrences. The same method was utilized in 2017 by Khairul Muzaka [81] on GMAW process to optimize welding current on a vertical-position welding. One problem of this method is that only correlate in function one input.
Bai and Lubecki [82] proposed a Localized Minimum and Maximum (LMM) analysis method in real time for welding monitoring system. The problem of LMM is that it exposes a simple function to measure the quality than not defining the complexity of the system. That is why, this work is limited only to the short-circuit transfer mode.
In 2017 by Junheung Park [83], a SVM was proposed with bootstrap aggregating that reduced the noisy on RSW data with computational efficiency. In this framework, other techniques as Generalized Regressive Neural Networks (GRNN) and Genetic algorithms for optimization were joined. This article demonstrates an increase in more complex computer science techniques for better analysis of welding processes. But the only way to know if all this was necessary is comparing with other techniques.
Some researchers already had this reference of advantages of these algorithms. Bo Chen in 2009 [84] utilized ANN to training the experimental obtaining data. The good result of ANN prediction was validated by D-S evidence theory information fusion. They have also been utilized for different purposes and in different welding processes such as in SAW process [85] and GMAW cold metal transfer (CMT) process [86], for predicting weld bead geometry; in GTAW process, for predicting the angular distortion considering the bead geometry [87]; in girth welded pipes process, for predicting residual stresses [88]; and in underwater wet welding process, for predicting the weld seams, geometric parameters [89].
For better results, ANNs have been mixed with other techniques. One example is [90], where ANN and Support Vector Machine (SVM) are utilized for welded defect detecting and monitoring on a laser welding process. The other technique is by Bo Chen and Shanben Chen [91] for predicting the penetration in GTAW process. But they used different ANNs to process information from different sensors, and finally, they used the predictive fuzzy integral method.
Another example is [92], for predicting bead height and width in GMAW process using ANN Fuzzy ARTMAP, like monitoring task.
The increase in computational resources has allowed an increase in the complexity of ANN architectures. These are called Deep Neural Networks (DNN). They, bit by bit, begin to be applied in the welding process. One of them utilized was in [93]. The model is based on a DNN architecture to make a study of the estimation of weld bead parameters. This article mixed data from different welding processes. This is a risk for results analysis since different processes can have different outcomes with the same input parameters.
Rao et al. [94] utilized Generalized Regressive Neural Networks (GRNN) technique for estimating and optimizing the vibratory assisted welding parameters to produce quality welded joints. But in this case, it does not have comparison with other algorithms.
Di Wu, in 2017 [95], wrote a paper that addresses to perform Variable Polarity Plasma Arc Welding (VPPAW) process. Deep Belief Network (DBN), DNN variant, and t-Stochastic Neighbor Embedding (t-SNE) were studied for monitoring and identifying the penetration values. Experimental comparisons and verifications expose better performance for DBN, 97.62% exactly. This reaffirms the good results offered by the learning models developed with these algorithms. This work does not take the advantage of DNN algorithms to analyze both images and sound in real time.
Figure 6 shows a summary of articles analyzed. It shows that ANNs are one of the most used techniques, but they do not always offer the best result. This demonstrates the need to make comparisons between various modeling techniques in order to define the best result, in terms of efficiency and computational cost.
Comparison between ANNs and ANN variations.
As it has been expressed in the previous sections, there are new techniques to analyze very complex systems. But they require expensive computational resources for their construction and sometimes for their execution. A comparison between models will allow to know which model has better results and which model can be the most effective to be utilized. This effectivity is measured in function of problem necessity, like the one shown in data mining (DM) methodologies and processes [16, 17].
An interesting comparison is Support Vector Machine (SVM) and ANN model, to identify weld groove state and weld deviation extraction in rotating arc narrow gap MAG welding (RANGMW) [96]. It presented SVM models with better results than ANN model.
One comparison with focus on time optimized was [97]. It utilized an ANN and ANN with differential evolutionary algorithm (DEA) separately. The results obtained by ANN using DEA were closer to ANN, but the computational time of ANN using DEA was shorter.
In the article [98], Response Surface Methodology (RSM) was compared with linear isotonic regression, regression (LR), regression trees, ANN, GP, and SVM, to evaluate mechanical properties in GMAW process. The results present that the DM models have poorer generalization on this research, because DM techniques require, to obtain acceptable results, a large amount dataset.
