Challenges and opportunities to advance manufacturing research for sustainable battery life cycles

Advanced manufacturing research for sustainable battery life cycles is of utmost importance to reach net zero carbon emissions (European Commission, 2023a) as well as several of the United Nations Sustainable Development Goals (UNSDGs), for example: 30% reduction of CO2 emission, 10 million job opportunities and access to electricity for 600 million people (World Economic Forum, 2019). This editorial paper highlights international motivations for pursuing more sustainable manufacturing practices and discusses key research topics in battery manufacturing. Batteries will be central to our sustainable future as generation and storage become key components to on-demand energy supply. Four underlying themes are identified to address industrial needs in this field: 1. Digitalizing and automating production capabilities: data-driven solutions for production quality, smart maintenance, automation, and human factors, 2. Human-centric production: extended reality for operator support and skills development, 3. Circular battery life cycles: circular battery systems supported by service-based and other novel business models, 4. Future topics for battery value chains: increased industrial resilience and transparency with digital product passports, and next-generation battery chemistries. Challenges and opportunities along these themes are highlighted for transforming battery value chains through circularity and more sustainable production, with a particular emphasis on lithium-ion batteries (LIB). The paper concludes with directions for further research to advance a circular and sustainable battery value chain through utilizing the full potential of digitalization realising a cleaner, more energy-efficient society

A fixture failure control chart for variation caused by assembly fixtures

In the auto body assembly process, fixtures are used to position parts during assembly and inspection. If there is variation in the positioning process, this will propagate to the final assembly. There are also other sources of variation in the final assembly, such as variation in parts due to previous manufacturing steps. To facilitate the separation of the different sources of variation, and thereby also improve fault diagnosis, a fixture failure subspace control chart is proposed. This control chart is based on a multivariate T 2-chart, but only variations in the fixture failure subspace are considered. The method is applied to two industrial case studies with satisfying results

Controlling geometrical variation caused by assembly fixtures

In the auto body assembly process, fixtures position parts during assembly and inspection. Variation in the positioning process propagates to the final assembly. To control the assembly fixtures, repeatability studies are used. Those studies are, however, usually done with long intervals and the fixtures might be afflicted with variation between studies. There are also other sources of variation in the final assembly, such as variation in parts due to previous manufacturing steps. To separate variation caused by fixtures and the variation caused by previous manufacturing processes, a multivariate fixture failure sub-space control chart is proposed

An investigation of the effect of sample size on geometrical inspection point reduction using cluster analysis

Since the model program in automotive industry gets more and more extensive, the costs related to inspection increase. Therefore, there are needs for more effective inspection preparation. In this paper a method for reducing the number of inspection points using cluster analysis is tested on production data. This leads to reductions evaluated up to 90 percent in the case studies considered. Furthermore, the relation between movements in locators and the resulting movements in inspection points is used to find inspection points particularly suited to monitor the fixtures and its locating points. Those same points are used as input to the cluster analysis and chosen as representatives for the clusters. Using cluster analysis, the sample size is an important matter. The sample size, of course, affects the statistical confidence, but it is also important to choose a sample large enough to contain as many effects as possible related to the different process phenomena that ex ist over time. Those issues are investigated and a sample size lower than 50 items cannot be recommended

Including assembly fixture repeatability in rigid and non-rigid variation simulation

The repeatability of the assembly fixtures influences the geometrical outcome of an assembly. To control the fixtures, capability studies are conducted. Those studies give however just information about the variability in a number of inspection points. In this paper, a method for transforming the variation in inspection data to variation in the contacts between workpiece and locators is described. By doing this, the fault localizing of the fixture is facilitated. Further, the accuracy of the variation simulations used to evaluate different concepts and designs can be improved. Usually, when data from a repeatability study are used as input to a variation simulation, the tolerances are only applied in the points that actually were inspected. The suggested methodology makes it possible to transform the tolerances containing the repeatability of the fixture to tolerances on the locating scheme, and they are thereby affecting every point in the simulation model, not only th e inspected ones. The method is tested on a case study and the effect of including fixture repeatability in a variation simulation is investigated

A measure of the information loss for inspection point reduction

Since the vehicle program in the automotive industry gets more and more extensive, the costs related to inspection increase. Therefore, there are needs for more effective inspection preparation. In many situations, a large number of inspection points are measured, despite the fact that only a small subset of points is needed. A method, based on cluster analysis, for identifying redundant inspection points has earlier been successfully tested on industrial cases. Cluster analysis is used for grouping the variables into clusters, where the points in each cluster are highly correlated. From every cluster only one representing point is selected for inspection. In this paper the method is further developed, and multiple linear regression is used for evaluating how much of the information is lost when discarding an inspection point. The information loss can be quantified using an efficiency measure based on linear multiple regression, where the part of the variation in the discarded variables that can be explained by the remaining variables is calculated. This measure can be illustrated graphically and that helps to decide how many clusters that should be formed, i.e., how many inspection points that can be discarded

Minimizing Weld Variation Effects Using Permutation Genetic Algorithms and Virtual Locator Trimming

The mass production paradigm strives for uniformity, and for assembly operations to be identical for each individual product. To accommodate geometric variation between individual parts in such a process, tolerances are introduced into the design. However, for certain assembly operations this method can yield suboptimal quality. For instance, in welded assemblies, geometric variation in ingoing parts can significantly impair quality. When parts misalign in interfaces, excessive clamping force must be applied, resulting in additional residual stresses in the welded assemblies. This problem may not always be cost-effective to address simply by tightening tolerances. Therefore, under new paradigm of mass customization, the manufacturing approach can be adapted on an individual level. Since parts in welded assemblies are not easily disassembled and reused, interchangeability is not a relevant concern. This recognition means that each welded assembly can be adapted individually for the specific idiosyncrasies of ingoing parts. This paper focuses on two specific mass customization techniques; permutation genetic algorithms to assemble nominally identical parts, and virtual locator trimming. Based on these techniques, a six-step method is proposed, aimed at minimizing thing effects of geometric variation. The six steps are nominal reference point optimization, permutation GA configuration optimization, virtual locator trimming, clamping, welding simulation, and fatigue life evaluation. A case study is presented which focuses on one specific product; the turbine rear structure of a commercial turbofan engine. Using this simulation approach, the effects of using permutation genetic algorithms and virtual locator trimming to reduce variation are evaluated. The results show that both methods significantly reduce seam variation. However, virtual locator trimming is far more effective in the test case presented, since it virtually eliminates seam variation. This can be attributed to the orthogonality in fixturing. Seam variation is linked to weldability, which in turn has significant impact on estimated fatigue life. These results underscore the potential of virtual trimming and genetic algorithms in manufacturing, as a means both to reduce cost and increase functional quality

Minimizing dimensional variation and robot traveling time in welding stations

Complex assembled products as an automotive car body consist of about 300 sheet metal parts joined by up to 4000 spot welds. In the body factory, there are several hundred robots organized into lines of welding stations. The distribution of welds between robots and the welding sequences have a significant influence on both dimensional quality and throughput. Therefore, this paper proposes a novel method for quality and throughput optimization based on a systematic search algorithm which exploits properties of the welding process. It uses approximated lower bounds to speed up the search and to estimate the quality of the solution. The method is successfully tested on reference assemblies, including detailed fixtures, welding robots and guns