Sumesh in 2015 [99] compared Decision Trees (DT), ANN, Fuzzy Logic, SVM, and Random forest technique Weld Quality Monitoring in SMAW. The most efficient technique was Random forest. This shows that not always the most complex techniques offer the best results.
One of the few comparative analysis algorithms is Kumar’s paper in 2016 [100]. This paper explores Self-Organizing Maps (SOM) using as a mechanism for performing unsupervised learning, for comparing performance characteristics of various welding parameters which include welding power supplies and welders. Results obtained using SOM has been compared with the Probability Density Distributions (PDDs) obtained during statistical analysis. Voltage and current data analyzed using the SOM technique can also be utilized to evaluate the arc welding process. These studies demonstrate that there are other potential algorithms for welding process analysis. For that reason, it is necessary to evaluate and compare several of them to be agreed upon in a real-time process.
Other comparison in 2016 by Di Wu is [54]. The article compared a prediction model for Plasma Arc Welding based on Extreme Learning Machine (ELM) with ANN and SVM techniques. The ELM model had better generalization performance and was faster than others. This potentiality was established too by Nandhitha in 2016 [106]. He utilized GRNN and Radial Basis Networks (RBN) for torch current prediction in GTAW process. The torch current deviation was 98.95 % accuracy for the best result of GRNN.
In 2016 too, Kyoung-Yun Kim [107] discusses that in Resistance Spot Welding (RSW) process. He examined the prediction performance with GRNN and k-Nearest Neighbor (kNN) algorithms. The results indicate that with smaller k of kNN, the prediction performance measured by mean acceptable error has increased.
Other quality welding article was Xiaodong Wan in 2017 [102]. It proposed a Probabilistic Neural Network (PNN) model for quality prediction in large scale RSW process. In this case, the PNN model was more appropriate in quality level classification than the Back Propagation Neural Network.
The one of the last articles with direct DM techniques and welding relation is of Yiming Huang in 2017 [103]. This is an investigation of porosity on pulsed gas tungsten arc welding (P-GTAW) with an X-ray image analysis. To detect, an Empirical Mode Decomposition (EMD) and Spectral Analyses were made based on DM.
In 2017, Petković [104] predicted the laser welding quality by training data for the computational intelligence methodologies and support vector regression (SVR). SVR is a novel variant of SVM for regression task. This article made a comparison between SVR, ANN, and GP. It is another example that in certain problems, less complex algorithms can offer better results.
Table 2 presents a series of articles that were based on the monitoring and quality of the welding processes. The column Preparation defines the technique of processing the data obtained by the sensors; Classic for processes that do not use the latest techniques of image processing and DL for the use of deep learning; Online defines if the model was executed in real time; Compare, if in the research carried out in the article, a comparison is made between several algorithms; and Modeling defines the algorithms used in specific article. When a comparison exits, the first model before coma was the best quality result. As Tables 2–4, the best algorithm does not always match.
Author | Year | Welding process | Sensors | Data preparations | Modeling | Online | Compare |
---|---|---|---|---|---|---|---|
Saini [52] | 1998 | GMAW | Sound | Classic | No | Yes | No |
Yue [69] | 2009 | Pipeline welding | Visual | Classic | Theoretical model | No | No |
Chen [64] | 2010 | LBW/GMAW | Visual | Classic | Yes | No | |
Horvat [53] | 2011 | GMAW | Sound | Classic | No | Yes | No |
Gao [78] | 2011 | GTAW | Visual | Classic | LR-ANN | No | No |
Feng [80] | 2012 | GMAW | Standard | Classic | MDM | Yes | No |
Fidali [45] | 2013 | GMAW | Infrared | Classic | Statistical analysis | Yes | No |
Sreedhar [48] | 2013 | GTAW | Infrared | Classic | Statistical analysis | Yes | No |
Kalaichelvi [101] | 2013 | GMAW | Standard | Classic | GA-Fuzzy | Yes | No |
Kumar [97] | 2014 | GMAW | Visual | Classic | ANN, ANN-DEA | Yes | Yes |
Deyong You [90] | 2015 | Laser welding | Photodiode, spectrometer | WPD-PCA | FFANN-SVM | Yes | No |
Sumesh [99] | 2015 | SMAW | Sound | Classic | Some DM (RF) | Yes | Yes |
Kumar [100] | 2016 | SMAW | Standard | Classic | PDDs, SOM | No | Yes |
Muzaka [81] | 2016 | GMAW | Standard | Classic | MDM | Yes | No |
Bai [82] | 2016 | GMAW | Standard | Classic | LMM | Yes | No |
Park [83] | 2017 | RSW | Standard | Classic | GRNN-SVM | Yes | No |
Wan [102] | 2017 | LSRSW | Standard | Classic | ANN (BP), ANN (Prob) | Yes | Yes |
Huang [103] | 2017 | P-GTAW | Visual | Classic | DM, EMD | No | Yes |
Petković [104] | 2017 | Laser welding | Multiples | Classic | SVM, ANN, GP | Yes | Yes |
Muniategui [72] | 2017 | RSW | visual | DL, classic | Fuzzy | Yes | Yes |
Wan [105] | 2017 | GTAW | visual | Classic | ANN and fuzzy | Yes | No |
Table articles with quality objective.
Author | Year | Welding process | Sensors | Data preparations | Modeling | Online | Compare |
---|---|---|---|---|---|---|---|
Bo Chen [84] | 2009 | GTAW | Multiples | Classic | ANN-DS | No | No |
Bo Chen [91] | 2010 | GTAW | Multiples | Classic | ANN-fuzzy | No | No |
Seyyedian [108] | 2012 | GTAW | Standard | Classic | ANN | Yes | No |
Li [79] | 2014 | GTAW | Visual | Classic | LR | No | No |
Bo Chen [89] | 2014 | UWW | Visual | Classic | ANN | Yes | No |
Li [96] | 2014 | RANGMW | Visual | Classic | SVM, ANN | Yes | Yes |
Escribano-García [98] | 2014 | GMAW | Standard | Classic | RSM, some DM | Yes | Yes |
Sen [74] | 2015 | DP-GMAW | Standard | Classic | Taguchi-RSM | No | No |
Keshmiri [93] | 2015 | SAW, GMAW, GTAW | Standard | Classic | DNN | Yes | No |
Wu [54] | 2016 | VPPAW | Sound | Classic | ELM, ANN, SVM | Yes | Yes |
Lv [55] | 2016 | GTAW | Sound | Classic | BP-Adaboost | Yes | Yes |
Dong [77] | 2016 | GTAW | Standard | Classic | GPR | Yes | No |
Sarkar [85] | 2016 | SAW | Standard | Classic | MRA and ANN | Yes | Yes |
Rong [87] | 2016 | GTAW | Standard | Classic | ANN | Yes | No |
Rios-Cabrera [92] | 2016 | GMAW | Visual | Classic | ANN fuzzy ARTMAP | Yes | No |
Nandhitha [106] | 2016 | GTAW | Thermography | Classic | ELM, RBN, GRNN | Yes | Yes |
Kim [107] | 2016 | RSW | Standard | Classic | kNN, GRNN | Yes | Yes |
Aviles-Viñas [109, 110] | 2016 | GMAW | Visual | Classic | ANN-fuzzy | Yes | No |
Pavan Kumar [86] | 2017 | GMAW CMT | Standard | Classic | ANN | Yes | No |
Mathew [88] | 2017 | Girth welds | Standard | Classic | ANN | Yes | No |
Di Wu [95] | 2017 | VP-PAW | Visual, sound | Classic | t-SNE and DBN | No | No |
Table articles with prediction objective.
Author | Year | Welding process | Sensors | Data preparations | Modeling | Online | Compare |
---|---|---|---|---|---|---|---|
Chen [66] | 2000 | P-GTAW | Double-visual | Classic | ANN-learning control | Yes | Yes |
Chen [111] | 2009 | GTAW | Visual | Classic | ANN-fuzzy | Yes | No |
Malviya [112] | 2011 | GMAW | Standard | Classic | ANN-PSO | Yes | No |
Hailin [105] | 2012 | GMAW | Visual | Classic | ANN and fuzzy | Yes | No |
Cruz [113] | 2015 | GMAW | Visual | Classic | ANN and fuzzy | Yes | No |
Günther [63] | 2016 | Laser welding | Visual | DL | DL-RL | Yes | No |
Santhana [75] | 2016 | GTAW | Standard | Classic | RSM | Yes | No |
Sharma [114] | 2016 | SAW | Standard | Classic | RSM and fuzzy | Yes | No |
Moghaddam [115] | 2016 | GMAW | Visual | Classic | ANN-PSO | Yes | No |
Lv [56] | 2017 | GTAW | Sound | Classic | ANN | Yes | No |
Rao [94] | 2017 | Vibratory Welding | Standard | Classic | GRNN | Yes | No |
Pengfei Hu [116] | 2017 | GMAW | Standard | Classic | Math-model—fuzzy | Yes | No |
Table articles with control objective.
Defining which of the techniques is more effective for our problem also helps in the effectiveness of a future process of intelligent control.
The intelligent control approach offers interesting perspectives since it is able to provide methodologies that allow to perform automatically some of the tasks typically performed by humans [117]. This combines with data mining models.
One intelligent control tendency utilized is a fuzzy method with ANN model. Example of this was [111] on GTAW process for predicting the dynamic of the weld pool; and in [105] for GMAW pipe-line welding, to improve the welding quality.
Another example was [113], on GMAW process, for modeling and control of weld bead width. Other example of fuzzy methods but different model techniques was [114]. It was applied for better control purpose of bead geometry parameters in submerged arc welding (SAW) process. This article proposed the response of a fuzzy logic approach with surface methodology (RSM). Demonstrating that any model obtained from a welding process can be integrated into a control system. As long as it meets time demands.
Conventional and intelligent control methods were investigated by [67] in P-GTAW process. This work made a comparison with PID control, fuzzy control, and neuron self-learning PSD control. It had better performance. This article highlights the advantage of learning-based control.
Other optimization based in learning was [115]. It proposed ANN model with a Particle Swarm Optimization (PSO) algorithm to optimize weld bead geometry characteristics on the GMAW process. The ANN-PSO model obtained an efficient optimization and multi-criteria modeling.
An emerging learning-based control system was used by Günther in [62, 63] for laser welding control. This technique is called reinforcement learning (RL). It is a machine learning branch. It is focused on decision-making by learning process [118]. Control learning can be an optimization-based method like Q-learning algorithm. It can be used to solve optimal control problems like expressed in [119].
Günther’s study [63] is one of the few RL studies for laser welding system. This makes this work an important contribution to welding process engineering. RL is a new technique open now in welding process with noble success in other areas like appearing in [120, 121, 122, 123].
These techniques of data analysis based on learning, as appearing in this article, is not yet widespread in welding process area. A bibliometric analysis among the authors studied in this research, presents a very little relationship between them. Figure 7 exposes this. The small dimensions of the authors’ clouds (articles with welding process and new data analysis techniques) and their relationships (joint publications) show little maturity in the interrelation of these areas.
Bibliometric analysis: authors’ interrelationship.
Some of the works demonstrate a small approximation between the areas, fulfilling the interdisciplinarity that Industry 4.0 advocates. Achieving this interdisciplinarity implies new study processes, defining new methodologies that unify the potential of these two areas. The needs of the modern world are going to make this happen in a short time. The new data analysis conception in welding processes area will be an acceleration in obtaining new and better models, more efficient predictions, and controls.
Several articles about the welding process were analyzed. These allowed to determine for each data mining stage how it is possible to optimize the results to obtain a good result of process analysis. Several analysis algorithms of the welding process were shown, and it was demonstrated that the comparison between them can make the process analysis more efficient and less expensive. The potential of learning-based techniques was described, because computational resources are becoming cheaper, and more quality information of welding process can be obtained. All these premises aligned with the so-called Industry 4.0, where a set of technologies that allow a fusion of physical and digital world, create a more intelligent and dynamic system.
The authors would like to acknowledge IntechOpen, Brasilia University, CNPq, CAPES, and PPMEC-UnB, and also to professors Alysson Martin Silva, and Guillermo Albarez Bestard.
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